Information processing program, information processing method and information processing device

Abstract

This information processing device has an expanded knowledge database (800). The information processing device reads out a plurality of hypotheses which include the hypothesis "X:A→B," the hypothesis "Y:A→B," and the hypothesis "Z:A→B." The information processing device calculates, as a ratio of conditions, the ratio of the union obtained by joining conditions X, conditions Y and conditions Z to a universal set Ω which encompasses the conditions X, the conditions Y and the conditions Z. When the calculated ratio of conditions is equal to or greater than a threshold, the information processing device adds the hypothesis "Ω:A→B," which unconditionally expresses the cause-and-effect relationship "A→B," to the expanded knowledge database (800).

Description

[Technical field]

[0001] The present invention relates to an information processing program, an information processing method, and an information processing device. [Background technology]

[0002] In the past, it was desirable to add new hypotheses indicating causal relationships between elements under specific conditions to an existing knowledge DB (DataBase) that stores hypotheses indicating causal relationships between elements under unspecified conditions. For example, it is conceivable to add new hypotheses indicating causal relationships between elements under specific conditions estimated by AI (Artificial Intelligence) to an existing knowledge DB.

[0003] In a prior art technique, for example, a template of knowledge stored in a knowledge base is modified so as to meet certain conditions, and then added to the knowledge base as new knowledge. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 04-160536 Summary of the Invention [Problem to be solved by the invention]

[0005] However, in the conventional technology, it may be difficult to appropriately update an existing knowledge DB. For example, if the formats of hypotheses that can be stored in the existing knowledge DB and new hypotheses estimated by AI are different, it is difficult to add new hypotheses estimated by AI to the existing knowledge DB. In addition, for example, it is unclear how to integrate and store a hypothesis indicating a causal relationship between elements under undetermined conditions stored in the existing knowledge DB and a new hypothesis indicating the same causal relationship between the same elements under specific conditions estimated by AI.

[0006] In one aspect, the present invention has an object to make it possible to appropriately update a storage unit. [Means for solving the problem]

[0007] According to one embodiment, an information processing program, an information processing method, and an information processing device are proposed which determine whether a ratio of a union of multiple hypotheses indicating the same first causal relationship under different conditions, which are stored in a memory unit that stores hypotheses indicating a causal relationship between elements, to a universal set that includes the different conditions is greater than or equal to a threshold value, and if the ratio is greater than or equal to the threshold value, add to the memory unit a first hypothesis that is different from the multiple hypotheses and that unconditionally indicates the first causal relationship within the universal set.

[0008] According to one embodiment, an information processing program, an information processing method, and an information processing device are proposed that determine whether a ratio of a union of multiple hypotheses indicating the same first causal relationship under different conditions, which are stored in a memory unit that stores hypotheses indicating a causal relationship between elements, to a universal set including each of the different conditions is greater than or equal to a threshold value, and if the ratio is greater than or equal to the threshold value, and if the memory unit stores a hypothesis that indicates the first causal relationship under undetermined conditions and is different from the multiple hypotheses, deletes the hypothesis from the memory unit. Effect of the Invention

[0009] According to one aspect, the storage unit can be appropriately updated. [Brief description of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram illustrating an example of an information processing method according to an embodiment. [Diagram 2] FIG. 2 is a diagram illustrating another example of the information processing method according to the embodiment. [Diagram 3] FIG. 3 is an explanatory diagram illustrating an example of an information processing system 300. As shown in FIG. [Figure 4] FIG. 4 is a block diagram showing an example of a hardware configuration of the information processing device 100. As shown in FIG. [Diagram 5] FIG. 5 is an explanatory diagram of an example of the contents stored in the extended knowledge DB. [Figure 6] FIG. 6 is a block diagram showing an example of a functional configuration of the information processing device 100. As shown in FIG. [Figure 7] FIG. 7 is a block diagram showing an example of a functional configuration of the information processing system 300. As shown in FIG. [Figure 8] FIG. 8 is an explanatory diagram showing a first operation example of the information processing device 100. As shown in FIG. [Figure 9] FIG. 9 is an explanatory diagram showing a second operation example of the information processing device 100. In FIG. [Figure 10] FIG. 10 is an explanatory diagram showing a third operation example of the information processing device 100. In FIG. [Figure 11] FIG. 11 is an explanatory diagram showing a fourth operation example of the information processing device 100. In FIG. [Figure 12] FIG. 12 is an explanatory diagram showing a fifth operation example of the information processing device 100. In FIG. [Figure 13] FIG. 13 is an explanatory diagram showing a sixth operation example of the information processing device 100. In FIG. [Figure 14] FIG. 14 is a flowchart illustrating an example of a procedure of the first addition process. [Figure 15] FIG. 15 is a flowchart illustrating an example of a procedure of the first contraction process. [Figure 16] FIG. 16 is a flowchart illustrating an example of a procedure of the second addition process. [Figure 17] FIG. 17 is a flowchart illustrating an example of a procedure of the second contraction process. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, an embodiment of an information processing program, an information processing method, and an information processing device according to the present invention will be described in detail with reference to the drawings.

[0012] (An example of an information processing method according to an embodiment) 1 is an explanatory diagram showing an example of an information processing method according to an embodiment. The information processing device 100 is a computer for enabling a storage unit to be appropriately updated. The information processing device 100 is, for example, a server or a PC (Personal Computer).

[0013] The memory unit stores, for example, hypotheses indicating causal relationships between elements. The memory unit is, for example, an existing knowledge DB that stores hypotheses indicating causal relationships with undetermined conditions that are estimated without considering conditions. A condition is expressed by a combination of one or more elements. For example, a hypothesis indicates a causal relationship between an element indicating a gene and an element indicating a disease. For example, a hypothesis indicates a causal relationship between an element indicating a material and an element indicating a physical property.

[0014] A hypothesis indicates, for example, a causal relationship in which if element A is established, then element B is established. In the following explanation, the causal relationship in which if element A is established, then element B is established may be expressed as "A → B." A hypothesis may indicate, for example, a causal relationship in which if element A is not established, then element B is established, or a causal relationship in which if element A is established, then element B is not established.

[0015] Here, it may be desirable to add new hypotheses to the existing knowledge DB. For example, there may be cases where AI is used to successively estimate new hypotheses that indicate causal relationships between elements under certain conditions. In this case, each time a new hypothesis is estimated by AI, the new hypothesis may be added to the existing knowledge DB.

[0016] Conventionally, it may be difficult to appropriately update an existing knowledge DB. For example, if the formats of hypotheses that can be stored in the existing knowledge DB and new hypotheses estimated by AI are different, it is difficult to add new hypotheses estimated by AI to the existing knowledge DB. Specifically, it is possible that the existing knowledge DB includes a field for setting causal relationships but does not include a field for setting conditions, and stores only hypotheses indicating causal relationships between elements whose conditions are undetermined. In this case, it is not possible to directly store new hypotheses indicating causal relationships between elements under specific conditions estimated by AI in the existing knowledge DB.

[0017] In response to this, it is conceivable to expand the existing knowledge DB so that new hypotheses estimated by AI can be stored. For example, a field for setting conditions can be added to the existing knowledge DB. In this case, for hypotheses already stored in the existing knowledge DB, the character Φ, indicating that the condition is not yet determined, is set in the field for setting conditions. Then, new hypotheses that indicate causal relationships between elements under specific conditions estimated by AI are added to the expanded knowledge DB.

[0018] However, it is unclear how to integrate and store a hypothesis indicating a causal relationship under undetermined conditions stored in an existing knowledge DB and a new hypothesis indicating the same causal relationship under specific conditions estimated by AI. Therefore, it may be difficult to appropriately update the knowledge DB. For example, it may be difficult to add a new hypothesis estimated by AI to the existing knowledge DB while taking into account the relationship between the hypotheses already stored in the existing knowledge DB and the new hypothesis estimated by AI. In addition, there is a problem in that it is difficult to suppress the increase in the amount of data in the existing knowledge DB.

[0019] In this embodiment, an information processing method that allows the storage unit to be appropriately updated will be described.

[0020] 1, the information processing device 100 includes a storage unit 101. The storage unit 101 stores a hypothesis indicating a causal relationship between elements. The storage unit 101 may store, for example, a plurality of hypotheses indicating the same first causal relationship under different conditions. A condition is expressed, for example, by a combination of one or more elements. A plurality of elements that can be used to express a causal relationship and a plurality of elements that can be used to express a condition may include the same element.

[0021] (1-1) The information processing device 100 calculates the ratio of a union of different conditions, which is a combination of a plurality of hypotheses indicating the same first causal relationship under different conditions stored in the storage unit 101, to a universal set including each of the different conditions. The information processing device 100 calculates, for example, the ratio of the number of elements used in the union including each of the different conditions to the number of elements that can be used to express a condition. The information processing device 100 judges whether the calculated ratio is equal to or greater than a threshold value. The threshold value is, for example, set in advance by a user. This makes it possible to obtain a guideline for judging how it is preferable to update the contents stored in the storage unit 101.

[0022] (1-2) When the calculated ratio is equal to or greater than a threshold, the information processing device 100 adds a first hypothesis, which is different from the multiple hypotheses and unconditionally indicates the first causal relationship, to the storage unit 101. The unconditional is expressed by Ω, for example. This allows the information processing device 100 to appropriately update the storage unit 101. For example, after adding the first hypothesis, the information processing device 100 can delete the multiple hypotheses from the storage unit 101, thereby suppressing an increase in the amount of data in the storage unit 101.

[0023] The information processing device 100 determines that the likelihood that the first causal relationship unconditionally holds is relatively low until the union of the different conditions becomes larger than a certain level, and can store and appropriately manage a hypothesis indicating the first causal relationship for each individual condition in the storage unit 101. On the other hand, when the union of the different conditions becomes larger than a certain level, the information processing device 100 determines that the likelihood that the first causal relationship unconditionally holds is relatively high, and can add the first hypothesis indicating the first causal relationship unconditionally to the storage unit 101 and appropriately manage it.

[0024] In this way, the information processing device 100 can appropriately manage hypotheses indicating the first causal relationship in the memory unit 101 depending on the degree of certainty that the first causal relationship holds unconditionally, and can make the memory unit 101 appropriately updateable.

[0025] Here, the case where the information processing device 100 has the storage unit 101 has been described, but this is not limiting. For example, a computer other than the information processing device 100 may have the storage unit 101. For example, the information processing device 100 may be able to access the storage unit 101 of the other computer.

[0026] Here, the case where the information processing device 100 operates independently has been described, but the present invention is not limited to this. For example, the information processing device 100 may cooperate with another computer. For example, a plurality of computers may realize the functions of the information processing device 100. Specifically, the functions of the information processing device 100 may be realized on a cloud.

[0027] (Another Example of the Information Processing Method According to the Embodiment) Next, another example of the information processing method according to the embodiment will be described with reference to Fig. 2. Fig. 2 is an explanatory diagram showing another example of the information processing method according to the embodiment.

[0028] In FIG. 2, the information processing device 100 has a storage unit 201. The storage unit 201 stores a hypothesis indicating a causal relationship between elements. The storage unit 201 may store a second hypothesis indicating a first causal relationship under undetermined conditions. The undetermined conditions are expressed by, for example, Φ. The storage unit 201 may store, for example, a plurality of hypotheses indicating the same first causal relationship under different conditions. The conditions are expressed by, for example, a combination of one or more elements. The plurality of elements that can be used to express the causal relationship and the plurality of elements that can be used to express the condition may include the same element.

[0029] (2-1) The information processing device 100 calculates the ratio of a union of different conditions, which is a combination of a plurality of hypotheses indicating the same first causal relationship under different conditions stored in the storage unit 201, to a universal set including the different conditions. The information processing device 100 calculates, for example, the ratio of the number of elements used in the union including the different conditions to the number of elements that can be used to express a condition. The information processing device 100 judges whether the calculated ratio is equal to or greater than a threshold value. The threshold value is, for example, set in advance by a user. This makes it possible to obtain a guideline for judging how it is preferable to update the contents stored in the storage unit 201.

[0030] (2-2) When the calculated ratio is equal to or greater than a threshold, if a second hypothesis that is different from the multiple hypotheses and indicates a first causal relationship under undetermined conditions is stored in the storage unit 201, the information processing device 100 deletes the second hypothesis from the storage unit 201. This enables the information processing device 100 to appropriately update the storage unit 201. The information processing device 100 can, for example, delete the second hypothesis to suppress an increase in the amount of data in the storage unit 201.

[0031] Until the union of the different conditions becomes larger than a certain size, the information processing device 100 can provisionally determine that the first causal relationship holds unconditionally, although the conditions are not yet determined, and can keep the second hypothesis stored in the storage unit 201. Therefore, the information processing device 100 can appropriately manage the storage unit 201 so as to respect the second hypothesis obtained by existing research or the like.

[0032] On the other hand, if the union of the different conditions becomes larger than a certain level, the information processing device 100 can determine that the first causal relationship is established under conditions wider than a certain level. The second hypothesis may not take into consideration whether the first causal relationship is established under other conditions that are not included in the union of the different conditions. The information processing device 100 can delete the second hypothesis from the storage unit 201, taking into consideration the possibility that the second hypothesis was generated in an existing study without determining whether the first causal relationship is established under other conditions.

[0033] Therefore, the information processing device 100 can appropriately update the storage unit 201 so as to be able to distinguish between a condition in which it is determined that the first causal relationship holds and a condition in which it is not determined that the first causal relationship holds. The information processing device 100 can make it possible to determine under which condition it is preferable to research whether the first causal relationship holds, which can contribute to future research.

[0034] In this way, the information processing device 100 can appropriately manage the second hypothesis indicating the first causal relationship under undetermined conditions and a plurality of hypotheses indicating the same first causal relationship under different conditions by estimating and considering the range of conditions verified in existing research. The information processing device 100 can appropriately update the storage unit 201.

[0035] Here, the case where the information processing device 100 has the storage unit 201 has been described, but this is not limiting. For example, a computer other than the information processing device 100 may have the storage unit 201. For example, the information processing device 100 may be able to access the storage unit 201 of the other computer.

[0036] Here, the case where the information processing device 100 operates independently has been described, but the present invention is not limited to this. For example, the information processing device 100 may cooperate with another computer. For example, a plurality of computers may realize the functions of the information processing device 100. Specifically, the functions of the information processing device 100 may be realized on a cloud.

[0037] Here, the information processing device 100 may have both a function for realizing the one embodiment described above and a function for realizing the other embodiment described above. The information processing device 100 may have only one of the functions for realizing the one embodiment described above and the function for realizing the other embodiment described above. In the following description, the case where the information processing device 100 has both the function for realizing the one embodiment described above and the function for realizing the other embodiment described above will be mainly described.

[0038] (An example of the information processing system 300) Next, an example of an information processing system 300 to which the information processing device 100 shown in FIG. 1 or 2 is applied will be described with reference to FIG.

[0039] 3 is an explanatory diagram showing an example of an information processing system 300. In FIG. 3, the information processing system 300 includes the information processing device 100, a knowledge learning device 301, and a client device 302.

[0040] In the information processing system 300, the information processing device 100 and the knowledge learning device 301 are connected via a wired or wireless network 310. The network 310 is, for example, a local area network (LAN), a wide area network (WAN), the Internet, etc. In the information processing system 300, the information processing device 100 and the client device 302 are connected via the wired or wireless network 310.

[0041] The information processing device 100 is a computer that manages a storage unit 311. The storage unit 311 stores a hypothesis indicating a causal relationship between elements. The storage unit 311 may store a hypothesis indicating a first causal relationship under undetermined conditions. The storage unit 311 may store, for example, a plurality of hypotheses indicating the same first causal relationship under different conditions. The condition is expressed, for example, by a combination of one or more elements. A plurality of elements that can be used to express a causal relationship and a plurality of elements that can be used to express a condition may include the same element.

[0042] The storage unit 311 may be included in the information processing device 100. The storage unit 311 may be included in a computer other than the information processing device 100. In this case, the information processing device 100 can access the storage unit 311 included in the other computer. The storage unit 311 corresponds to the storage unit 101 shown in Fig. 1 or the storage unit 201 shown in Fig. 2. Specifically, the storage unit 311 is realized by an extended knowledge DB 500 described later in Fig. 5.

[0043] The information processing device 100 receives a new hypothesis indicating a causal relationship between elements from the knowledge learning device 301. The information processing device 100 adds the received new hypothesis to the storage unit 311.

[0044] The information processing device 100 calculates the ratio of a union of a plurality of hypotheses indicating the same first causal relationship under different conditions stored in the storage unit 311 to a universal set including the different conditions. The information processing device 100 determines whether the calculated ratio is equal to or greater than a threshold. The threshold is set in advance by a user, for example. The information processing device 100 updates the storage unit 311 based on the result of the determination.

[0045] The information processing device 100 receives an inquiry about the stored contents of the storage unit 311. The information processing device 100 receives an inquiry about the stored contents of the storage unit 311 from, for example, the knowledge learning device 301 or the client device 302. In response to the inquiry, the information processing device 100 transmits the stored contents of the storage unit 311 to the knowledge learning device 301 or the client device 302. The information processing device 100 is used by, for example, a system administrator. The information processing device 100 is, for example, a server or a PC.

[0046] The knowledge learning device 301 is a computer that learns a new hypothesis indicating a causal relationship between elements. The knowledge learning device 301 learns a new hypothesis indicating a causal relationship between elements, for example, by AI. Specifically, the knowledge learning device 301 may transmit an inquiry about the memory contents of the memory unit 311 to the information processing device 100 and receive the memory contents of the memory unit 311 from the information processing device 100. Specifically, the knowledge learning device 301 may learn a new hypothesis indicating a causal relationship between elements, by AI, by referring to the memory contents of the memory unit 311. The knowledge learning device 301 transmits the learned new hypothesis to the information processing device 100. The knowledge learning device 301 is used, for example, by a system administrator. The knowledge learning device 301 is, for example, a server or a PC.

[0047] The client device 302 is a computer used by a system user. Based on an operational input by the system user, the client device 302 transmits an inquiry about the stored contents of the memory unit 311 to the information processing device 100. The client device 302 receives the stored contents of the memory unit 311 from the information processing device 100. The client device 302 outputs the stored contents of the memory unit 311 so that the system user can refer to them. The client device 302 is, for example, a PC, a tablet terminal, or a smartphone.

[0048] Here, the case where the information processing device 100 is a device different from the knowledge learning device 301 has been described, but the present invention is not limited to this. For example, the information processing device 100 may have a function as the knowledge learning device 301 and may also operate as the knowledge learning device 301.

[0049] Here, the case where the information processing device 100 is a device different from the client device 302 has been described, but this is not limited thereto. For example, the information processing device 100 may have a function as the client device 302 and may also operate as the client device 302.

[0050] (Example of use of information processing system 300) Next, an example of use of the information processing system 300 shown in FIG. 3 will be described. For example, the information processing system 300 can be applied to the field of material research. Specifically, the information processing system 300 can be applied to the field of searching for catalysts for producing materials with preferable physical properties. The storage unit 311 stores, for example, a hypothesis showing a causal relationship between the catalyst and the physical properties. This allows the information processing system 300 to easily search for catalysts for producing materials with preferable physical properties.

[0051] For example, the information processing system 300 can be applied to the field of genetic research. Specifically, the information processing system 300 can be applied to the field of verifying the relationship between a specific gene and a disease. The storage unit 311 stores, for example, a hypothesis indicating a causal relationship between a gene and a disease. This allows the information processing system 300 to easily verify the relationship between a specific gene and a disease.

[0052] (Example of hardware configuration of information processing device 100) Next, an example of the hardware configuration of the information processing device 100 will be described with reference to FIG.

[0053] Fig. 4 is a block diagram showing an example of a hardware configuration of the information processing device 100. In Fig. 4, the information processing device 100 has a CPU (Central Processing Unit) 401, a memory 402, a network I / F (Interface) 403, a recording medium I / F 404, and a recording medium 405. In addition, each component is connected to each other via a bus 400.

[0054] Here, the CPU 401 is responsible for the overall control of the information processing device 100. The memory 402 has, for example, a read only memory (ROM), a random access memory (RAM), and a flash ROM. Specifically, for example, the flash ROM and the ROM store various programs, and the RAM is used as a work area for the CPU 401. The programs stored in the memory 402 are loaded into the CPU 401, causing the CPU 401 to execute the coded processes.

[0055] The network I / F 403 is connected to the network 310 through a communication line, and is connected to other computers via the network 310. The network I / F 403 manages an internal interface with the network 310, and controls input and output of data from other computers. The network I / F 403 is, for example, a modem or a LAN adapter.

[0056] The recording medium I / F 404 controls reading / writing data from / to the recording medium 405 under the control of the CPU 401. The recording medium I / F 404 is, for example, a disk drive, a solid state drive (SSD), a universal serial bus (USB) port, etc. The recording medium 405 is a non-volatile memory that stores data written under the control of the recording medium I / F 404. The recording medium 405 is, for example, a disk, a semiconductor memory, a USB memory, etc. The recording medium 405 may be detachable from the information processing device 100.

[0057] In addition to the above-mentioned components, the information processing device 100 may have, for example, a keyboard, a mouse, a display, a printer, a scanner, a microphone, a speaker, etc. Furthermore, the information processing device 100 may have a plurality of recording medium I / Fs 404 and recording media 405. Furthermore, the information processing device 100 may not have the recording medium I / Fs 404 and the recording media 405.

[0058] (Expanded knowledge DB500 memory contents) Next, an example of the contents stored in the extended knowledge DB 500 will be described with reference to Fig. 5. The extended knowledge DB 500 is realized by, for example, a storage area such as the memory 402 or the recording medium 405 of the information processing device 100 shown in Fig. 4.

[0059] Fig. 5 is an explanatory diagram showing an example of the contents stored in the extended knowledge DB 500. As shown in Fig. 5, the extended knowledge DB 500 has fields for conditions and causal relationships. The extended knowledge DB 500 stores hypothesis information as record 500-a by setting information in each field for each hypothesis. a is an arbitrary integer.

[0060] In the condition field, a condition for forming a hypothesis is set. In the condition field, for example, a condition expressed by a combination of one or more elements is set. In the condition field, for example, Φ indicating that the condition is not determined may be set. In the condition field, for example, Ω indicating that there is no condition may be set. In the causal relationship field, a causal relationship for forming a hypothesis in the above condition is set.

[0061] (Example of hardware configuration of knowledge learning device 301) A specific example of the hardware configuration of the knowledge learning device 301 is similar to the example of the hardware configuration of the information processing device 100 shown in FIG. 4, and therefore a description thereof will be omitted.

[0062] (Example of Hardware Configuration of Client Device 302) Specifically, an example of the hardware configuration of the client device 302 is similar to the example of the hardware configuration of the information processing device 100 shown in FIG. 4, and therefore a description thereof will be omitted.

[0063] (Example of functional configuration of information processing device 100) Next, an example of a functional configuration of the information processing device 100 will be described with reference to FIG.

[0064] 6 is a block diagram showing an example of a functional configuration of the information processing device 100. The information processing device 100 includes a storage unit 600, an acquisition unit 601, a determination unit 602, an update unit 603, and an output unit 604.

[0065] The storage unit 600 is realized by, for example, a storage area such as the memory 402 or the recording medium 405 shown in Fig. 4. In the following, a case where the storage unit 600 is included in the information processing device 100 will be described, but this is not limiting. For example, the storage unit 600 may be included in a device different from the information processing device 100, and the stored contents of the storage unit 600 may be accessible from the information processing device 100.

[0066] The acquiring unit 601 to the output unit 604 function as an example of a control unit. Specifically, the acquiring unit 601 to the output unit 604 realize their functions by, for example, causing the CPU 401 to execute a program stored in a storage area such as the memory 402 or the recording medium 405 shown in Fig. 4, or by the network I / F 403. The processing results of each functional unit are stored in, for example, a storage area such as the memory 402 or the recording medium 405 shown in Fig. 4.

[0067] The storage unit 600 stores various information that is referred to or updated in the processing of each functional unit. The storage unit 600 stores hypotheses that indicate causal relationships between elements. The storage unit 600 stores, for example, a plurality of hypotheses that indicate the same causal relationship under different conditions. A condition is expressed, for example, by a combination of one or more elements. An element may be, for example, a value.

[0068] The storage unit 600 may store, for example, a hypothesis indicating a causal relationship under undetermined conditions. Undetermined conditions may be provisionally treated as being unconditional. Unconditional includes, for example, all conditions. An unconditional causal relationship is, for example, a causal relationship that holds under all conditions. The storage unit 600 may store, for example, a hypothesis indicating a causal relationship unconditionally. Specifically, the storage unit 600 stores the extended knowledge DB 500.

[0069] The acquisition unit 601 acquires various information used for processing by each functional unit. The acquisition unit 601 stores the acquired various information in the storage unit 600 or outputs it to each functional unit. The acquisition unit 601 may also output the various information stored in the storage unit 600 to each functional unit. The acquisition unit 601 acquires various information based on, for example, an operation input by a user of the device itself. The acquisition unit 601 may receive various information from, for example, a device different from the information processing device 100.

[0070] The acquiring unit 601 acquires, for example, a new hypothesis different from the multiple hypotheses already stored in the storage unit 600. Specifically, the acquiring unit 601 acquires the new hypothesis by accepting an input of the new hypothesis based on an operation input by a user of the own device. Specifically, the acquiring unit 601 may acquire the new hypothesis by receiving it from another computer. The other computer is, for example, the knowledge learning device 301.

[0071] The acquiring unit 601 acquires, for example, an output request for the contents stored in the storage unit 600. Specifically, the acquiring unit 601 acquires the output request by accepting an input of the output request based on an operation input by a user of the own device. Specifically, the acquiring unit 601 may acquire the output request by receiving it from another computer. The other computer is, for example, the client device 302.

[0072] The acquisition unit 601 may receive a start trigger for starting processing of any of the functional units. The start trigger may be, for example, a predetermined operational input by a user of the device. The start trigger may be, for example, reception of predetermined information from another computer. The start trigger may be, for example, output of predetermined information by any of the functional units. The start trigger may be, for example, detection of a signal output by a timer at regular intervals.

[0073] The acquiring unit 601 may, for example, receive the acquisition of a new hypothesis as a start trigger for starting the processes of the determining unit 602 and the updating unit 603. The acquiring unit 601 may, for example, receive the acquisition of an output request as a start trigger for starting the process of the output unit 604.

[0074] The determination unit 602 calculates the ratio of a union of different conditions, which is a combination of a plurality of hypotheses indicating the same first causal relationship under different conditions stored in the storage unit 600, to a universal set including the different conditions. The universal set includes, for example, all conditions. The ratio can be, for example, a guideline for updating the storage unit 600. The ratio can be, for example, a guideline for evaluating whether the first causal relationship holds unconditionally. The ratio can be, for example, a guideline for evaluating whether a certain number of conditions for the first causal relationship to hold have been identified.

[0075] The determination unit 602 calculates, for example, a ratio of the number of elements used in the union to the number of elements that can be used to express a condition for a plurality of hypotheses that indicate the same first causal relationship under different conditions stored in the storage unit 600. For example, the number of elements that can be used to express a condition is set in advance by the user of the device. For example, the number of elements that can be used to express a condition may be set to the total number of elements included in the storage unit 600, which is obtained by counting up all elements used in all hypotheses stored in the storage unit 600 without duplication.

[0076] Here, the condition may indicate a range of values. In this case, the determination unit 602 may calculate, for example, a ratio of the range of the value of the union to the entire range of values ​​that can be taken for a plurality of hypotheses that indicate the same first causal relationship under different conditions stored in the storage unit 600. This allows the determination unit 602 to obtain a guideline for updating the storage unit 600. For example, the determination unit 602 can obtain a guideline for evaluating whether or not the first causal relationship is unconditionally established. For example, the determination unit 602 can obtain a guideline for evaluating whether or not a certain number of conditions for the first causal relationship to be established have been identified.

[0077] The determination unit 602 determines whether the calculated ratio is equal to or greater than a threshold. The threshold is, for example, set in advance by the user of the device. This allows the determination unit 602 to determine how to update the storage unit 600 in the update unit 603. For example, if the ratio is equal to or greater than the threshold, the determination unit 602 can determine that there is a relatively high probability that the first causal relationship is established unconditionally. For example, if the ratio is equal to or greater than the threshold, the determination unit 602 can determine that a certain level of conditions for the first causal relationship to be established have been identified.

[0078] The update unit 603 updates the storage contents of the storage unit 600. For example, when the determination unit 602 determines that the ratio is equal to or greater than a threshold, the update unit 603 adds a first hypothesis unconditionally indicating the first causal relationship to the storage unit 600. The first hypothesis is different from a plurality of hypotheses each indicating the same first causal relationship under different conditions. In this way, when the probability that the first causal relationship is unconditionally established is relatively high, the update unit 603 can unconditionally add the first hypothesis indicating the first causal relationship to the storage unit 600. Then, the update unit 603 can appropriately update the storage unit 600 so that the storage contents of the storage unit 600 represent the already determined conditions under which the first causal relationship is established.

[0079] For example, when the determination unit 602 determines that the ratio is equal to or greater than the threshold, the update unit 603 deletes the multiple hypotheses indicating the same first causal relationship under different conditions from the storage unit 600. Specifically, instead of adding the first hypothesis to the storage unit 600, the update unit 603 deletes the multiple hypotheses indicating the same first causal relationship under different conditions from the storage unit 600. For example, when the determination unit 602 determines that the ratio is less than the threshold, the update unit 603 leaves the multiple hypotheses indicating the same first causal relationship under different conditions in the storage unit 600.

[0080] As a result, the updating unit 603 can determine that since a plurality of hypotheses indicating the same first causal relationship under different conditions are included in the first hypothesis, it is not necessary to store the plurality of hypotheses in the storage unit 600. Therefore, the updating unit 603 can delete a plurality of hypotheses indicating the same first causal relationship under different conditions from the storage unit 600, and can suppress an increase in the amount of data in the storage unit 600. The updating unit 603 can leave a plurality of hypotheses indicating the same first causal relationship under different conditions in the storage unit 600 until the first hypothesis is added to the storage unit 600, and can control the storage unit 600 to respect existing research.

[0081] For example, when the determination unit 602 determines that the ratio is equal to or greater than the threshold, if the second hypothesis indicating the first causal relationship under undetermined conditions is stored in the storage unit 600, the update unit 603 deletes the second hypothesis indicating the first causal relationship under undetermined conditions from the storage unit 600. The second hypothesis is a different hypothesis from a plurality of hypotheses indicating the same first causal relationship under different conditions. Specifically, instead of adding the first hypothesis to the storage unit 600, the update unit 603 deletes the second hypothesis indicating the first causal relationship under undetermined conditions from the storage unit 600. For example, when the determination unit 602 determines that the ratio is less than the threshold, the update unit 603 leaves the second hypothesis indicating the first causal relationship under undetermined conditions in the storage unit 600.

[0082] As a result, the update unit 603 can determine that the second hypothesis indicating the first causal relationship under undetermined conditions is included in the first hypothesis, and therefore the second hypothesis does not need to be stored in the storage unit 600. Therefore, the update unit 603 can delete the second hypothesis indicating the first causal relationship under undetermined conditions from the storage unit 600, and can suppress an increase in the amount of data in the storage unit 600. The update unit 603 can leave the second hypothesis indicating the first causal relationship under undetermined conditions in the storage unit 600 until the first hypothesis is added to the storage unit 600, and can control the storage unit 600 to respect existing research.

[0083] When the acquisition unit 601 acquires a third hypothesis indicating the first causal relationship under new conditions, if the first hypothesis is not stored in the storage unit 600, the update unit 603 adds the input third hypothesis to the storage unit 600. The third hypothesis is a hypothesis that has not yet been stored in the storage unit 600, and is a different hypothesis from the multiple hypotheses that indicate the same first causal relationship under different conditions. This enables the update unit 603 to add a new hypothesis to the storage unit 600, and enables the storage unit 600 to be appropriately updated.

[0084] When the acquisition unit 601 acquires a third hypothesis indicating a first causal relationship under new conditions, if the first hypothesis is stored in the storage unit 600, the update unit 603 discards the input third hypothesis. This allows the update unit 603 to avoid adding the third hypothesis to the storage unit 600 if the first hypothesis including the third hypothesis is already stored in the storage unit 600, and can suppress an increase in the amount of data in the storage unit 600. In addition, the update unit 603 can prevent the storage unit 600 from simultaneously storing the first hypothesis and the third hypothesis included in the first hypothesis. Therefore, the update unit 603 can put the stored contents of the storage unit 600 into a format that is easy for the system user to intuitively understand.

[0085] The output unit 604 outputs the processing result of at least one of the functional units. The output format is, for example, display on a display, printout on a printer, transmission to an external device via the network I / F 403, or storage in a storage area such as the memory 402 or the recording medium 405. In this way, the output unit 604 can notify the user of the device of the processing result of at least one of the functional units, thereby improving the convenience of the information processing device 100.

[0086] The output unit 604 outputs, for example, the contents stored in the storage unit 600. Specifically, in response to an output request acquired by the acquisition unit 601, the output unit 604 outputs the contents stored in the storage unit 600 so that the system user can refer to them. More specifically, the output unit 604 transmits the contents stored in the storage unit 600 to the client device 302 that is the sender of the output request. In this way, the output unit 604 can make the contents stored in the storage unit 600 so that the system user can refer to them.

[0087] Here, the case has been described in which the determination unit 602 calculates the ratio of a union of a plurality of hypotheses showing the same first causal relationship in different conditions included in a universal set to the universal set, but this is not limiting. For example, the information processing device 100 may adopt the first condition instead of the universal set.

[0088] Specifically, the determination unit 602 calculates the proportion of a union of different conditions for a first condition for a plurality of hypotheses indicating the same first causal relationship in different conditions included in the first condition stored in the storage unit 600. The determination unit 602 determines whether the calculated proportion is equal to or greater than a threshold value.

[0089] This allows the determination unit 602 to determine how to update the storage unit 600 with the update unit 603. For example, if the ratio is equal to or greater than a threshold, the determination unit 602 can determine that there is a relatively high probability that the first causal relationship holds unconditionally within the first condition. For example, if the ratio is equal to or greater than a threshold, the determination unit 602 can determine that a certain number or more of individual conditions among the first conditions under which the first causal relationship holds have been identified.

[0090] Specifically, when the calculated ratio is equal to or greater than a threshold, the updating unit 603 adds a first hypothesis, which is different from the multiple hypotheses and which unconditionally indicates the first causal relationship within the first condition, to the storage unit 600. In this way, when the probability that the first causal relationship unconditionally holds within the first condition is relatively high, the updating unit 603 can add the first hypothesis which unconditionally indicates the first causal relationship within the first condition to the storage unit 600. Then, the updating unit 603 can appropriately update the storage unit 600 so that the stored contents of the storage unit 600 represent the already determined conditions under which the first causal relationship holds.

[0091] Specifically, when the updating unit 603 determines that the calculated ratio is equal to or greater than the threshold, the updating unit 603 deletes from the storage unit 600 a plurality of hypotheses indicating the same first causal relationship in different conditions included in the first condition. More specifically, instead of adding the first hypothesis to the storage unit 600, the updating unit 603 deletes from the storage unit 600 a plurality of hypotheses indicating the same first causal relationship in different conditions included in the first condition. Specifically, when the updating unit 603 determines that the ratio is less than the threshold, the updating unit 603 leaves in the storage unit 600 a plurality of hypotheses indicating the same first causal relationship in different conditions included in the first condition.

[0092] As a result, the update unit 603 can determine that a plurality of hypotheses indicating the same first causal relationship in different conditions included in the first condition are included in the first hypothesis, and therefore the plurality of hypotheses do not need to be stored in the storage unit 600. Therefore, the update unit 603 can delete a plurality of hypotheses indicating the same first causal relationship in different conditions included in the first condition from the storage unit 600, and can suppress an increase in the amount of data in the storage unit 600. The update unit 603 can leave a plurality of hypotheses indicating the same first causal relationship in different conditions included in the first condition in the storage unit 600 until the first hypothesis is added to the storage unit 600, and can control the storage unit 600 to respect existing research.

[0093] Specifically, when the calculated ratio is equal to or greater than a threshold, if a second hypothesis that is different from the multiple hypotheses and that provisionally indicates the first causal relationship under the first condition under undetermined conditions is stored in the storage unit 600, the update unit 603 deletes the second hypothesis from the storage unit 600. More specifically, instead of adding the first hypothesis to the storage unit 600, the update unit 603 deletes the second hypothesis from the storage unit 600. Specifically, when the ratio is less than the threshold, the update unit 603 leaves the second hypothesis in the storage unit 600.

[0094] As a result, the updating unit 603 can determine that since the second hypothesis is included in the first hypothesis, the second hypothesis does not need to be stored in the storage unit 600. As a result, the updating unit 603 can delete the second hypothesis from the storage unit 600, and can suppress an increase in the amount of data in the storage unit 600. The updating unit 603 can leave the second hypothesis in the storage unit 600 until the first hypothesis is added to the storage unit 600, and can control the storage unit 600 to respect existing research.

[0095] (Example of functional configuration of information processing system 300) Next, an example of a functional configuration of the information processing system 300 will be described with reference to FIG.

[0096] 7 is a block diagram showing an example of a functional configuration of the information processing system 300. In the information processing system 300, the information processing device 100 includes a condition ratio calculation unit 711, a knowledge reduction unit 712, and a hypothesis addition unit 713. The knowledge learning device 301 includes a condition assignment unit 721 and a knowledge comparison unit 722.

[0097] The knowledge learning device 301 can access a knowledge DB 701 and a hypothesis DB 702. The knowledge learning device 301 has, for example, the knowledge DB 701. The information processing device 100 can access a hypothesis DB 702 and an extended knowledge DB 703. The extended knowledge DB 703 corresponds to, for example, the extended knowledge DB 500. The information processing device 100 has, for example, the extended knowledge DB 703.

[0098] The knowledge DB 701 stores hypotheses obtained by existing research. The knowledge DB 701 stores, for example, hypotheses indicating causal relationships between elements under undetermined conditions. The hypothesis DB 702 stores new hypotheses generated by the knowledge learning device 301. The hypothesis DB 702 stores, for example, new conditional hypotheses generated by the knowledge learning device 301. The hypothesis DB 702 specifically stores new hypotheses indicating causal relationships between elements under certain conditions.

[0099] The extended knowledge DB 703 stores conditional hypotheses. The extended knowledge DB 703 stores hypotheses indicating causal relationships between elements under certain conditions. The extended knowledge DB 703 may store hypotheses corresponding to hypotheses stored in the knowledge DB 701. Specifically, the extended knowledge DB 703 may store hypotheses in which a symbol Φ indicating an undetermined condition is associated with a causal relationship indicated by a hypothesis stored in the knowledge DB 701.

[0100] For example, the information processing device 100 may generate a hypothesis in which a symbol Φ indicating an undetermined condition is associated with a causal relationship indicated by a hypothesis stored in the knowledge DB 701 , and add the hypothesis to the extended knowledge DB 703 .

[0101] The condition assignment unit 721 refers to the extended knowledge DB 703, identifies the condition to be verified, and outputs it to the knowledge comparison unit 722. The knowledge comparison unit 722 accepts the condition to be verified, and generates a new hypothesis indicating a causal relationship between elements in the target condition by AI. The knowledge comparison unit 722 adds the generated new hypothesis to the hypothesis DB 702.

[0102] The condition ratio calculation unit 711 extracts one or more hypotheses that indicate each causal relationship under different conditions for each causal relationship, and calculates the condition ratio for the extracted one or more hypotheses. The condition ratio is the ratio of the number of elements used in the union of the extracted one or more hypotheses to the number of elements that can be used to express the condition. This allows the condition ratio calculation unit 711 to obtain a guideline for determining how to preferably update the contents stored in the extended knowledge DB 703.

[0103] The knowledge reduction unit 712 updates the storage contents of the extended knowledge DB 703. The knowledge reduction unit 712 determines whether or not the calculated condition ratio is equal to or greater than a threshold for each causal relationship. The threshold is, for example, set in advance by a user. The knowledge reduction unit 712 updates the storage contents of the extended knowledge DB 703 based on whether or not the calculated condition ratio is equal to or greater than a threshold for each causal relationship.

[0104] For example, when the calculated condition ratio for the first causal relationship is equal to or greater than a threshold, the knowledge reduction unit 712 generates a new hypothesis unconditionally indicating the first causal relationship and adds it to the extended knowledge DB 703. For example, when the calculated condition ratio for the first causal relationship is less than a threshold, the knowledge reduction unit 712 does not generate a new hypothesis unconditionally indicating the first causal relationship.

[0105] As a result, when the probability that the first causal relationship unconditionally holds is relatively high, the knowledge reduction unit 712 can add a new hypothesis that unconditionally indicates the first causal relationship to the extended knowledge DB 703. Then, the knowledge reduction unit 712 can appropriately update the storage contents of the extended knowledge DB 703 so that the storage contents of the extended knowledge DB 703 represent the already determined conditions under which the first causal relationship holds.

[0106] For example, when the calculated condition ratio for the first causal relationship is equal to or greater than a threshold, the knowledge reduction unit 712 unconditionally adds a new hypothesis indicating the first causal relationship to the extended knowledge DB 703, but instead deletes one or more hypotheses indicating the extracted first causal relationship from the extended knowledge DB 703. For example, when the calculated condition ratio for the first causal relationship is less than a threshold, the knowledge reduction unit 712 does not delete one or more hypotheses indicating the extracted first causal relationship from the extended knowledge DB 703.

[0107] Here, the one or more hypotheses indicating the first causal relationship under different conditions are included in the new hypothesis added to the extended knowledge DB 703. For this reason, the knowledge reduction unit 712 can determine that it is not necessary to store the one or more hypotheses indicating the first causal relationship under different conditions in the extended knowledge DB 703. The knowledge reduction unit 712 can delete, from the extended knowledge DB 703, a plurality of hypotheses indicating the same first causal relationship under different conditions, and can suppress an increase in the amount of data in the extended knowledge DB 703.

[0108] For example, when the calculated condition ratio for the first causal relationship is equal to or greater than a threshold, if an existing hypothesis indicating the first causal relationship with undetermined conditions exists in the extended knowledge DB 703, the knowledge reduction unit 712 deletes the existing hypothesis indicating the first causal relationship with undetermined conditions from the extended knowledge DB 703. For example, when the calculated condition ratio for the first causal relationship is less than a threshold, the knowledge reduction unit 712 does not delete the existing hypothesis indicating the first causal relationship with undetermined conditions from the extended knowledge DB 703.

[0109] This enables the knowledge reduction unit 712 to delete from the extended knowledge DB 703 an existing hypothesis indicating the first causal relationship under undetermined conditions, thereby suppressing an increase in the amount of data in the extended knowledge DB 703. The knowledge reduction unit 712 can appropriately update the storage contents of the extended knowledge DB 703 so that the storage contents of the extended knowledge DB 703 represent the determined conditions under which the first causal relationship holds.

[0110] Here, a case has been described in which the knowledge reduction unit 712 unconditionally adds a new hypothesis indicating the first causal relationship to the extended knowledge DB 703 when the calculated condition ratio for the first causal relationship is equal to or greater than a threshold value, but this is not limiting. For example, there may be a case in which the knowledge reduction unit 712 does not unconditionally add a new hypothesis indicating the first causal relationship to the extended knowledge DB 703 when the calculated condition ratio for the first causal relationship is equal to or greater than a threshold value.

[0111] The hypothesis adding unit 713 extracts a new hypothesis added to the hypothesis DB 702 and adds it to the extended knowledge DB 703. If the new hypothesis added to the hypothesis DB 702 is not included in the hypotheses stored in the extended knowledge DB 703, the hypothesis adding unit 713 extracts the new hypothesis added to the hypothesis DB 702 and adds it to the extended knowledge DB 703. If the new hypothesis added to the hypothesis DB 702 is included in the hypotheses stored in the extended knowledge DB 703, the hypothesis adding unit 713 does not extract the new hypothesis added to the hypothesis DB 702.

[0112] (Operation example of information processing device 100) Next, operation examples of the information processing device 100 will be described with reference to Fig. 8 to Fig. 13. First, operation example 1 of the information processing device 100 will be described with reference to Fig. 8.

[0113] Fig. 8 is an explanatory diagram showing a first operation example of the information processing device 100. In Fig. 8, the information processing device 100 has an extended knowledge DB 800. The extended knowledge DB 800 stores, for example, a hypothesis "X:A→B" indicating a causal relationship "A→B" in condition X, a hypothesis "Y:A→B" indicating a causal relationship "A→B" in condition Y, and a hypothesis "Z:A→B" indicating a causal relationship "A→B" in condition Z.

[0114] The extended knowledge DB 800 may store, for example, a hypothesis "Φ:A→B" indicating a causal relationship "A→B" with undetermined conditions. Φ indicates an undetermined condition. Specifically, Φ is treated as a symbol indicating a condition of the universal set Ω provisionally, although the condition is undetermined. Ω includes, for example, condition X, condition Y, and condition Z. Specifically, Ω includes all conditions.

[0115] For example, the information processing device 100 generates a hypothesis that associates a symbol Φ indicating an undetermined condition with a causal relationship indicated by a hypothesis stored in an existing knowledge DB, and adds the hypothesis to the extended knowledge DB 800, so that the extended knowledge DB 800 may store a hypothesis “Φ: A→B.” The extended knowledge DB 800 corresponds to, for example, the extended knowledge DB 500.

[0116] The information processing device 100 reads out one or more hypotheses indicating a causal relationship "A→B" under different conditions stored in the extended knowledge DB 800. The information processing device 100 reads out multiple hypotheses including, for example, a hypothesis "X:A→B" indicating a causal relationship "A→B" under condition X, a hypothesis "Y:A→B" indicating a causal relationship "A→B" under condition Y, and a hypothesis "Z:A→B" indicating a causal relationship "A→B" under condition Z.

[0117] The information processing device 100 calculates the condition ratio for the multiple hypotheses read out. For example, the information processing device 100 calculates the ratio of a union of the conditions X, Y, and Z to a universal set Ω that includes the conditions X, Y, and Z, as the condition ratio. Specifically, the information processing device 100 calculates the ratio of the number of elements expressing the conditions included in the union to the total number of elements expressing the conditions included in the universal set Ω, as the condition ratio. An example of calculating the condition ratio will be described later.

[0118] The information processing device 100 determines whether the calculated condition ratio is equal to or greater than a threshold. The threshold is, for example, set in advance by a user. The threshold is, for example, 0<ε≦1. For example, when the calculated condition ratio is equal to or greater than the threshold, the information processing device 100 adds a hypothesis "Ω:A→B" that unconditionally indicates the causal relationship "A→B" to the extended knowledge DB 800. Ω indicates that it has been determined that the causal relationship "A→B" holds under any condition.

[0119] As a result, when the probability that the causal relationship "A → B" unconditionally holds is relatively high, the information processing device 100 can add the hypothesis "Ω:A → B" that unconditionally indicates the causal relationship "A → B" to the extended knowledge DB 800. The information processing device 100 can appropriately update the storage contents of the extended knowledge DB 800 so that the storage contents of the extended knowledge DB 800 represent the conditions that have been determined for the causal relationship "A → B" to hold.

[0120] When the calculated condition ratio is equal to or greater than the threshold, the information processing device 100 deletes the one or more read hypotheses from the extended knowledge DB 800. When the calculated condition ratio is equal to or greater than the threshold, for example, the information processing device 100 deletes the hypotheses "X: A→B", "Y: A→B", and "Z: A→B" from the extended knowledge DB 800.

[0121] As a result, the information processing device 100 can determine that the hypothesis "X:A→B", the hypothesis "Y:A→B", and the hypothesis "Z:A→B" are definitely included in the hypothesis "Ω:A→B", and do not need to be stored in the extended knowledge DB 800. The information processing device 100 can delete the hypothesis "X:A→B", the hypothesis "Y:A→B", and the hypothesis "Z:A→B" from the extended knowledge DB 800, and can suppress an increase in the amount of data in the extended knowledge DB 800.

[0122] For example, when the calculated condition ratio is equal to or greater than a threshold value, the information processing device 100 deletes the hypothesis "Φ:A→B" indicating the causal relationship "A→B" with the condition undetermined from the extended knowledge DB 800. This enables the information processing device 100 to suppress an increase in the amount of data in the extended knowledge DB 800. The information processing device 100 can appropriately update the storage contents of the extended knowledge DB 800 so that the storage contents of the extended knowledge DB 800 express that the condition for the causal relationship "A→B" to hold is not undetermined.

[0123] Thereafter, it is assumed that the information processing device 100 acquires a new hypothesis "P:A→B" that indicates a causal relationship "A→B" in the condition P. Since the new hypothesis "P:A→B" is included in the hypothesis "Ω:A→B", the information processing device 100 discards the new hypothesis "P:A→B" without adding it to the extended knowledge DB 800.

[0124] In this way, since Ω indicates that it is determined that the causal relationship "A → B" holds under any condition, the information processing device 100 does not need to add the conditional causal relationship "A → B" to the extended knowledge DB 800 thereafter. This allows the information processing device 100 to suppress an increase in the amount of data in the extended knowledge DB 800.

[0125] The information processing device 100 can prevent a user from being able to refer to both the hypothesis "Ω: A→B" and the hypothesis "P: A→B" when referring to the contents stored in the extended knowledge DB 800. Therefore, the information processing device 100 can prevent a user from misunderstanding the conditions for the causal relationship "A→B" to be established.

[0126] The information processing device 100 can prevent the user from being confused as to which of the hypothesis "Ω:A→B" and the hypothesis "P:A→B" is appropriate, for example. The information processing device 100 can prevent the user from misunderstanding the conditions for the causal relationship "A→B" to be established, for example, by overlooking the hypothesis "Ω:A→B" and referring only to the hypothesis "P:A→B".

[0127] Here, an example of calculating the condition ratio will be described. For example, there is a set Σ of all elements expressing a condition. The set Σ is a set obtained by extracting all elements used in hypotheses in the extended knowledge DB 800 without duplication. Here, it is assumed that Σ={A, B, C, D, E}. A condition is expressed by a logical formula using one or more elements of Σ. For example, X=A∪B. The condition ratio of the union of conditions X, Y, Z,··· for the universal set Ω is expressed as Rate(X, Y, Z,···; Ω)=|X∪Y∪Z∪···| / |Σ|. |X| is the number of elements covered by the logical formula X. |Σ| is the number of elements included in Σ.

[0128] Specifically, the condition X=A∪B and the condition Y=C∪D are considered. In this case, Rate(X,Y;Ω)=|X∪Y| / |Σ|=|A∪B∪C∪D| / |{A,B,C,D,E}|=4 / 5=0.8.

[0129] Specifically, a case where condition X=A∩C, condition Y=B∪C, and condition Z=D is considered. In this case, X∪Y∪Z=(A∩C)∪(B∪C)∪D=(A∪B∪C∪D)∩(B∪C∪D)=B∪C∪D. Therefore, Rate(X,Y,Z;Ω)=|X∪Y∪Z| / |Σ|=|B∪C∪D| / |Σ|=3 / 5=0.6. When calculating the Rate, the information processing device 100 does not need to consider instances of observed data related to the hypothesis. Next, an operation example 2 of the information processing device 100 will be described with reference to FIG. 9.

[0130] Fig. 9 is an explanatory diagram showing a second operation example of the information processing device 100. In Fig. 9, the information processing device 100 has an extended knowledge DB 900. The extended knowledge DB 900 stores, for example, a hypothesis "X:A→B" indicating a causal relationship "A→B" in condition X, a hypothesis "Y:A→B" indicating a causal relationship "A→B" in condition Y, and a hypothesis "Z:A→B" indicating a causal relationship "A→B" in condition Z.

[0131] The information processing device 100 reads out one or more hypotheses indicating a causal relationship "A→B" under different conditions stored in the extended knowledge DB 900. The information processing device 100 reads out multiple hypotheses including, for example, a hypothesis "X:A→B" indicating a causal relationship "A→B" under condition X, a hypothesis "Y:A→B" indicating a causal relationship "A→B" under condition Y, and a hypothesis "Z:A→B" indicating a causal relationship "A→B" under condition Z.

[0132] The information processing device 100 calculates a condition ratio for the multiple read hypotheses. For example, the information processing device 100 calculates, as the condition ratio, the ratio of a union of condition X, condition Y, and condition Z to a universal set Ω that includes condition X, condition Y, and condition Z. Specifically, the information processing device 100 calculates, as the condition ratio, the ratio of the number of elements expressing the conditions included in the union to the total number of elements expressing the conditions included in the universal set Ω.

[0133] The information processing device 100 determines whether the calculated condition ratio is equal to or greater than a threshold. The threshold is, for example, set in advance by a user. The threshold is, for example, 0<ε≦1. For example, when the calculated condition ratio is equal to or greater than the threshold, the information processing device 100 adds a hypothesis "Ω:A→B" that unconditionally indicates the causal relationship "A→B" to the extended knowledge DB 900. Ω indicates that it has been determined that the causal relationship "A→B" holds under any condition.

[0134] Thereby, when the probability that the causal relationship "A → B" unconditionally holds is relatively high, the information processing device 100 can add the hypothesis "Ω:A → B" that unconditionally indicates the causal relationship "A → B" to the extended knowledge DB 900. The information processing device 100 can appropriately update the storage contents of the extended knowledge DB 900 so that the storage contents of the extended knowledge DB 900 represent the already determined conditions under which the causal relationship "A → B" holds.

[0135] When the calculated condition ratio is equal to or greater than the threshold, the information processing device 100 deletes the one or more read hypotheses from the extended knowledge DB 900. When the calculated condition ratio is equal to or greater than the threshold, for example, the information processing device 100 deletes the hypotheses "X: A→B", "Y: A→B", and "Z: A→B" from the extended knowledge DB 900.

[0136] As a result, the information processing device 100 can determine that the hypothesis "X:A→B", the hypothesis "Y:A→B", and the hypothesis "Z:A→B" are definitely included in the hypothesis "Ω:A→B", and do not need to be stored in the extended knowledge DB 900. The information processing device 100 can delete the hypothesis "X:A→B", the hypothesis "Y:A→B", and the hypothesis "Z:A→B" from the extended knowledge DB 900, and can suppress an increase in the amount of data in the extended knowledge DB 900.

[0137] Thereafter, it is assumed that the information processing device 100 acquires a new hypothesis "P:A→B" that indicates a causal relationship "A→B" in the condition P. Since the new hypothesis "P:A→B" is included in the hypothesis "Ω:A→B", the information processing device 100 discards the new hypothesis "P:A→B" without adding it to the extended knowledge DB 900.

[0138] In this way, since Ω indicates that the causal relationship "A → B" is determined to hold under any condition, the information processing device 100 does not need to add the conditional causal relationship "A → B" to the extended knowledge DB 900 thereafter. This allows the information processing device 100 to suppress an increase in the amount of data in the extended knowledge DB 900.

[0139] The information processing device 100 can prevent a user from being able to refer to both the hypothesis "Ω: A→B" and the hypothesis "P: A→B" when referring to the contents stored in the extended knowledge DB 900. Therefore, the information processing device 100 can prevent a user from misunderstanding the conditions for the causal relationship "A→B" to be established.

[0140] The information processing device 100 can prevent, for example, the user from being confused as to which of the hypothesis "Ω:A→B" and the hypothesis "P:A→B" is appropriate. The information processing device 100 can prevent, for example, the user from overlooking the hypothesis "Ω:A→B" and referring only to the hypothesis "P:A→B", leading to a misunderstanding of the conditions under which the causal relationship "A→B" is established. Next, an operation example 3 of the information processing device 100 will be described with reference to FIG. 10.

[0141] Fig. 10 is an explanatory diagram showing an operation example 3 of the information processing device 100. In Fig. 10, the information processing device 100 has an extended knowledge DB 1000. The extended knowledge DB 1000 stores, for example, a hypothesis "X:A→B" indicating a causal relationship "A→B" in condition X, a hypothesis "Y:A→B" indicating a causal relationship "A→B" in condition Y, and a hypothesis "Z:A→B" indicating a causal relationship "A→B" in condition Z.

[0142] The extended knowledge DB 1000 may store, for example, a hypothesis "Φ:A→B" indicating a causal relationship "A→B" with undetermined conditions. Φ indicates an undetermined condition. Specifically, Φ is treated as a symbol indicating a condition of the universal set Ω provisionally, although the condition is undetermined. Ω includes, for example, condition X, condition Y, and condition Z. Specifically, Ω includes all conditions.

[0143] For example, the information processing device 100 may generate a hypothesis that associates the symbol Φ, which indicates an undetermined condition, with a causal relationship indicated by a hypothesis stored in an existing knowledge DB, and add the hypothesis to the extended knowledge DB 1000, so that the extended knowledge DB 1000 may store the hypothesis "Φ:A→B."

[0144] The information processing device 100 reads out one or more hypotheses indicating a causal relationship "A→B" under different conditions stored in the extended knowledge DB 1000. The information processing device 100 reads out multiple hypotheses including, for example, a hypothesis "X:A→B" indicating a causal relationship "A→B" under condition X, a hypothesis "Y:A→B" indicating a causal relationship "A→B" under condition Y, and a hypothesis "Z:A→B" indicating a causal relationship "A→B" under condition Z.

[0145] The information processing device 100 calculates a condition ratio for the multiple read hypotheses. For example, the information processing device 100 calculates, as the condition ratio, the ratio of a union of condition X, condition Y, and condition Z to a universal set Ω that includes condition X, condition Y, and condition Z. Specifically, the information processing device 100 calculates, as the condition ratio, the ratio of the number of elements expressing the conditions included in the union to the total number of elements expressing the conditions included in the universal set Ω.

[0146] The information processing device 100 determines whether the calculated condition ratio is equal to or greater than a threshold value. The threshold value is, for example, set in advance by a user. The threshold value is, for example, 0<ε≦1. For example, when the calculated condition ratio is equal to or greater than the threshold value, the information processing device 100 deletes the hypothesis "Φ:A→B" indicating the causal relationship "A→B" with the condition undetermined from the extended knowledge DB 1000. This enables the information processing device 100 to suppress an increase in the amount of data in the extended knowledge DB 1000.

[0147] The information processing device 100 can appropriately update the storage contents of the extended knowledge DB 1000 so that the storage contents of the extended knowledge DB 1000 can distinguish between a condition where it is determined that the causal relationship "A → B" holds and an undetermined condition. The information processing device 100 can determine under which condition it is preferable to research whether the causal relationship "A → B" holds, which can contribute to future research.

[0148] Until the calculated condition ratio becomes equal to or greater than a threshold value, the information processing device 100 can provisionally determine that the causal relationship "A → B" holds unconditionally, although the condition is not yet determined, and can leave the hypothesis "Φ:A → B" in the extended knowledge DB 1000. Therefore, the information processing device 100 can appropriately manage the extended knowledge DB 1000 so as to respect the hypothesis "Φ:A → B" obtained by existing research or the like.

[0149] Thereafter, it is assumed that the information processing device 100 acquires a new hypothesis "P:A→B" that indicates a causal relationship "A→B" in condition P. Since the new hypothesis "P:A→B" is not included in the hypotheses stored in the extended knowledge DB1000, the information processing device 100 adds the new hypothesis "P:A→B" to the extended knowledge DB1000. This enables the information processing device 100 to appropriately update the extended knowledge DB1000.

[0150] As described above, the information processing device 100 can incorporate hypotheses stored in the existing knowledge DB into the extended knowledge DB without contradiction by using the symbol Φ indicating that the condition is not yet determined. When a relatively large number of hypotheses indicating the same causal relationship under different conditions are accumulated in the extended knowledge DB, the information processing device 100 can integrate the hypotheses by using the symbol Ω indicating the universal set, thereby suppressing an increase in the amount of data in the extended knowledge DB.

[0151] When a relatively large number of hypotheses showing the same causal relationship under different conditions are accumulated in the extended knowledge DB, the information processing device 100 can temporarily delete hypotheses using the symbol Φ representing the universal set, thereby suppressing an increase in the amount of data in the extended knowledge DB. The information processing device 100 may selectively perform the operations shown in Figures 8 and 9 and the operation shown in Figure 10 according to the user's policy for updating the extended knowledge DB.

[0152] 8 and 9, the information processing device 100 integrates a plurality of hypotheses using conditions X, Y, and Z included in the universal set Ω into a hypothesis using a universal set Ω that includes all conditions, but the present invention is not limited to this. For example, the information processing device 100 may integrate a plurality of hypotheses using conditions X1, X2, and X3 included in the condition XΩ into a hypothesis using condition XΩ. This case will be described later in detail with reference to FIGS. 11 and 12.

[0153] Similarly, in the example of Fig. 10, the information processing device 100 determines whether or not to delete a hypothesis using an undefined symbol Φ that provisionally represents a condition of the universal set Ω, depending on the number of determined conditional hypotheses, but the present invention is not limited to this. For example, the information processing device 100 may determine whether or not to delete a hypothesis using an undefined symbol XΦ that provisionally represents a condition XΩ, depending on the number of determined conditional hypotheses included in the condition XΩ. This case will be described later in detail with reference to Fig. 13. Next, an operation example 4 of the information processing device 100 will be described with reference to Fig. 11.

[0154] Fig. 11 is an explanatory diagram showing an operation example 4 of the information processing device 100. In Fig. 11, the information processing device 100 has an extended knowledge DB 1100. The extended knowledge DB 1100 stores, for example, a hypothesis "X1:A→B" indicating a causal relationship "A→B" in condition X1, a hypothesis "X2:A→B" indicating a causal relationship "A→B" in condition X2, and a hypothesis "X3:A→B" indicating a causal relationship "A→B" in condition X3.

[0155] The extended knowledge DB 1100 may store, for example, a hypothesis "XΦ: A→B" that provisionally indicates a causal relationship "A→B" in condition XΩ, with the condition undetermined. XΦ indicates that the condition is undetermined. Specifically, XΦ is treated as a symbol that provisionally indicates the condition of condition XΩ, although the condition is undetermined. XΩ includes, for example, condition X1, condition X2, and condition X3. XΩ may include, for example, other condition Xi, where i=4, 5, . . .

[0156] For example, the information processing device 100 may generate a hypothesis that associates a symbol XΦ indicating an undetermined condition in condition X with a causal relationship that is provisionally treated as being true in condition XΩ, which is indicated by a hypothesis stored in an existing knowledge DB, and add the hypothesis to the extended knowledge DB 1100. For this reason, the extended knowledge DB 1100 may store the hypothesis "Φ: A→B".

[0157] The information processing device 100 reads out one or more hypotheses indicating a causal relationship "A→B" under different conditions included in the condition XΩ, which are stored in the extended knowledge DB 1100. The information processing device 100 reads out multiple hypotheses including, for example, a hypothesis "X1:A→B" indicating the causal relationship "A→B", a hypothesis "X2:A→B" indicating the causal relationship "A→B", and a hypothesis "X3:A→B" indicating the causal relationship "A→B".

[0158] The information processing device 100 calculates the condition ratio for the multiple hypotheses read out. For example, the information processing device 100 calculates the ratio of a union of conditions X1, X2, and X3 to condition XΩ that includes conditions X1, X2, and X3 as the condition ratio. Specifically, the information processing device 100 calculates the ratio of the number of elements expressing the conditions included in the union to the total number of elements expressing the conditions included in condition XΩ as the condition ratio.

[0159] The information processing device 100 judges whether the calculated condition ratio is equal to or greater than a threshold. The threshold is, for example, set in advance by a user. The threshold is, for example, 0<ε≦1. For example, when the calculated condition ratio is equal to or greater than the threshold, the information processing device 100 determines that the causal relationship "A→B" holds under condition XΩ, and adds a hypothesis "XΩ:A→B" that unconditionally indicates the causal relationship "A→B" within condition XΩ to the extended knowledge DB 1100. XΩ indicates that it has been determined that the causal relationship "A→B" holds under any condition within condition XΩ.

[0160] As a result, when there is a relatively high probability that the causal relationship "A → B" unconditionally holds within the condition XΩ, the information processing device 100 can add a hypothesis "XΩ:A → B" that unconditionally indicates the causal relationship "A → B" within the condition XΩ to the extended knowledge DB 1100. The information processing device 100 can appropriately update the storage contents of the extended knowledge DB 1100 so that the storage contents of the extended knowledge DB 1100 represent the already determined conditions under which the causal relationship "A → B" holds.

[0161] When the calculated condition ratio is equal to or greater than the threshold, the information processing device 100 deletes the one or more read hypotheses from the extended knowledge DB 1100. When the calculated condition ratio is equal to or greater than the threshold, for example, the information processing device 100 deletes the hypotheses "X1: A→B", "X2: A→B", and "X3: A→B" from the extended knowledge DB 1100.

[0162] As a result, the information processing device 100 can determine that the hypothesis "X1:A→B", the hypothesis "X2:A→B", and the hypothesis "X3:A→B" are definitely included in the hypothesis "XΩ:A→B", and that they do not need to be stored in the extended knowledge DB 1100. The information processing device 100 can delete the hypothesis "X1:A→B", the hypothesis "X2:A→B", and the hypothesis "X3:A→B" from the extended knowledge DB 1100, and can suppress an increase in the amount of data in the extended knowledge DB 1100.

[0163] For example, when the calculated condition ratio is equal to or greater than a threshold value, the information processing device 100 deletes the hypothesis "XΦ:A→B" that provisionally indicates the causal relationship "A→B" under condition XΩ with the condition undetermined from the extended knowledge DB 1100. This enables the information processing device 100 to suppress an increase in the amount of data in the extended knowledge DB 1100. The information processing device 100 can appropriately update the storage contents of the extended knowledge DB 1100 so that the storage contents of the extended knowledge DB 1100 express that the condition for the causal relationship "A→B" to hold is not undetermined.

[0164] After that, it is assumed that the information processing device 100 acquires a new hypothesis "XP:A→B" that indicates a causal relationship "A→B" in the condition XP. Since the new hypothesis "XP:A→B" is included in the hypothesis "XΩ:A→B", the information processing device 100 discards the new hypothesis "XP:A→B" without adding it to the extended knowledge DB 1100.

[0165] In this way, since XΩ indicates that it is determined that the causal relationship "A → B" holds under any condition in the condition XΩ, the information processing device 100 does not need to add the conditional causal relationship "A → B" included in the condition XΩ to the extended knowledge DB 1100. This allows the information processing device 100 to suppress an increase in the amount of data in the extended knowledge DB 1100.

[0166] The information processing device 100 can prevent a user from being able to refer to both the hypothesis "XΩ:A→B" and the hypothesis "XP:A→B" when referring to the contents stored in the extended knowledge DB 1100. Therefore, the information processing device 100 can prevent a user from misunderstanding the conditions for the causal relationship "A→B" to be established.

[0167] The information processing device 100 can prevent, for example, the user from being confused as to which of the hypotheses "XΩ:A→B" and "XP:A→B" is appropriate. The information processing device 100 can prevent, for example, the user from overlooking the hypothesis "XΩ:A→B" and referring only to the hypothesis "XP:A→B", leading to a misunderstanding of the conditions under which the causal relationship "A→B" is established. Next, a fifth operation example of the information processing device 100 will be described with reference to FIG. 12.

[0168] Fig. 12 is an explanatory diagram showing a fifth operation example of the information processing device 100. In Fig. 12, the information processing device 100 has an extended knowledge DB 1200. The extended knowledge DB 1200 stores, for example, a hypothesis "X1:A→B" indicating a causal relationship "A→B" in condition X1, a hypothesis "X2:A→B" indicating a causal relationship "A→B" in condition X2, and a hypothesis "X3:A→B" indicating a causal relationship "A→B" in condition X3.

[0169] The information processing device 100 reads out one or more hypotheses indicating a causal relationship "A→B" in different conditions included in the condition XΩ, which are stored in the extended knowledge DB 1200. The information processing device 100 reads out multiple hypotheses including, for example, a hypothesis "X1:A→B" indicating the causal relationship "A→B", a hypothesis "X2:A→B" indicating the causal relationship "A→B", and a hypothesis "X3:A→B" indicating the causal relationship "A→B".

[0170] The information processing device 100 calculates the condition ratio for the multiple hypotheses read out. For example, the information processing device 100 calculates the ratio of a union of conditions X1, X2, and X3 to condition XΩ that includes conditions X1, X2, and X3 as the condition ratio. Specifically, the information processing device 100 calculates the ratio of the number of elements expressing the conditions included in the union to the total number of elements expressing the conditions included in condition XΩ as the condition ratio.

[0171] The information processing device 100 judges whether the calculated condition ratio is equal to or greater than a threshold. The threshold is, for example, set in advance by a user. The threshold is, for example, 0<ε≦1. For example, when the calculated condition ratio is equal to or greater than the threshold, the information processing device 100 adds a hypothesis "XΩ:A→B" that indicates the causal relationship "A→B" unconditionally within the condition XΩ to the extended knowledge DB 1200. XΩ indicates that it has been determined that the causal relationship "A→B" holds under any condition within the condition XΩ.

[0172] As a result, when there is a relatively high probability that the causal relationship "A → B" holds unconditionally within the condition XΩ, the information processing device 100 can add a hypothesis "XΩ:A → B" that indicates the causal relationship "A → B" unconditionally within the condition XΩ to the extended knowledge DB 1200. The information processing device 100 can appropriately update the storage contents of the extended knowledge DB 1200 so that the storage contents of the extended knowledge DB 1200 represent the known conditions under which the causal relationship "A → B" holds.

[0173] When the calculated condition ratio is equal to or greater than the threshold, the information processing device 100 deletes the one or more read hypotheses from the extended knowledge DB 1200. When the calculated condition ratio is equal to or greater than the threshold, for example, the information processing device 100 deletes the hypotheses "X1: A→B", "X2: A→B", and "X3: A→B" from the extended knowledge DB 1200.

[0174] As a result, the information processing device 100 can determine that the hypotheses "X1:A→B", "X2:A→B", and "X3:A→B" are definitely included in the hypothesis "XΩ:A→B", and that they do not need to be stored in the extended knowledge DB 1200. The information processing device 100 can delete the hypotheses "X1:A→B", "X2:A→B", and "X3:A→B" from the extended knowledge DB 1200, and can suppress an increase in the amount of data in the extended knowledge DB 1200.

[0175] After that, it is assumed that the information processing device 100 acquires a new hypothesis "XP:A→B" that indicates a causal relationship "A→B" in the condition XP. Since the new hypothesis "XP:A→B" is included in the hypothesis "XΩ:A→B", the information processing device 100 discards the new hypothesis "XP:A→B" without adding it to the extended knowledge DB 1200.

[0176] In this way, since XΩ indicates that it is determined that the causal relationship "A → B" holds under any condition, the information processing device 100 does not need to add the conditional causal relationship "A → B" to the extended knowledge DB 1200 thereafter. This allows the information processing device 100 to suppress an increase in the amount of data in the extended knowledge DB 1200.

[0177] The information processing device 100 can prevent a user from being able to refer to both the hypothesis "XΩ:A→B" and the hypothesis "XP:A→B" when referring to the contents stored in the extended knowledge DB 1200. Therefore, the information processing device 100 can prevent a user from misunderstanding the conditions for the causal relationship "A→B" to be established.

[0178] The information processing device 100 can prevent, for example, the user from being confused as to which of the hypotheses "XΩ:A→B" and "XP:A→B" is appropriate. The information processing device 100 can prevent, for example, the user from overlooking the hypothesis "XΩ:A→B" and referring only to the hypothesis "XP:A→B", and misunderstanding the conditions under which the causal relationship "A→B" is established. Next, a sixth operation example of the information processing device 100 will be described with reference to FIG. 13.

[0179] Fig. 13 is an explanatory diagram showing an operation example 6 of the information processing device 100. In Fig. 13, the information processing device 100 has an extended knowledge DB 1300. The extended knowledge DB 1300 stores, for example, a hypothesis "X1:A→B" indicating a causal relationship "A→B" in condition X1, a hypothesis "X2:A→B" indicating a causal relationship "A→B" in condition X2, and a hypothesis "X3:A→B" indicating a causal relationship "A→B" in condition X3.

[0180] The extended knowledge DB 1300 may store, for example, a hypothesis "XΦ: A→B" that provisionally indicates a causal relationship "A→B" in condition XΩ with the condition undetermined. XΦ indicates that the condition is undetermined. Specifically, XΦ is treated as a symbol that provisionally indicates the condition of condition XΩ, although the condition is undetermined. XΩ includes, for example, condition X1, condition X2, and condition X3. XΩ may include, for example, other condition Xi. i=4, 5, . . .

[0181] For example, the information processing device 100 may generate a hypothesis that associates a symbol XΦ indicating an undetermined condition in condition X with a causal relationship that is provisionally treated as being true in condition XΩ, which is indicated by a hypothesis stored in an existing knowledge DB, and add the hypothesis to the extended knowledge DB 1300. For this reason, the extended knowledge DB 1300 may store the hypothesis "Φ: A→B".

[0182] The information processing device 100 reads out one or more hypotheses indicating a causal relationship "A→B" in different conditions included in the condition XΩ, which are stored in the extended knowledge DB 1300. The information processing device 100 reads out multiple hypotheses including, for example, a hypothesis "X1:A→B" indicating the causal relationship "A→B", a hypothesis "X2:A→B" indicating the causal relationship "A→B", and a hypothesis "X3:A→B" indicating the causal relationship "A→B".

[0183] The information processing device 100 calculates the condition ratio for the multiple hypotheses read out. For example, the information processing device 100 calculates the ratio of a union of conditions X1, X2, and X3 to condition XΩ that includes conditions X1, X2, and X3 as the condition ratio. Specifically, the information processing device 100 calculates the ratio of the number of elements expressing the conditions included in the union to the total number of elements expressing the conditions included in condition XΩ as the condition ratio.

[0184] The information processing device 100 determines whether the calculated condition ratio is equal to or greater than a threshold. The threshold is, for example, set in advance by a user. The threshold is, for example, 0<ε≦1. For example, when the calculated condition ratio is equal to or greater than the threshold, the information processing device 100 deletes from the extended knowledge DB 1300 a hypothesis "XΦ:A→B" that provisionally indicates a causal relationship "A→B" in the condition XΩ with the condition undetermined. This enables the information processing device 100 to suppress an increase in the amount of data in the extended knowledge DB 1300.

[0185] The information processing device 100 can appropriately update the storage contents of the extended knowledge DB 1300 so that the storage contents of the extended knowledge DB 1300 can distinguish between a condition where it is determined that the causal relationship "A → B" holds and an undetermined condition. The information processing device 100 can make it possible to determine under which condition it is preferable to research whether the causal relationship "A → B" holds, which can contribute to future research.

[0186] Until the calculated condition ratio becomes equal to or greater than a threshold value, the information processing device 100 provisionally determines that the causal relationship "A → B" unconditionally holds within the condition XΩ without determining the condition, and can leave the hypothesis "XΦ: A → B" in the extended knowledge DB 1300. Therefore, the information processing device 100 can appropriately manage the extended knowledge DB 1300 so as to respect the hypothesis "XΦ: A → B" obtained by existing research or the like.

[0187] After that, it is assumed that the information processing device 100 acquires a new hypothesis "X4: A → B" that indicates the causal relationship "A → B" in the condition X4. Since the new hypothesis "X4: A → B" is not included in the hypotheses stored in the extended knowledge DB 1300, the information processing device 100 adds the new hypothesis "X4: A → B" to the extended knowledge DB 1300. This enables the information processing device 100 to appropriately update the extended knowledge DB 1300.

[0188] As described above, the information processing device 100 can incorporate hypotheses stored in the existing knowledge DB into the extended knowledge DB without contradiction by using the symbol XΦ indicating that the condition is not yet determined. When a relatively large number of hypotheses indicating the same causal relationship in different conditions included in the condition XΩ are accumulated in the extended knowledge DB, the information processing device 100 can integrate the hypotheses by using the symbol XΩ representing the condition. Therefore, the information processing device 100 can suppress an increase in the amount of data in the extended knowledge DB.

[0189] When a relatively large number of hypotheses indicating the same causal relationship in different conditions included in condition XΩ are accumulated in the extended knowledge DB, the information processing device 100 can temporarily delete hypotheses using the symbol XΦ representing condition XΩ. Therefore, the information processing device 100 can suppress an increase in the amount of data in the extended knowledge DB. The information processing device 100 may selectively perform the operations shown in Figs. 11 and 12 and the operation shown in Fig. 13 according to a user's policy for updating the extended knowledge DB.

[0190] (First additional processing step) Next, an example of a first addition process procedure executed by the information processing device 100 will be described with reference to Fig. 14. The first addition process is realized by, for example, the CPU 401, storage areas such as the memory 402 and the recording medium 405, and the network I / F 403 shown in Fig. 4.

[0191] 14 is a flowchart showing an example of the procedure of the first addition process. In FIG 14, the current extended knowledge DB is assumed to be "D." First, the information processing device 100 receives an input of a hypothesis "P: A→B" (step S1401).

[0192] Next, the information processing device 100 determines whether or not a record of the hypothesis "P: A→B" exists in D (step S1402). If the record exists (step S1402: Yes), the information processing device 100 proceeds to processing of step S1404. On the other hand, if the record does not exist (step S1402: No), the information processing device 100 proceeds to processing of step S1403.

[0193] In step S1403, the information processing device 100 generates a record of the hypothesis "P: A→B" and adds it to D (step S1403). Next, the information processing device 100 outputs D (step S1404). Then, the information processing device 100 ends the first addition process. This allows the information processing device 100 to appropriately update the extended knowledge DB.

[0194] (First reduction process procedure) Next, an example of a first reduction process procedure executed by the information processing device 100 will be described with reference to Fig. 15. The first reduction process is realized by, for example, the CPU 401, storage areas such as the memory 402 and the recording medium 405, and the network I / F 403 shown in Fig. 4.

[0195] Fig. 15 is a flowchart showing an example of a first contraction process procedure. In Fig. 15, the current extended knowledge DB is "D1". The information processing device 100 prepares an empty extended knowledge DB "D2". First, the information processing device 100 determines whether or not a record exists in D1 (step S1501).

[0196] If the record does not exist (step S1501: No), the information processing device 100 proceeds to the process of step S1507. On the other hand, if the record exists (step S1501: Yes), the information processing device 100 proceeds to the process of step S1502.

[0197] In step S1502, the information processing device 100 extracts one or more records that indicate the same causal relationship "A → B" from D1 and deletes them from D1 (step S1502). Next, the information processing device 100 calculates a condition ratio based on the extracted one or more records (step S1503).

[0198] Then, the information processing device 100 determines whether the calculated condition ratio ≧ε (step S1504). If the condition ratio ≧ε (step S1504: Yes), the information processing device 100 proceeds to processing in step S1506. On the other hand, if the condition ratio ≧ε is not satisfied (step S1504: No), the information processing device 100 proceeds to processing in step S1505.

[0199] In step S1505, the information processing device 100 adds the one or more extracted records to D2 (step S1505). Then, the information processing device 100 returns to the process of step S1501.

[0200] In step S1506, the information processing device 100 adds a new record indicating the causal relationship "Ω:A→B" by aggregating the one or more extracted records to D2 (step S1506). Then, the information processing device 100 returns to the process of step S1501.

[0201] In step S1507, the information processing device 100 outputs D2 (step S1507). Then, the information processing device 100 ends the first reduction process. This enables the information processing device 100 to suppress an increase in the amount of data in the knowledge DB.

[0202] (Second additional processing procedure) Next, an example of a procedure of the second addition process executed by the information processing device 100 will be described with reference to Fig. 16. The second addition process is realized by, for example, the CPU 401, storage areas such as the memory 402 and the recording medium 405, and the network I / F 403 shown in Fig. 4.

[0203] 16 is a flowchart showing an example of the procedure of the second addition process. In FIG 16, the current extended knowledge DB is assumed to be "D." First, the information processing device 100 receives an input of a hypothesis "P: A→B" (step S1601).

[0204] Next, the information processing device 100 determines whether or not at least one of a record of the hypothesis "P: A→B" or a record of the hypothesis "Ω: A→B" exists in D (step S1602). If the record exists (step S1602: Yes), the information processing device 100 proceeds to processing of step S1604. On the other hand, if the record does not exist (step S1602: No), the information processing device 100 proceeds to processing of step S1603.

[0205] In step S1603, the information processing device 100 generates a record of the hypothesis "P: A→B" and adds it to D (step S1603). Next, the information processing device 100 outputs D (step S1604). Then, the information processing device 100 ends the second addition process. This allows the information processing device 100 to appropriately update the extended knowledge DB.

[0206] (Second reduction process procedure) Next, an example of a second reduction process procedure executed by the information processing device 100 will be described with reference to Fig. 17. The second reduction process is realized by, for example, the CPU 401, storage areas such as the memory 402 and the recording medium 405, and the network I / F 403 shown in Fig. 4.

[0207] Fig. 17 is a flowchart showing an example of the procedure of the second contraction process. In Fig. 17, the current extended knowledge DB is "D1." The information processing device 100 prepares an empty extended knowledge DB "D2."

[0208] First, the information processing device 100 determines whether or not a record exists in D1 (step S1701). If the record does not exist (step S1701: No), the information processing device 100 proceeds to processing of step S1708. On the other hand, if the record exists (step S1701: Yes), the information processing device 100 proceeds to processing of step S1702.

[0209] In step S1702, the information processing device 100 extracts one or more records that indicate the same causal relationship "A → B" from D1 and deletes them from D1 (step S1702). Next, the information processing device 100 calculates a condition ratio based on the extracted one or more records (step S1703).

[0210] Then, the information processing device 100 determines whether the calculated condition ratio ≧ε (step S1704). If the condition ratio ≧ε (step S1704: Yes), the information processing device 100 proceeds to step S1705. On the other hand, if the condition ratio ≧ε is not satisfied (step S1704: No), the information processing device 100 proceeds to step S1706.

[0211] In step S1705, the information processing device 100 determines whether or not a record that satisfies the condition Φ exists among the one or more extracted records (step S1705). If the record does not exist (step S1705: No), the information processing device 100 proceeds to processing of step S1706. On the other hand, if the record exists (step S1705: Yes), the information processing device 100 proceeds to processing of step S1707.

[0212] In step S1706, the information processing device 100 adds the one or more extracted records to D2 (step S1706). Then, the information processing device 100 returns to the process of step S1701.

[0213] In step S1707, the information processing device 100 adds, to D2, records other than the records that satisfy the condition Φ, among the one or more extracted records (step S1707). Then, the information processing device 100 returns to the process of step S1701.

[0214] In step S1708, the information processing device 100 outputs D2 (step S1708). Then, the information processing device 100 ends the second reduction process. This enables the information processing device 100 to suppress an increase in the amount of data in the knowledge DB.

[0215] Here, the information processing device 100 may change the order of the processes of some steps in the flowcharts of Figures 14 to 17. Furthermore, the information processing device 100 may omit the processes of some steps in the flowcharts of Figures 14 to 17.

[0216] As described above, the information processing device 100 can calculate the ratio of a union of multiple hypotheses indicating the same first causal relationship under different conditions stored in the storage unit to a universal set. The information processing device 100 can determine whether the calculated ratio is equal to or greater than a threshold. If the ratio is equal to or greater than a threshold, the information processing device 100 can add to the storage unit a first hypothesis that is different from the multiple hypotheses and that unconditionally indicates the first causal relationship in the universal set. This allows the information processing device 100 to make the storage unit appropriately updateable.

[0217] According to the information processing device 100, when the ratio is equal to or greater than a threshold, it is possible to delete a plurality of hypotheses from the storage unit, thereby enabling the information processing device 100 to suppress an increase in the amount of data in the storage unit.

[0218] According to the information processing device 100, if the ratio is equal to or greater than the threshold, and if a second hypothesis that is different from the multiple hypotheses and indicates the first causal relationship under undetermined conditions is stored in the storage unit, the second hypothesis can be deleted from the storage unit. This allows the information processing device 100 to suppress an increase in the amount of data in the storage unit.

[0219] According to the information processing device 100, it is possible to accept the input of a third hypothesis that indicates a first causal relationship under new conditions, different from the multiple hypotheses. According to the information processing device 100, when the input of the third hypothesis is accepted, if the first hypothesis is not stored in the storage unit, it is possible to add the input third hypothesis to the storage unit. According to the information processing device 100, when the input of the third hypothesis is accepted, if the first hypothesis is stored in the storage unit, it is possible to discard the input third hypothesis. In this way, the information processing device 100 can appropriately update the storage unit while suppressing an increase in the amount of data in the storage unit.

[0220] According to the information processing device 100, it is possible to calculate the ratio of the number of elements used in the union to the number of elements that can be used to express a condition for a plurality of hypotheses stored in the storage unit. According to the information processing device 100, it is possible to determine whether or not the calculated ratio is equal to or greater than a threshold value. This allows the information processing device 100 to calculate the ratio with high accuracy.

[0221] According to the information processing device 100, it is possible to output the stored contents of the storage unit. The information processing device 100 can make the stored contents of the storage unit available. The information processing device 100 can make the stored contents of the storage unit available for reference by a user.

[0222] According to the information processing device 100, for a plurality of hypotheses indicating the same first causal relationship in different conditions included in the first condition, stored in the storage unit, it is possible to calculate the proportion of a union obtained by combining different conditions for the first condition. According to the information processing device 100, it is possible to determine whether the calculated proportion is equal to or greater than a threshold. According to the information processing device 100, if the proportion is equal to or greater than a threshold, it is possible to add to the storage unit a first hypothesis that is different from the plurality of hypotheses and that unconditionally indicates the first causal relationship within the first condition. This allows the information processing device 100 to appropriately update the storage unit.

[0223] According to the information processing device 100, if the ratio is equal to or greater than a threshold value and a second hypothesis that is different from the multiple hypotheses and that provisionally indicates the first causal relationship under the first condition under undetermined conditions is stored in the storage unit, the second hypothesis can be deleted from the storage unit. This allows the information processing device 100 to suppress an increase in the amount of data in the storage unit.

[0224] According to the information processing device 100, for a plurality of hypotheses showing the same first causal relationship under different conditions stored in the storage unit, the ratio of a union obtained by combining the different conditions to a universal set including the different conditions can be calculated. According to the information processing device 100, it is possible to determine whether the calculated ratio is equal to or greater than a threshold. According to the information processing device 100, if the ratio is equal to or greater than the threshold and a hypothesis showing the first causal relationship under undetermined conditions that is different from the plurality of hypotheses is stored in the storage unit, the hypothesis can be deleted from the storage unit. This allows the information processing device 100 to suppress an increase in the amount of data in the storage unit.

[0225] The information processing method described in this embodiment can be realized by executing a prepared program on a computer such as a PC or a workstation. The information processing program described in this embodiment is recorded on a computer-readable recording medium, and is executed by being read from the recording medium by the computer. The recording medium may be a hard disk, a flexible disk, a CD (Compact Disc)-ROM, an MO (Magneto Optical disc), a DVD (Digital Versatile Disc), or the like. The information processing program described in this embodiment may also be distributed via a network such as the Internet.

[0226] The following supplementary notes are further disclosed regarding the above-described embodiment.

[0227] (Supplementary Note 1) For a plurality of hypotheses indicating the same first causal relationship under different conditions, which are stored in a storage unit that stores hypotheses indicating causal relationships between elements, it is determined whether or not a ratio of a union of the different conditions to a universal set including the different conditions is equal to or greater than a threshold value; If the ratio is equal to or greater than the threshold, a first hypothesis that is different from the plurality of hypotheses and that unconditionally indicates the first causal relationship within the universal set is added to the storage unit. An information processing program that causes a computer to execute a process.

[0228] (Note 2) If the ratio is equal to or greater than the threshold, the plurality of hypotheses are deleted from the storage unit. 2. The information processing program according to claim 1, characterized in that the information processing program causes the computer to execute a process.

[0229] (Note 3) When the ratio is equal to or greater than the threshold, if a second hypothesis that is different from the plurality of hypotheses and indicates the first causal relationship under undetermined conditions is stored in the storage unit, the second hypothesis is deleted from the storage unit. 3. The information processing program according to claim 2, characterized in that the information processing program causes the computer to execute a process.

[0230] (Appendix 4) When receiving an input of a third hypothesis that is different from the plurality of hypotheses and indicates the first causal relationship under new conditions, if the first hypothesis is not stored in the storage unit, the input third hypothesis is added to the storage unit, and if the first hypothesis is stored in the storage unit, the input third hypothesis is discarded. 4. The information processing program according to any one of claims 1 to 3, which causes the computer to execute a process.

[0231] (Appendix 5) Each of the different conditions is expressed by a combination of one or more elements, calculating a ratio of the number of elements used in the union to the number of elements that can be used to express a condition for the plurality of hypotheses stored in the storage unit; causing the computer to execute a process; The process of determining includes: 4. The information processing program according to claim 1, further comprising determining whether the calculated ratio is equal to or greater than the threshold value.

[0232] (Appendix 6) Output the contents stored in the storage unit. 4. The information processing program according to any one of claims 1 to 3, which causes the computer to execute a process.

[0233] (Appendix 7) The multiple hypotheses indicate the same first causal relationship under different conditions that are included in the first condition, The process of determining includes: determining whether or not a ratio of a union of the first condition and the different conditions is equal to or greater than a threshold value for the plurality of hypotheses stored in the storage unit; The process to be added is: The information processing program according to claim 1 or 2, characterized in that, when the ratio is equal to or greater than the threshold, a first hypothesis that is different from the multiple hypotheses and that unconditionally indicates the first causal relationship within the first condition is added to the memory unit.

[0234] (Appendix 8) When the ratio is equal to or greater than the threshold, if a second hypothesis that is different from the plurality of hypotheses and that provisionally indicates the first causal relationship under the first condition under undetermined conditions is stored in the storage unit, the second hypothesis is deleted from the storage unit. 8. The information processing program according to claim 7, characterized in that the information processing program causes the computer to execute a process.

[0235] (Appendix 9) For a plurality of hypotheses indicating the same first causal relationship under different conditions, which are stored in a storage unit that stores hypotheses indicating causal relationships between elements, it is determined whether or not a ratio of a union of the different conditions to a universal set including the different conditions is equal to or greater than a threshold value; If the ratio is equal to or greater than the threshold, a first hypothesis that is different from the plurality of hypotheses and that unconditionally indicates the first causal relationship within the universal set is added to the storage unit. An information processing method characterized in that the processing is executed by a computer.

[0236] (Appendix 10) For a plurality of hypotheses indicating the same first causal relationship under different conditions, which are stored in a storage unit that stores hypotheses indicating causal relationships between elements, it is determined whether or not a ratio of a union of the different conditions to a universal set including the different conditions is equal to or greater than a threshold value; If the ratio is equal to or greater than the threshold, a first hypothesis that is different from the plurality of hypotheses and that unconditionally indicates the first causal relationship within the universal set is added to the storage unit. An information processing device comprising a control unit.

[0237] (Appendix 11) For a plurality of hypotheses indicating the same first causal relationship under different conditions, which are stored in a storage unit that stores hypotheses indicating causal relationships between elements, it is determined whether or not a ratio of a union of the different conditions to a universal set including the different conditions is equal to or greater than a threshold value; When the ratio is equal to or greater than the threshold, if a hypothesis that is different from the plurality of hypotheses and indicates the first causal relationship under undetermined conditions is stored in the storage unit, the hypothesis is deleted from the storage unit. An information processing program that causes a computer to execute a process.

[0238] (Appendix 12) For a plurality of hypotheses indicating the same first causal relationship under different conditions, which are stored in a storage unit that stores hypotheses indicating causal relationships between elements, it is determined whether or not a ratio of a union of the different conditions to a universal set including the different conditions is equal to or greater than a threshold value; When the ratio is equal to or greater than the threshold, if a hypothesis that is different from the plurality of hypotheses and indicates the first causal relationship under undetermined conditions is stored in the storage unit, the hypothesis is deleted from the storage unit. An information processing method characterized in that the processing is executed by a computer.

[0239] (Appendix 13) For a plurality of hypotheses indicating the same first causal relationship under different conditions, which are stored in a storage unit that stores hypotheses indicating causal relationships between elements, it is determined whether or not a ratio of a union of the different conditions to a universal set including the different conditions is equal to or greater than a threshold value; When the ratio is equal to or greater than the threshold, if a hypothesis that is different from the plurality of hypotheses and indicates the first causal relationship under undetermined conditions is stored in the storage unit, the hypothesis is deleted from the storage unit. An information processing device comprising a control unit. [Explanation of symbols]

[0240] 100 Information processing device 101,201,311,600 Storage section 300 Information Processing Systems 301 Knowledge Learning Device 302 Client device 310 Network 400 Bus 401 CPU 402 Memory 403 Network Interface 404 Recording media I / F 405 Recording media 601 Acquisition Department 602 Judgment section 603 Update Department 604 Output section 701 Knowledge DB 702 Hypothesis DB 703,800,900,1000,1100,1200,1300 Extended Knowledge DB 711 Condition Ratio Calculation Section 712 Knowledge Reduction Department 713 Hypothesis Addition Section 721 Conditional Assignment Section 722 Knowledge Comparison Department

Claims

1. determining whether or not a ratio of a union of a plurality of hypotheses indicating the same first causal relationship under different conditions, each of which is stored in a storage unit that stores hypotheses indicating a causal relationship between elements, to a universal set including each of the different conditions is equal to or greater than a threshold value; If the ratio is equal to or greater than the threshold, a first hypothesis that is different from the plurality of hypotheses and that unconditionally indicates the first causal relationship within the universal set is added to the storage unit. An information processing program that causes a computer to execute a process.

2. If the ratio is equal to or greater than the threshold, the plurality of hypotheses are deleted from the storage unit.

2. The information processing program according to claim 1, which causes the computer to execute a process.

3. When the ratio is equal to or greater than the threshold, if a second hypothesis that is different from the plurality of hypotheses and indicates the first causal relationship under undetermined conditions is stored in the storage unit, the second hypothesis is deleted from the storage unit.

3. The information processing program according to claim 2, which causes the computer to execute a process.

4. when receiving an input of a third hypothesis that is different from the plurality of hypotheses and indicates the first causal relationship under new conditions, if the first hypothesis is not stored in the storage unit, adding the inputted third hypothesis to the storage unit, and if the first hypothesis is stored in the storage unit, discarding the inputted third hypothesis.

4. The information processing program according to claim 1, which causes the computer to execute a process.

5. The different conditions are expressed by a combination of one or more elements, calculating a ratio of the number of elements used in the union to the number of elements that can be used to express a condition for the plurality of hypotheses stored in the storage unit; causing the computer to execute a process; The process of determining includes:

4. The information processing program according to claim 1, further comprising: determining whether or not the calculated ratio is equal to or greater than the threshold value.

6. determining whether or not a ratio of a union of a plurality of hypotheses indicating the same first causal relationship under different conditions, each of which is stored in a storage unit that stores hypotheses indicating a causal relationship between elements, to a universal set including each of the different conditions is equal to or greater than a threshold value; If the ratio is equal to or greater than the threshold, a first hypothesis that is different from the plurality of hypotheses and that unconditionally indicates the first causal relationship within the universal set is added to the storage unit. An information processing method characterized in that the processing is executed by a computer.

7. determining whether or not a ratio of a union of a plurality of hypotheses indicating the same first causal relationship under different conditions, each of which is stored in a storage unit that stores hypotheses indicating a causal relationship between elements, to a universal set including each of the different conditions is equal to or greater than a threshold value; If the ratio is equal to or greater than the threshold, a first hypothesis that is different from the plurality of hypotheses and that unconditionally indicates the first causal relationship within the universal set is added to the storage unit. An information processing device comprising a control unit.

8. determining whether or not a ratio of a union of a plurality of hypotheses indicating the same first causal relationship under different conditions, each of which is stored in a storage unit that stores hypotheses indicating a causal relationship between elements, to a universal set including each of the different conditions is equal to or greater than a threshold value; When the ratio is equal to or greater than the threshold, if a hypothesis that is different from the plurality of hypotheses and indicates the first causal relationship under undetermined conditions is stored in the storage unit, the hypothesis is deleted from the storage unit. An information processing program that causes a computer to execute a process.

9. determining whether or not a ratio of a union of a plurality of hypotheses indicating the same first causal relationship under different conditions, each of which is stored in a storage unit that stores hypotheses indicating a causal relationship between elements, to a universal set including each of the different conditions is equal to or greater than a threshold value; When the ratio is equal to or greater than the threshold, if a hypothesis that is different from the plurality of hypotheses and indicates the first causal relationship under undetermined conditions is stored in the storage unit, the hypothesis is deleted from the storage unit. An information processing method characterized in that the processing is executed by a computer.

10. determining whether or not a ratio of a union of a plurality of hypotheses indicating the same first causal relationship under different conditions, each of which is stored in a storage unit that stores hypotheses indicating a causal relationship between elements, to a universal set including each of the different conditions is equal to or greater than a threshold value; When the ratio is equal to or greater than the threshold, if a hypothesis that is different from the plurality of hypotheses and indicates the first causal relationship under undetermined conditions is stored in the storage unit, the hypothesis is deleted from the storage unit. An information processing device comprising a control unit.