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 apparatus.

Background Art

[0002] Conventionally, there may be a case where it is desired to add a new hypothesis indicating a causal relationship between elements that are not given conditions and whose conditions are uncertain to an existing knowledge DB (DataBase) that stores hypotheses. For example, it is conceivable to add a new hypothesis indicating a causal relationship between elements under specific conditions, estimated by AI (Artificial Intelligence), to an existing knowledge DB.

[0003] As a prior art, for example, there is a method of changing a prototype of knowledge stored in a knowledge base so that it fits the conditions and adding it to the knowledge base as new knowledge.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] However, in the prior art, it may be difficult to appropriately update an existing knowledge DB. For example, if the format of a hypothesis that can be stored in an existing knowledge DB is different from that of a new hypothesis estimated by AI, it is difficult to add the new hypothesis estimated by AI to the existing knowledge DB. Also, for example, it is unclear how to integrate and store a hypothesis indicating a causal relationship between elements with uncertain conditions stored in an 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 aims to enable the storage unit to be appropriately updated. [Means for solving the problem]

[0007] According to one embodiment, an information processing program, information processing method, and information processing device are proposed that, for multiple hypotheses showing the same first causal relationship under different conditions, stored in a memory unit that stores hypotheses showing causal relationships between elements, determine whether the ratio of the union of the combined sets of each different condition to the whole set encompassing each of the different conditions is greater than or equal to a threshold, and if the ratio is greater than or equal to the threshold, add to the memory unit a first hypothesis that is different from the multiple hypotheses and unconditionally shows the first causal relationship within the whole set.

[0008] According to one embodiment, an information processing program, information processing method, and information processing device are proposed that determine whether the ratio of a union of multiple hypotheses, each showing the same first causal relationship under different conditions, stored in a storage unit that stores hypotheses showing causal relationships between elements, to the whole set that includes each of the different conditions, is greater than or equal to a threshold, and if the ratio is greater than or equal to the threshold, and if the storage unit also stores a hypothesis that shows the first causal relationship under conditions that is different from the multiple hypotheses, then delete that hypothesis from the storage unit. [Effects of the Invention]

[0009] According to one embodiment, the memory unit can be updated appropriately. [Brief explanation of the drawing]

[0010] [Figure 1] Figure 1 is an explanatory diagram showing one embodiment of the information processing method according to the embodiment. [Figure 2] Figure 2 is an explanatory diagram showing another embodiment of the information processing method according to the embodiment. [Figure 3] Figure 3 is an explanatory diagram showing an example of the information processing system 300. [Figure 4] FIG. 4 is a block diagram showing an example of the hardware configuration of the information processing apparatus 100. [Figure 5] FIG. 5 is an explanatory diagram showing an example of the stored content of the extended knowledge DB. [Figure 6] FIG. 6 is a block diagram showing an example of the functional configuration of the information processing apparatus 100. [Figure 7] FIG. 7 is a block diagram showing an example of the functional configuration of the information processing system 300. [Figure 8] FIG. 8 is an explanatory diagram showing Operation Example 1 of the information processing apparatus 100. [Figure 9] FIG. 9 is an explanatory diagram showing Operation Example 2 of the information processing apparatus 100. [Figure 10] FIG. 10 is an explanatory diagram showing Operation Example 3 of the information processing apparatus 100. [Figure 11] FIG. 11 is an explanatory diagram showing Operation Example 4 of the information processing apparatus 100. [Figure 12] FIG. 12 is an explanatory diagram showing Operation Example 5 of the information processing apparatus 100. [Figure 13] FIG. 13 is an explanatory diagram showing Operation Example 6 of the information processing apparatus 100. [Figure 14] FIG. 14 is a flowchart showing an example of the first additional processing procedure. [Figure 15] FIG. 15 is a flowchart showing an example of the first reduction processing procedure. [Figure 16] FIG. 16 is a flowchart showing an example of the second additional processing procedure. <000008,FIG. 17 is a flowchart showing an example of the second reduction processing procedure.

Embodiments for Carrying Out the Invention

[0011] Hereinafter, embodiments of an information processing program, an information processing method, and an information processing apparatus according to the present invention will be described in detail with reference to the drawings.

[0012] (An Example of the Information Processing Method According to the Embodiment) FIG. 1 is an explanatory diagram showing an example of an information processing method according to an embodiment. The information processing apparatus 100 is a computer for appropriately updating a storage unit. The information processing apparatus 100 is, for example, a server or a PC (Personal Computer).

[0013] The storage unit stores, for example, a hypothesis indicating a causal relationship between elements. The storage unit is, for example, an existing knowledge DB that stores a hypothesis indicating a causal relationship with uncertain conditions, estimated without considering conditions. The conditions are represented by a combination of one or more elements. The hypothesis indicates, for example, a causal relationship between an element indicating a gene and an element indicating a disease. The hypothesis indicates, for example, a causal relationship between an element indicating a material and an element indicating a physical property.

[0014] The hypothesis indicates, for example, a causal relationship that if element A holds, then element B holds. In the following description, the causal relationship that if element A holds, then element B holds may be denoted as "A→B". The hypothesis may indicate, for example, a causal relationship that if element A does not hold, then element B holds, or a causal relationship that if element A holds, then element B does not hold.

[0015] Here, there may be a case where it is desired to add a new hypothesis to the existing knowledge DB. For example, it is conceivable that an AI successively estimates new hypotheses indicating causal relationships between elements under certain conditions. In this case, each time a new hypothesis is estimated by the AI, it is conceivable to add the new hypothesis to the existing knowledge DB.

[0016] Traditionally, it can be difficult to properly update existing knowledge databases. For example, if the format of hypotheses that can be stored in an existing knowledge database differs from that of new hypotheses estimated by AI, it becomes difficult to add the new hypotheses estimated by AI to the existing knowledge database. Specifically, an existing knowledge database may include fields for setting causal relationships but not fields for setting conditions, and may only store hypotheses that show causal relationships between elements where the conditions are not yet determined. In this case, it is not possible to directly store new hypotheses estimated by AI that show causal relationships between elements under specific conditions in the existing knowledge database.

[0017] In response to this, one approach is to extend existing knowledge databases to store new hypotheses estimated by AI. For example, one could add a field for setting conditions to an existing knowledge database. In this case, for hypotheses already stored in the existing knowledge database, the field for setting conditions would be set to the character Φ, indicating that the conditions are not yet determined. Then, new hypotheses indicating causal relationships between elements under specific conditions, estimated by AI, would be added to the extended knowledge database.

[0018] However, it remains unclear how to integrate and store hypotheses showing causal relationships with uncertain conditions stored in the existing knowledge database, and new hypotheses showing the same causal relationships under specific conditions, which are estimated by AI. Consequently, it can be difficult to properly update the knowledge database. For example, it can be difficult to add new hypotheses estimated by AI to the existing knowledge database, taking into account the relationship between hypotheses already stored in the existing knowledge database and new hypotheses estimated by AI. Furthermore, there is the problem of difficulty in controlling the increasing amount of data in the existing knowledge database.

[0019] Therefore, this embodiment describes an information processing method that enables the storage unit to be appropriately updated.

[0020] In Figure 1, the information processing device 100 has a storage unit 101. The storage unit 101 stores hypotheses that show causal relationships between elements. The storage unit 101 may store, for example, multiple hypotheses that show the same first causal relationship under different conditions. The conditions are expressed, for example, by a combination of one or more elements. The multiple elements that can be used to express causal relationships and the multiple elements that can be used to express conditions may include the same elements.

[0021] (1-1) The information processing device 100 calculates the ratio of the union of multiple hypotheses stored in the memory unit 101 that show the same first causal relationship under different conditions, to the total set that includes each of the different conditions. The information processing device 100 calculates, for example, the ratio of the number of elements used in the union that includes each of the different conditions to the number of elements that can be used to express the conditions. The information processing device 100 determines whether the calculated ratio is greater than or equal to a threshold. The threshold is set in advance by the user, for example. This provides guidance on how to preferably update the contents of the memory unit 101.

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

[0023] The information processing device 100 can determine that the probability of the first causal relationship being established unconditionally is relatively small until the union of the combined different conditions becomes larger than a certain size, and can appropriately manage hypotheses indicating the first causal relationship for each individual condition by storing them in the memory unit 101. On the other hand, once the union of the combined different conditions becomes larger than a certain size, the information processing device 100 can determine that the probability of the first causal relationship being established unconditionally is relatively large, and can appropriately manage first hypotheses indicating the first causal relationship unconditionally by adding them to the memory unit 101.

[0024] In this way, the information processing device 100 can appropriately manage hypotheses indicating the first causal relationship in the memory unit 101 according to the probability that the first causal relationship is unconditionally established, and can appropriately update the memory unit 101.

[0025] Here, we have described the case where the information processing device 100 has a storage unit 101, but it is not limited to this. For example, another computer other than the information processing device 100 may have a storage unit 101. For example, the information processing device 100 may be able to access a storage unit 101 owned by another computer.

[0026] This explanation describes the case where the information processing device 100 operates independently, but it is not limited to this. For example, the information processing device 100 may collaborate with other computers. For example, multiple computers may implement the functions of the information processing device 100. Specifically, the functions of the information processing device 100 may be implemented on the cloud.

[0027] (Another embodiment of the information processing method according to the embodiment) Next, another embodiment of the information processing method according to the embodiment will be described using Figure 2. Figure 2 is an explanatory diagram showing another embodiment of the information processing method according to the embodiment.

[0028] In Figure 2, the information processing device 100 has a storage unit 201. The storage unit 201 stores hypotheses that show causal relationships between elements. The storage unit 201 may also store a second hypothesis that shows a first causal relationship under uncertain conditions. Undetermined conditions are represented, for example, by Φ. The storage unit 201 may also store, for example, multiple hypotheses that show the same first causal relationship under different conditions. Conditions are represented, for example, by a combination of one or more elements. Multiple elements that can be used to represent causal relationships and multiple elements that can be used to represent conditions may include the same elements.

[0029] (2-1) The information processing device 100 calculates the ratio of the union of multiple hypotheses stored in the memory unit 201 that show the same first causal relationship under different conditions, to the total set that includes each of the different conditions. The information processing device 100 calculates, for example, the ratio of the number of elements used in the union that includes each of the different conditions to the number of elements that can be used to express the 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 the user, for example. This provides guidance on how to preferably update the contents of the memory unit 201.

[0030] (2-2) If the calculated ratio is above a threshold, the information processing device 100 deletes the second hypothesis from the storage unit 201 if the storage unit 201 contains a second hypothesis that is different from the multiple hypotheses and indicates a first causal relationship with undetermined conditions. This allows the information processing device 100 to appropriately update the storage unit 201. For example, the information processing device 100 can delete the second hypothesis and suppress the increase in the amount of data in the storage unit 201.

[0031] The information processing device 100 can, until the union of the combined different conditions becomes larger than a certain size, provisionally determine that the first causal relationship is unconditionally established, even though the conditions are not yet determined, and can store the second hypothesis in the memory unit 201. Therefore, the information processing device 100 can appropriately manage the memory unit 201 to respect the second hypothesis obtained from existing research, etc.

[0032] On the other hand, the information processing device 100 can determine that if the union of the combined different conditions is larger than a certain size, the first causal relationship holds under a certain range of conditions. The second hypothesis may not consider whether the first causal relationship holds under other conditions not included in the union of the combined different conditions. The information processing device 100 can delete the second hypothesis from the memory unit 201, taking into account the possibility that previous research may have generated the second hypothesis without considering whether the first causal relationship holds under other conditions.

[0033] Therefore, the information processing device 100 can appropriately update the memory unit 201 so that it can distinguish between conditions under which the first causal relationship is confirmed to exist and conditions under which the first causal relationship is not yet confirmed. The information processing device 100 can determine which conditions under which it is preferable to study whether the first causal relationship exists, which can be useful for future research.

[0034] In this way, the information processing device 100 can appropriately manage a second hypothesis that shows a first causal relationship under uncertain conditions, and multiple hypotheses that show 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 memory unit 201.

[0035] Here, we have described the case where the information processing device 100 has a storage unit 201, but it is not limited to this. For example, another computer other than the information processing device 100 may have a storage unit 201. For example, the information processing device 100 may be able to access a storage unit 201 owned by another computer.

[0036] This explanation describes the case where the information processing device 100 operates independently, but it is not limited to this. For example, the information processing device 100 may collaborate with other computers. For example, multiple computers may implement the functions of the information processing device 100. Specifically, the functions of the information processing device 100 may be implemented on the cloud.

[0037] Here, the information processing device 100 may have both the function to implement the above-described embodiment and the function to implement the above-described alternative embodiment. The information processing device 100 may have only one of the functions, either the function to implement the above-described embodiment or the function to implement the above-described alternative embodiment. In the following description, we will mainly describe the case in which the information processing device 100 has both the function to implement the above-described embodiment and the function to implement the above-described alternative embodiment.

[0038] (An example of information processing system 300) Next, using Figure 3, we will describe an example of an information processing system 300 that applies the information processing device 100 shown in Figure 1 or Figure 2.

[0039] Figure 3 is an explanatory diagram showing an example of an information processing system 300. In Figure 3, the information processing system 300 includes an 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 LAN (Local Area Network), a WAN (Wide Area Network), or the Internet. In the information processing system 300, the information processing device 100 and the client device 302 are connected via a wired or wireless network 310.

[0041] The information processing device 100 is a computer that manages the memory unit 311. The memory unit 311 stores hypotheses that show causal relationships between elements. The memory unit 311 may also store hypotheses that show a first causal relationship under conditions that are not yet determined. The memory unit 311 may, for example, store multiple hypotheses that show the same first causal relationship under different conditions. The conditions are expressed, for example, by a combination of one or more elements. The multiple elements that can be used to express causal relationships and the multiple elements that can be used to express conditions may include the same elements.

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

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

[0044] The information processing device 100 calculates the proportion of the union of multiple hypotheses stored in the memory unit 311 that show the same first causal relationship under different conditions, relative to the total set that includes each of the different conditions. The information processing device 100 determines whether the calculated proportion is greater than or equal to a threshold. The threshold is set in advance by the user, for example. Based on the result of the determination, the information processing device 100 updates the memory unit 311.

[0045] The information processing device 100 receives inquiries about the contents of the memory unit 311. The information processing device 100 receives inquiries about the contents of the memory unit 311 from, for example, a knowledge learning device 301 or a client device 302. In response to the inquiry, the information processing device 100 transmits the contents of the memory unit 311 to the knowledge learning device 301 or the client device 302. The information processing device 100 is used, for example, by 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 new hypotheses that show causal relationships between elements. The knowledge learning device 301 learns new hypotheses that show causal relationships between elements, for example, using AI. Specifically, the knowledge learning device 301 may send a query for the contents of the memory unit 311 to the information processing device 100 and receive the contents of the memory unit 311 from the information processing device 100. Specifically, the knowledge learning device 301 may learn new hypotheses that show causal relationships between elements by referring to the contents of the memory unit 311 using AI. The knowledge learning device 301 sends the learned new hypotheses 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 the system user's input, the client device 302 sends a query to the information processing device 100 regarding the contents of the memory unit 311. The client device 302 receives the contents of the memory unit 311 from the information processing device 100. The client device 302 outputs the contents of the memory unit 311 so that the system user can refer to them. The client device 302 may be, for example, a PC, a tablet terminal, or a smartphone.

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

[0049] This explanation describes a case where the information processing device 100 is a different device from the client device 302, but it is not limited to this case. For example, the information processing device 100 may have the functionality of a client device 302 and may operate as a client device 302.

[0050] (Examples of using information processing system 300) Next, an example of the use of the information processing system 300 shown in Figure 3 will be described. For example, the information processing system 300 can be applied to the field of materials research. Specifically, the information processing system 300 can be applied to the field of searching for catalysts to produce materials with desirable physical properties. The memory unit 311 stores, for example, hypotheses showing the causal relationship between the catalyst and the physical properties. This makes it easier for the information processing system 300 to search for catalysts to produce materials with desirable 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 particular gene and a disease. The memory unit 311 stores, for example, hypotheses that show a causal relationship between a gene and a disease. This makes it easier for the information processing system 300 to verify the relationship between a particular 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 using Figure 4.

[0053] Figure 4 is a block diagram showing an example of the hardware configuration of the information processing device 100. In Figure 4, the information processing device 100 includes a CPU (Central Processing Unit) 401, memory 402, network interface 403, recording medium interface 404, and recording medium 405. Each component is connected by a bus 400.

[0054] Here, the CPU 401 is responsible for the overall control of the information processing device 100. The memory 402 includes, for example, ROM (Read Only Memory), RAM (Random Access Memory), and flash ROM. Specifically, for example, flash ROM and ROM store various programs, and RAM is used as the work area for the CPU 401. Programs stored in memory 402 are loaded into the CPU 401, causing the CPU 401 to execute the coded processes.

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

[0056] The recording medium interface (I / F) 404 controls the reading and writing of data to the recording medium 405 according to the control of the CPU 401. The recording medium interface (I / F) 404 is, for example, a disk drive, an SSD (Solid State Drive), or a USB (Universal Serial Bus) port. The recording medium 405 is a non-volatile memory that stores the data written under the control of the recording medium interface (I / F) 404. The recording medium 405 is, for example, a disk, semiconductor memory, or USB memory. The recording medium 405 may be detachable from the information processing device 100.

[0057] In addition to the components described above, the information processing device 100 may also have, for example, a keyboard, mouse, display, printer, scanner, microphone, speaker, etc. Furthermore, the information processing device 100 may have multiple recording medium interfaces 404 and recording mediums 405. Alternatively, the information processing device 100 may not have recording medium interfaces 404 and recording mediums 405.

[0058] (Contents of the Extended Knowledge DB500) Next, an example of the contents of the extended knowledge DB 500 will be explained using Figure 5. The extended knowledge DB 500 is implemented, for example, by storage areas such as the memory 402 and recording medium 405 of the information processing device 100 shown in Figure 4.

[0059] Figure 5 is an explanatory diagram showing an example of the contents stored in the Extended Knowledge DB 500. As shown in Figure 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] The condition field contains the conditions that form the hypothesis. For example, the condition field may contain conditions expressed by a combination of one or more elements. For example, the condition field may contain Φ to indicate an undetermined condition. For example, the condition field may contain Ω to indicate no conditions. The causal relationship field contains the causal relationships that form the hypothesis in the above conditions.

[0061] (Example of hardware configuration for knowledge learning device 301) The hardware configuration example of the knowledge learning device 301 is specifically the same as the hardware configuration example of the information processing device 100 shown in Figure 4, so a detailed explanation will be omitted.

[0062] (Example hardware configuration of client device 302) The hardware configuration example of the client device 302 is specifically the same as the hardware configuration example of the information processing device 100 shown in Figure 4, so a detailed explanation will be omitted.

[0063] (Example of the functional configuration of the information processing device 100) Next, an example of the functional configuration of the information processing device 100 will be described using Figure 6.

[0064] Figure 6 is a block diagram showing an example of the 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 implemented by a storage area such as the memory 402 or recording medium 405 shown in Figure 4. The following description will focus on the case where the storage unit 600 is included in the information processing device 100, but is not limited to this case. For example, the storage unit 600 may be included in a device different from the information processing device 100, and the contents of the storage unit 600 may be accessible from the information processing device 100.

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

[0067] The memory unit 600 stores various information that is referenced or updated during the processing of each functional unit. The memory unit 600 stores hypotheses that show causal relationships between elements. The memory unit 600 stores, for example, multiple hypotheses that show the same causal relationship under different conditions. The conditions are expressed, for example, by a combination of one or more elements. The elements may be, for example, values.

[0068] The memory unit 600 may store, for example, hypotheses that show a causal relationship under uncertain conditions. Uncertain conditions may be provisionally treated as unconditional. Unconditional means, for example, encompassing all conditions. An unconditional causal relationship is, for example, a causal relationship that holds true under all conditions. The memory unit 600 may store, for example, hypotheses that show a causal relationship unconditionally. Specifically, the memory unit 600 stores the extended knowledge DB 500.

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

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

[0071] The acquisition unit 601 acquires, for example, an output request for the contents stored in the storage unit 600. Specifically, the acquisition unit 601 acquires the output request by receiving the output request input based on the operation input of the user of its own device. Specifically, the acquisition 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 to initiate processing in any of the functional units. A start trigger may be, for example, a predetermined operation input by a user of the device. A start trigger may also be, for example, the reception of predetermined information from another computer. A start trigger may also be, for example, the output of predetermined information by any of the functional units. A start trigger may also be, for example, the detection of a signal output by a timer at regular intervals.

[0073] The acquisition unit 601 may, for example, accept the acquisition of a new hypothesis as a start trigger to initiate processing by the determination unit 602 and the update unit 603. The acquisition unit 601 may, for example, accept the acquisition of an output request as a start trigger to initiate processing by the output unit 604.

[0074] The determination unit 602 calculates the ratio of the union of multiple hypotheses stored in the memory unit 600 that show the same first causal relationship under different conditions, to the total set that includes each of the different conditions. The total set includes, for example, all conditions. The ratio can serve as a guideline for updating the memory unit 600. The ratio can serve as a guideline for evaluating whether the first causal relationship is unconditionally valid or not. The ratio can serve as a guideline for evaluating whether a certain number of conditions for the first causal relationship to be valid have been identified or not.

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

[0076] Here, the condition may indicate a range of values. In this case, the determination unit 602 may, for example, calculate the ratio of the range of values ​​of the union to the total range of possible values ​​for multiple hypotheses that indicate the same first causal relationship under different conditions, which are stored in the memory unit 600. This allows the determination unit 602 to obtain a guideline for updating the memory unit 600. The determination unit 602 may, for example, obtain a guideline for evaluating whether the first causal relationship is unconditionally valid. The determination unit 602 may, for example, obtain a guideline for evaluating whether a certain number of conditions for the first causal relationship to be valid have been identified.

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

[0078] The update unit 603 updates the contents of the memory unit 600. For example, if the determination unit 602 determines that the ratio is above a threshold, the update unit 603 unconditionally adds a first hypothesis indicating a first causal relationship to the memory unit 600. The first hypothesis is a different hypothesis from multiple hypotheses that indicate the same first causal relationship under different conditions. As a result, the update unit 603 can add a first hypothesis indicating a first causal relationship unconditionally to the memory unit 600 when the probability of the first causal relationship being unconditionally established is relatively high. The update unit 603 can then appropriately update the memory unit 600 so that its contents represent the known conditions under which the first causal relationship is established.

[0079] For example, if the determination unit 602 determines that the ratio is above a threshold, the update unit 603 deletes multiple hypotheses from the memory unit 600 that show the same first causal relationship under different conditions. Specifically, instead of adding a first hypothesis to the memory unit 600, the update unit 603 deletes multiple hypotheses from the memory unit 600 that show the same first causal relationship under different conditions. For example, if the determination unit 602 determines that the ratio is below a threshold, the update unit 603 leaves multiple hypotheses from the memory unit 600 that show the same first causal relationship under different conditions.

[0080] As a result, the update unit 603 can determine that multiple hypotheses showing the same first causal relationship under different conditions are included in the first hypothesis, and therefore do not need to be stored in the memory unit 600. For this reason, the update unit 603 can delete multiple hypotheses showing the same first causal relationship under different conditions from the memory unit 600, thereby suppressing an increase in the amount of data in the memory unit 600. The update unit 603 can keep multiple hypotheses showing the same first causal relationship under different conditions in the memory unit 600 until the first hypothesis is added to the memory unit 600, and can control the memory unit 600 to respect existing research.

[0081] For example, if the determination unit 602 determines that the ratio is above a threshold, the update unit 603 deletes the second hypothesis, which indicates a first causal relationship under undetermined conditions, from the memory unit 600 if such a hypothesis is stored in the memory unit 600. The second hypothesis is a different hypothesis from multiple hypotheses that indicate the same first causal relationship under different conditions. Specifically, instead of adding the first hypothesis to the memory unit 600, the update unit 603 deletes the second hypothesis, which indicates a first causal relationship under undetermined conditions, from the memory unit 600. For example, if the determination unit 602 determines that the ratio is below a threshold, the update unit 603 leaves the second hypothesis, which indicates a first causal relationship under undetermined conditions, in the memory unit 600.

[0082] As a result, the update unit 603 can determine that the second hypothesis, which indicates a first causal relationship under uncertain conditions, is included in the first hypothesis, and therefore does not need to be stored in the memory unit 600. For this reason, the update unit 603 can delete the second hypothesis, which indicates a first causal relationship under uncertain conditions, from the memory unit 600, thereby suppressing an increase in the amount of data in the memory unit 600. The update unit 603 can keep the second hypothesis, which indicates a first causal relationship under uncertain conditions, in the memory unit 600 until the first hypothesis is added to the memory unit 600, and can control the memory unit 600 to respect existing research.

[0083] When the update unit 603 obtains a third hypothesis from the acquisition unit 601 that indicates a first causal relationship under new conditions, it adds the input third hypothesis to the memory unit 600, or to the memory unit 600 if the first hypothesis is not already stored there. The third hypothesis is a hypothesis that is not yet stored in the memory unit 600 and is a different hypothesis from multiple hypotheses that indicate the same first causal relationship under different conditions. This allows the update unit 603 to add new hypotheses to the memory unit 600 and to update the memory unit 600 appropriately.

[0084] When the update unit 603 obtains a third hypothesis from the acquisition unit 601 that indicates a first causal relationship under new conditions, it discards the input third hypothesis if the first hypothesis is already stored in the storage unit 600. This allows the update unit 603 to avoid adding the third hypothesis to the storage unit 600 if the first hypothesis, which includes the third hypothesis, is already stored in the storage unit 600, thereby suppressing an increase in the amount of data in the storage unit 600. Furthermore, the update unit 603 ensures that the storage unit 600 does not store the first hypothesis and the third hypothesis, which is included by the first hypothesis, simultaneously. Consequently, the update unit 603 can format the contents of the storage unit 600 in a way that is easily intuitive for system users to understand.

[0085] The output unit 604 outputs the processing result of at least one of the functional units. The output format can be, for example, display on a screen, print to a printer, transmit to an external device via the network interface 403, or store in a storage area such as the memory 402 or recording medium 405. This allows the output unit 604 to notify the user of the device of the processing result of at least one of the functional units, thereby improving the usability of the information processing device 100.

[0086] The output unit 604 outputs, for example, the contents of the storage unit 600. Specifically, the output unit 604 outputs the contents of the storage unit 600 so that system users can refer to them, in response to the acquisition unit 601 having received an output request. More specifically, the output unit 604 transmits the contents of the storage unit 600 to the client device 302, which is the source of the output request. This makes the contents of the storage unit 600 accessible to system users.

[0087] Here, we have described a case in which the determination unit 602 calculates the proportion of the union of multiple hypotheses that show the same first causal relationship under different conditions included in the universal set, relative to the universal set, but it is not limited to this. 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 the union of multiple hypotheses stored in the memory unit 600 that represent the same first causal relationship in different conditions included in the first condition, by combining each of these different conditions with respect to the first condition. The determination unit 602 then determines whether the calculated proportion is equal to or greater than a threshold.

[0089] This allows the determination unit 602 to determine how the update unit 603 should update the memory unit 600. For example, if the percentage is above a threshold, the determination unit 602 can determine that there is a relatively high probability that the first causal relationship is unconditionally established within the first conditions. For example, if the percentage is above a threshold, the determination unit 602 can determine that a certain number of individual conditions within the first conditions for the first causal relationship to be established have been identified.

[0090] Specifically, if the calculated percentage is above a threshold, the update unit 603 adds a first hypothesis to the memory unit 600 that is different from multiple hypotheses and unconditionally indicates a first causal relationship within the first condition. As a result, the update unit 603 can add a first hypothesis that unconditionally indicates a first causal relationship within the first condition to the memory unit 600 when the probability of the first causal relationship being unconditionally established within the first condition is relatively high. The update unit 603 can then appropriately update the memory unit 600 so that its contents represent the known conditions under which the first causal relationship is established.

[0091] Specifically, if the update unit 603 determines that the calculated ratio is above a threshold, it deletes from the memory unit 600 multiple hypotheses that indicate the same first causal relationship in different conditions included in the first condition. More specifically, instead of adding the first hypothesis to the memory unit 600, the update unit 603 deletes from the memory unit 600 multiple hypotheses that indicate the same first causal relationship in different conditions included in the first condition. Specifically, if the update unit 603 determines that the ratio is below a threshold, it leaves in the memory unit 600 multiple hypotheses that indicate the same first causal relationship in different conditions included in the first condition.

[0092] As a result, the update unit 603 can determine that multiple hypotheses showing the same first causal relationship in different conditions included in the first condition are included in the first hypothesis, and therefore do not need to be stored in the memory unit 600. For this reason, the update unit 603 can delete multiple hypotheses showing the same first causal relationship in different conditions included in the first condition from the memory unit 600, thereby suppressing an increase in the amount of data in the memory unit 600. The update unit 603 can keep multiple hypotheses showing the same first causal relationship in different conditions included in the first condition in the memory unit 600 until the first hypothesis is added to the memory unit 600, and can control the memory unit 600 to respect existing research.

[0093] Specifically, if the calculated ratio is above a threshold, the update unit 603 deletes the second hypothesis from the memory unit 600 if the memory unit 600 has a second hypothesis stored in it that is different from multiple hypotheses and provisionally indicates a first causal relationship in the first condition under undetermined conditions. More specifically, instead of adding the first hypothesis to the memory unit 600, the update unit 603 deletes the second hypothesis from the memory unit 600. Specifically, if the ratio is below a threshold, the update unit 603 leaves the second hypothesis in the memory unit 600.

[0094] As a result, the update unit 603 can determine that the second hypothesis is included in the first hypothesis, and therefore does not need to be stored in the memory unit 600. Consequently, the update unit 603 can delete the second hypothesis from the memory unit 600, thereby suppressing an increase in the amount of data in the memory unit 600. The update unit 603 can keep the second hypothesis in the memory unit 600 until the first hypothesis is added to the memory unit 600, and can control the memory unit 600 to respect existing research.

[0095] (Example of the functional configuration of information processing system 300) Next, an example of the functional configuration of the information processing system 300 will be explained using Figure 7.

[0096] Figure 7 is a block diagram showing an example of the 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 has access to the knowledge database 701 and the hypothesis database 702. The knowledge learning device 301, for example, has the knowledge database 701. The information processing device 100 has access to the hypothesis database 702 and the extended knowledge database 703. The extended knowledge database 703 corresponds to, for example, the extended knowledge database 500. The information processing device 100, for example, has the extended knowledge database 703.

[0098] Knowledge DB701 stores hypotheses obtained from existing research. For example, Knowledge DB701 stores hypotheses that show causal relationships between elements under uncertain conditions. Hypothesis DB702 stores new hypotheses generated by the knowledge learning device 301. For example, Hypothesis DB702 stores new conditional hypotheses generated by the knowledge learning device 301. Specifically, Hypothesis DB702 stores new hypotheses that show causal relationships between elements under certain conditions.

[0099] The extended knowledge DB703 stores conditional hypotheses. The extended knowledge DB703 stores hypotheses that show causal relationships between elements under certain conditions. The extended knowledge DB703 may also store hypotheses that correspond to hypotheses stored in knowledge DB701. Specifically, the extended knowledge DB703 may store hypotheses that associate the symbol Φ, which indicates an undetermined condition, with the causal relationships shown by hypotheses stored in knowledge DB701.

[0100] For example, the information processing device 100 could generate a hypothesis that associates the symbol Φ, which indicates an undetermined condition, with the causal relationship shown by the hypothesis stored in the knowledge DB 701, and add it to the extended knowledge DB 703.

[0101] The condition assignment unit 721 refers to the extended knowledge DB 703 to identify the conditions to be verified and outputs them to the knowledge comparison unit 722. The knowledge comparison unit 722 accepts the conditions to be verified and uses AI to generate new hypotheses that show the causal relationships between elements in the conditions. The knowledge comparison unit 722 adds the generated new hypotheses to the hypothesis DB 702.

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

[0103] The knowledge reduction unit 712 updates the contents of the extended knowledge database 703. The knowledge reduction unit 712 determines whether the calculated condition ratio is equal to or greater than a threshold for each causal relationship. The threshold is set in advance by the user, for example. The knowledge reduction unit 712 updates the contents of the extended knowledge database 703 based on whether the calculated condition ratio is equal to or greater than a threshold for each causal relationship.

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

[0105] As a result, the knowledge abbreviation unit 712 can add a new hypothesis that unconditionally indicates the first causal relationship to the abbreviated knowledge DB 703 when the probability of the first causal relationship being unconditionally true is relatively high. The knowledge abbreviation unit 712 can then appropriately update the contents of the abbreviated knowledge DB 703 so that the contents of the abbreviated knowledge DB 703 represent the known conditions under which the first causal relationship is true.

[0106] For example, if the calculated condition ratio for the first causal relationship is greater than or equal to a threshold, the knowledge reduction unit 712 will delete one or more hypotheses that have been extracted from the extended knowledge DB 703 that indicate the first causal relationship, instead of adding a new hypothesis that indicates the first causal relationship unconditionally to the extended knowledge DB 703. For example, if the calculated condition ratio for the first causal relationship is less than a threshold, the knowledge reduction unit 712 will not delete one or more hypotheses that have been extracted from the extended knowledge DB 703 that indicate the first causal relationship.

[0107] Here, one or more hypotheses that demonstrate a first causal relationship under different conditions are included in the new hypotheses added to the extended knowledge DB 703. Therefore, the knowledge reduction unit 712 can determine that it does not need to store one or more hypotheses that demonstrate a first causal relationship under different conditions in the extended knowledge DB 703. The knowledge reduction unit 712 can delete multiple hypotheses that demonstrate the same first causal relationship under different conditions from the extended knowledge DB 703, thereby suppressing the increase in the amount of data in the extended knowledge DB 703.

[0108] For example, if the calculated condition ratio for the first causal relationship is above a threshold, the knowledge reduction unit 712 will delete existing hypotheses indicating the first causal relationship with undetermined conditions from the extended knowledge DB 703 if such hypotheses exist in the extended knowledge DB 703. For example, if the calculated condition ratio for the first causal relationship is below a threshold, the knowledge reduction unit 712 will not delete existing hypotheses indicating the first causal relationship with undetermined conditions from the extended knowledge DB 703.

[0109] As a result, the knowledge reduction unit 712 can delete existing hypotheses from the extended knowledge DB 703 that indicate a first causal relationship but whose conditions are not yet determined, thereby suppressing the increase in the amount of data in the extended knowledge DB 703. The knowledge reduction unit 712 can appropriately update the contents of the extended knowledge DB 703 so that the contents of the extended knowledge DB 703 represent the known conditions under which the first causal relationship is established.

[0110] Here, we have described a case where the knowledge reduction unit 712 unconditionally adds a new hypothesis indicating the first causal relationship to the extended knowledge DB 703 if the calculated condition ratio for the first causal relationship is above a threshold, but it is not limited to this case. For example, the knowledge reduction unit 712 may not unconditionally add a new hypothesis indicating the first causal relationship to the extended knowledge DB 703 if the calculated condition ratio for the first causal relationship is above a threshold.

[0111] The hypothesis addition 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 not included in the hypotheses stored in the extended knowledge DB 703, the hypothesis addition 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 addition unit 713 does not extract the new hypothesis added to the hypothesis DB 702.

[0112] (Example of operation of the information processing device 100) Next, we will explain examples of the operation of the information processing device 100 using Figures 8 to 13. First, we will explain example 1 of the operation of the information processing device 100 using Figure 8.

[0113] Figure 8 is an explanatory diagram showing an example of operation of the information processing device 100. In Figure 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" that shows a causal relationship "A→B" under condition X, a hypothesis "Y:A→B" that shows a causal relationship "A→B" under condition Y, and a hypothesis "Z:A→B" that shows a causal relationship "A→B" under condition Z.

[0114] The Extended Knowledge DB800 may store, for example, a hypothesis "Φ: A → B" that indicates a causal relationship "A → B" even though the conditions are undetermined. Φ indicates undetermined conditions. Specifically, Φ is treated as a symbol that provisionally represents the conditions of the universal set Ω, even though the conditions are undetermined. Ω includes, for example, conditions X, Y, and Z. Specifically, Ω includes all conditions.

[0115] For example, if the information processing device 100 generates a hypothesis that associates the symbol Φ, which indicates an undetermined condition, with a causal relationship shown by a hypothesis stored in an existing knowledge database, and adds it to the extended knowledge database 800, then the extended knowledge database 800 may store the hypothesis "Φ: A → B". The extended knowledge database 800 corresponds to, for example, the extended knowledge database 500.

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

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

[0118] The information processing device 100 determines whether the calculated condition ratio is greater than or equal to a threshold. The threshold is set in advance by the user, for example. The threshold is, for example, 0 < ε ≤ 1. If the calculated condition ratio is greater than or equal to the threshold, the information processing device 100 adds the hypothesis "Ω: A → B" which unconditionally indicates a causal relationship "A → B" to the extended knowledge DB 800. Ω represents the determination that the causal relationship "A → B" is true under any of the conditions.

[0119] As a result, the information processing device 100 can add a hypothesis "Ω:A→B" that unconditionally indicates a causal relationship "A→B" to the extended knowledge DB 800 when the probability of the causal relationship "A→B" being unconditionally true is relatively high. The information processing device 100 can appropriately update the contents of the extended knowledge DB 800 so that the contents of the extended knowledge DB 800 represent the known conditions under which the causal relationship "A→B" is true.

[0120] If the calculated condition ratio is greater than or equal to a threshold, the information processing device 100 deletes one or more hypotheses that it has read from the extended knowledge DB 800. For example, if the calculated condition ratio is greater than or equal to a threshold, the information processing device 100 deletes 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 hypotheses "X:A→B", "Y:A→B", and "Z:A→B" are all included in hypothesis "Ω:A→B", and therefore do not need to be stored in the extended knowledge DB 800. The information processing device 100 can then delete hypotheses "X:A→B", "Y:A→B", and "Z:A→B" from the extended knowledge DB 800, thereby suppressing the increase in the amount of data in the extended knowledge DB 800.

[0122] For example, if the calculated condition ratio is above a threshold, the information processing device 100 deletes the hypothesis "Φ:A→B" which indicates a causal relationship "A→B" with an undetermined condition from the extended knowledge DB 800. This allows the information processing device 100 to suppress the increase in the amount of data in the extended knowledge DB 800. The information processing device 100 can appropriately update the contents of the extended knowledge DB 800 so that the contents of the extended knowledge DB 800 represent that the conditions for the causal relationship "A→B" to hold are not undetermined.

[0123] Subsequently, the information processing device 100 acquires a new hypothesis "P:A→B" that indicates a causal relationship "A→B" under condition P. Since the new hypothesis "P:A→B" is encompassed by 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] Thus, since Ω indicates that the causal relationship "A→B" is established under all conditions, 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 the increase in the amount of data in the extended knowledge DB 800.

[0125] The information processing device 100 can prevent users from accessing both the hypothesis "Ω: A → B" and the hypothesis "P: A → B" when they refer to the contents of the extended knowledge DB 800. Therefore, the information processing device 100 can prevent users from misunderstanding the conditions under which the causal relationship "A → B" is established.

[0126] The information processing device 100 can prevent, for example, a user from becoming confused about which hypothesis, "Ω:A→B" or "P:A→B," is appropriate. The information processing device 100 can also prevent, for example, a user from overlooking hypothesis "Ω:A→B" and only referring to hypothesis "P:A→B," thereby misunderstanding the conditions under which the causal relationship "A→B" is established.

[0127] Here, we will explain an example of calculating the condition ratio. For example, there exists a set Σ of all elements that represent a condition. The set Σ is a set of all elements used in hypotheses in the extended knowledge DB800 extracted without duplication. Here, we assume that Σ = {A, B, C, D, E}. A condition is expressed as 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, consider the case where the conditions X = A ∪ B and Y = C ∪ D. 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, consider the case where the conditions X = A ∩ C, Y = B ∪ C, and Z = D. 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. The information processing device 100 does not need to consider instances of observed data related to the hypothesis when calculating Rate. Next, using Figure 9, we will explain example 2 of the operation of the information processing device 100.

[0130] Figure 9 is an explanatory diagram showing example 2 of the operation of the information processing device 100. In Figure 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" that shows a causal relationship "A→B" under condition X, a hypothesis "Y:A→B" that shows a causal relationship "A→B" under condition Y, and a hypothesis "Z:A→B" that shows a causal relationship "A→B" under condition Z.

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

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

[0133] The information processing device 100 determines whether the calculated condition ratio is greater than or equal to a threshold. The threshold is set in advance by the user, for example. The threshold is, for example, 0 < ε ≤ 1. If the calculated condition ratio is greater than or equal to the threshold, the information processing device 100 adds the hypothesis "Ω: A → B" which unconditionally indicates a causal relationship "A → B" to the extended knowledge DB 900. Ω represents the determination that the causal relationship "A → B" is true under all conditions.

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

[0135] If the calculated condition ratio is greater than or equal to a threshold, the information processing device 100 deletes one or more hypotheses that it has read from the extended knowledge DB 900. For example, if the calculated condition ratio is greater than or equal to a threshold, the information processing device 100 deletes 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 hypotheses "X:A→B", "Y:A→B", and "Z:A→B" are all included in hypothesis "Ω:A→B", and therefore do not need to be stored in the extended knowledge DB 900. The information processing device 100 can then delete hypotheses "X:A→B", "Y:A→B", and "Z:A→B" from the extended knowledge DB 900, thereby suppressing the increase in the amount of data in the extended knowledge DB 900.

[0137] Subsequently, 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 encompassed by 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] Thus, since Ω indicates that the causal relationship "A→B" is established under all conditions, 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 the increase in the amount of data in the extended knowledge DB 900.

[0139] The information processing device 100 can prevent users from accessing both the hypothesis "Ω: A → B" and the hypothesis "P: A → B" when they refer to the contents of the extended knowledge DB 900. Therefore, the information processing device 100 can prevent users from misunderstanding the conditions under which the causal relationship "A → B" is established.

[0140] The information processing device 100 can, for example, prevent users from becoming confused about which hypothesis is appropriate, hypothesis "Ω:A→B" or hypothesis "P:A→B". The information processing device 100 can, for example, prevent users from overlooking hypothesis "Ω:A→B" and only referring to hypothesis "P:A→B", thereby misunderstanding the conditions under which the causal relationship "A→B" is established. Next, using Figure 10, an example of the operation of the information processing device 100, example 3, will be explained.

[0141] Figure 10 is an explanatory diagram showing example 3 of the operation of the information processing device 100. In Figure 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" that shows a causal relationship "A→B" under condition X, a hypothesis "Y:A→B" that shows a causal relationship "A→B" under condition Y, and a hypothesis "Z:A→B" that shows a causal relationship "A→B" under condition Z.

[0142] The Extended Knowledge DB1000 may store, for example, a hypothesis "Φ: A → B" that indicates a causal relationship "A → B" even though the conditions are undetermined. Φ indicates undetermined conditions. Specifically, Φ is treated as a symbol that provisionally represents the conditions of the universal set Ω, even though the conditions are undetermined. Ω includes, for example, conditions X, Y, and Z. Specifically, Ω includes all conditions.

[0143] For example, if the information processing device 100 generates a hypothesis that associates the symbol Φ, which indicates an undetermined condition, with a causal relationship shown by a hypothesis stored in the existing knowledge database, and adds it to the extended knowledge database 1000, the extended knowledge database 1000 may store the hypothesis "Φ: A → B".

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

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

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

[0147] The information processing device 100 can appropriately update the contents of the extended knowledge database 1000 so that the contents of the extended knowledge database 1000 can distinguish between conditions under which the causal relationship "A→B" is established and conditions under which it is not yet established. The information processing device 100 can determine which conditions under which the causal relationship "A→B" is established is preferable to study, thereby contributing to future research.

[0148] The information processing device 100 can, provisionally, determine that the causal relationship "A→B" is unconditionally established, even though the conditions are not yet determined, until the calculated condition ratio exceeds a threshold, and can store 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 to respect the hypothesis "Φ:A→B" obtained from existing research, etc.

[0149] Subsequently, the information processing device 100 acquires a new hypothesis, "P:A→B," which indicates a causal relationship "A→B" under condition P. Since the new hypothesis "P:A→B" is not included in the hypotheses stored in the extended knowledge DB 1000, the information processing device 100 adds the new hypothesis "P:A→B" to the extended knowledge DB 1000. This allows the information processing device 100 to appropriately update the extended knowledge DB 1000.

[0150] As described above, the information processing device 100 can use the symbol Φ to indicate undetermined conditions to consistently import hypotheses stored in the existing knowledge database into the extended knowledge database. When a relatively large number of hypotheses showing the same causal relationship under different conditions are accumulated in the extended knowledge database, the information processing device 100 can use the symbol Ω to represent the whole set to integrate these hypotheses, thereby suppressing the increase in the amount of data in the extended knowledge database.

[0151] The information processing device 100 can delete hypotheses that use the symbol Φ representing the whole set when a relatively large number of hypotheses showing the same causal relationship under different conditions have accumulated in the extended knowledge database, thereby suppressing the increase in the amount of data in the extended knowledge database. 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 extended knowledge database update policy.

[0152] In the examples in Figures 8 and 9, we described a case in which the information processing device 100 integrates multiple hypotheses using conditions X, Y, and Z included in the universal set Ω into a hypothesis using the universal set Ω that encompasses all conditions. However, the device is not limited to this case. For example, the information processing device 100 may integrate multiple hypotheses using conditions X1, X2, and X3 included in condition XΩ into a hypothesis using condition XΩ. This case will be described in detail later using Figures 11 and 12.

[0153] Similarly, the example in Figure 10 describes a case in which the information processing device 100 determines whether or not to delete a hypothesis using the undetermined symbol Φ that provisionally represents the conditions of the universal set Ω, depending on the number of confirmed conditional hypotheses. However, it is not limited to this case. For example, the information processing device 100 may determine whether or not to delete a hypothesis using the undetermined symbol XΦ that provisionally represents the condition XΩ, depending on the number of confirmed conditional hypotheses included in the condition XΩ. This case will be described in detail later using Figure 13. Next, an example of operation 4 of the information processing device 100 will be described using Figure 11.

[0154] Figure 11 is an explanatory diagram showing example 4 of the operation of the information processing device 100. In Figure 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" that shows a causal relationship "A→B" in condition X1, a hypothesis "X2:A→B" that shows a causal relationship "A→B" in condition X2, and a hypothesis "X3:A→B" that shows a causal relationship "A→B" in condition X3.

[0155] The extended knowledge DB1100 may, for example, store a hypothesis "XΦ: A → B" that provisionally indicates a causal relationship "A → B" under condition XΩ, even when the condition is not yet determined. XΦ indicates that the condition is not yet determined. Specifically, XΦ is treated as a symbol that provisionally indicates the condition of condition XΩ, even though the condition is not yet determined. XΩ may, for example, encompass conditions X1, X2, and X3. XΩ may also encompass other conditions Xi, for example, i = 4, 5, ...

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

[0157] The information processing device 100 reads one or more hypotheses stored in the extended knowledge DB 1100 that show a causal relationship "A→B" under different conditions encompassed by condition XΩ. For example, the information processing device 100 reads multiple hypotheses including hypothesis "X1: A→B" showing a causal relationship "A→B", hypothesis "X2: A→B" showing a causal relationship "A→B", and hypothesis "X3: A→B" showing a causal relationship "A→B".

[0158] The information processing device 100 calculates the condition ratio for the multiple hypotheses it has read. For example, the information processing device 100 calculates the condition ratio as the ratio of the union of conditions X1, X2, and X3 to condition XΩ, which encompasses condition X1, X2, and X3. Specifically, the information processing device 100 calculates the condition ratio as the ratio of the number of elements representing conditions included in the union to the total number of elements representing conditions included in condition XΩ.

[0159] The information processing device 100 determines whether the calculated condition ratio is greater than or equal to a threshold. The threshold is set in advance by the user, for example. The threshold is, for example, 0 < ε ≤ 1. If the calculated condition ratio is greater than or equal to the threshold, the information processing device 100 determines that the causal relationship "A → B" is established under condition XΩ, and adds the hypothesis "XΩ: A → B", which unconditionally indicates the causal relationship "A → B" within condition XΩ, to the extended knowledge DB 1100. XΩ represents the determination that the causal relationship "A → B" is established under any of the conditions within condition XΩ.

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

[0161] If the calculated condition ratio is greater than or equal to a threshold, the information processing device 100 deletes one or more hypotheses that it has read from the extended knowledge DB 1100. For example, if the calculated condition ratio is greater than or equal to a threshold, the information processing device 100 deletes 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 hypotheses "X1:A→B", "X2:A→B", and "X3:A→B" are all included in the hypothesis "XΩ:A→B", and therefore do not need to be stored in the extended knowledge DB 1100. The information processing device 100 can then delete hypotheses "X1:A→B", "X2:A→B", and "X3:A→B" from the extended knowledge DB 1100, thereby suppressing the increase in the amount of data in the extended knowledge DB 1100.

[0163] For example, if the calculated condition ratio is above a threshold, the information processing device 100 deletes the hypothesis "XΦ:A→B" from the extended knowledge DB 1100, which provisionally indicates a causal relationship "A→B" in condition XΩ when the condition is not yet determined. This allows the information processing device 100 to suppress the increase in the amount of data in the extended knowledge DB 1100. The information processing device 100 can appropriately update the contents of the extended knowledge DB 1100 so that the contents of the extended knowledge DB 1100 represent that the conditions for the causal relationship "A→B" to hold are not uncertain.

[0164] Subsequently, the information processing device 100 assumes it has acquired a new hypothesis, "XP:A→B," which indicates a causal relationship "A→B" in condition XP. The information processing device 100 discards the new hypothesis "XP:A→B" without adding it to the extended knowledge DB 1100, because the new hypothesis "XP:A→B" is encompassed by the hypothesis "XΩ:A→B."

[0165] Thus, since XΩ indicates that the causal relationship "A→B" is determined to be true under any of the conditions within condition XΩ, the information processing device 100 does not need to add the conditional causal relationship "A→B" contained within condition XΩ to the extended knowledge DB 1100 thereafter. This allows the information processing device 100 to suppress the increase in the amount of data in the extended knowledge DB 1100.

[0166] The information processing device 100 can prevent users from accessing both the hypothesis "XΩ: A → B" and the hypothesis "XP: A → B" when they refer to the contents of the extended knowledge DB 1100. Therefore, the information processing device 100 can prevent users from misunderstanding the conditions under which the causal relationship "A → B" is established.

[0167] The information processing device 100 can, for example, prevent users from becoming confused about which hypothesis is appropriate, "XΩ:A→B" or "XP:A→B". The information processing device 100 can, for example, prevent users from overlooking hypothesis "XΩ:A→B" and only referring to hypothesis "XP:A→B", thereby misunderstanding the conditions under which the causal relationship "A→B" is established. Next, an example of the operation of the information processing device 100, example 5, will be explained using Figure 12.

[0168] Figure 12 is an explanatory diagram showing example 5 of the operation of the information processing device 100. In Figure 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" that shows a causal relationship "A→B" in condition X1, a hypothesis "X2:A→B" that shows a causal relationship "A→B" in condition X2, and a hypothesis "X3:A→B" that shows a causal relationship "A→B" in condition X3.

[0169] The information processing device 100 reads one or more hypotheses stored in the extended knowledge DB 1200 that show a causal relationship "A→B" under different conditions encompassed by condition XΩ. For example, the information processing device 100 reads multiple hypotheses including hypothesis "X1: A→B" showing a causal relationship "A→B", hypothesis "X2: A→B" showing a causal relationship "A→B", and hypothesis "X3: A→B" showing a causal relationship "A→B".

[0170] The information processing device 100 calculates the condition ratio for the multiple hypotheses it has read. For example, the information processing device 100 calculates the condition ratio as the ratio of the union of conditions X1, X2, and X3 to condition XΩ, which encompasses condition X1, X2, and X3. Specifically, the information processing device 100 calculates the condition ratio as the ratio of the number of elements representing conditions included in the union to the total number of elements representing conditions included in condition XΩ.

[0171] The information processing device 100 determines whether the calculated condition ratio is greater than or equal to a threshold. The threshold is set in advance by the user, for example. The threshold is, for example, 0 < ε ≤ 1. If the information processing device 100 determines that the calculated condition ratio is greater than or equal to the threshold, it adds the hypothesis "XΩ: A → B" which unconditionally indicates a causal relationship "A → B" within condition XΩ to the extended knowledge DB 1200. XΩ represents the determination that the causal relationship "A → B" is established under any of the conditions within condition XΩ.

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

[0173] If the calculated condition ratio is above a threshold, the information processing device 100 deletes one or more hypotheses that it has read from the extended knowledge DB 1200. For example, if the calculated condition ratio is above a threshold, the information processing device 100 deletes 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 hypotheses "X1:A→B", "X2:A→B", and "X3:A→B" are all included in the hypothesis "XΩ:A→B", and therefore do not need to be stored in the extended knowledge DB 1200. The information processing device 100 can then delete hypotheses "X1:A→B", "X2:A→B", and "X3:A→B" from the extended knowledge DB 1200, thereby suppressing the increase in the amount of data in the extended knowledge DB 1200.

[0175] Subsequently, the information processing device 100 assumes it has acquired a new hypothesis, "XP:A→B," which indicates a causal relationship "A→B" in condition XP. The information processing device 100 discards the new hypothesis "XP:A→B" without adding it to the extended knowledge DB 1200, because the new hypothesis "XP:A→B" is encompassed by the hypothesis "XΩ:A→B."

[0176] Thus, since XΩ indicates that the causal relationship "A→B" is established under all conditions, 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 the increase in the amount of data in the extended knowledge DB 1200.

[0177] The information processing device 100 can prevent users from accessing both the hypothesis "XΩ: A → B" and the hypothesis "XP: A → B" when they refer to the contents of the extended knowledge DB 1200. Therefore, the information processing device 100 can prevent users from misunderstanding the conditions under which the causal relationship "A → B" is established.

[0178] The information processing device 100 can, for example, prevent users from becoming confused about which hypothesis is appropriate, "XΩ:A→B" or "XP:A→B". The information processing device 100 can, for example, prevent users from overlooking hypothesis "XΩ:A→B" and only referring to hypothesis "XP:A→B", thereby misunderstanding the conditions under which the causal relationship "A→B" is established. Next, an example of the operation 6 of the information processing device 100 will be explained using Figure 13.

[0179] Figure 13 is an explanatory diagram showing example 6 of the operation of the information processing device 100. In Figure 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" that shows a causal relationship "A→B" in condition X1, a hypothesis "X2:A→B" that shows a causal relationship "A→B" in condition X2, and a hypothesis "X3:A→B" that shows a causal relationship "A→B" in condition X3.

[0180] The extended knowledge DB1300 may, for example, store a hypothesis "XΦ: A → B" that provisionally indicates a causal relationship "A → B" under condition XΩ, even when the condition is not yet determined. XΦ indicates that the condition is not yet determined. Specifically, XΦ is treated as a symbol that provisionally indicates the condition of condition XΩ, even though the condition is not yet determined. XΩ may, for example, encompass conditions X1, X2, and X3. XΩ may also encompass other conditions Xi, for example, i = 4, 5, ...

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

[0182] The information processing device 100 reads one or more hypotheses stored in the extended knowledge DB 1300 that show a causal relationship "A→B" under different conditions encompassed by condition XΩ. For example, the information processing device 100 reads multiple hypotheses including hypothesis "X1: A→B" showing a causal relationship "A→B", hypothesis "X2: A→B" showing a causal relationship "A→B", and hypothesis "X3: A→B" showing a causal relationship "A→B".

[0183] The information processing device 100 calculates the condition ratio for the multiple hypotheses it has read. For example, the information processing device 100 calculates the condition ratio as the ratio of the union of conditions X1, X2, and X3 to condition XΩ, which encompasses condition X1, X2, and X3. Specifically, the information processing device 100 calculates the condition ratio as the ratio of the number of elements representing conditions included in the union to the total number of elements representing conditions included in condition XΩ.

[0184] The information processing device 100 determines whether the calculated condition ratio is greater than or equal to a threshold. The threshold is set in advance by the user, for example. The threshold is, for example, 0 < ε ≤ 1. If, for example, the calculated condition ratio is greater than or equal to the threshold, the information processing device 100 deletes the hypothesis "XΦ: A → B" from the extended knowledge DB 1300, which indicates a causal relationship "A → B" in condition XΩ when the condition is not yet determined. This allows the information processing device 100 to suppress the increase in the amount of data in the extended knowledge DB 1300.

[0185] The information processing device 100 can appropriately update the contents of the extended knowledge DB 1300 so that the contents of the extended knowledge DB 1300 can distinguish between conditions under which the causal relationship "A→B" is established and conditions under which it is not yet established. The information processing device 100 can determine which conditions under which the causal relationship "A→B" is established is preferable to study, thereby contributing to future research.

[0186] The information processing device 100 can provisionally determine that the causal relationship "A→B" is unconditionally established within condition XΩ, even if the calculated condition ratio exceeds a threshold, and can store 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 to respect the hypothesis "XΦ:A→B" obtained from existing research, etc.

[0187] Subsequently, the information processing device 100 acquires a new hypothesis, "X4:A→B," which indicates a causal relationship "A→B" under 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 allows the information processing device 100 to appropriately update the extended knowledge DB 1300.

[0188] As described above, the information processing device 100 can use the symbol XΦ, which indicates an undetermined condition, to import hypotheses stored in the existing knowledge database into the extended knowledge database without contradiction. When the extended knowledge database has accumulated a relatively large number of hypotheses that show the same causal relationship under different conditions encompassed by condition XΩ, the information processing device 100 can integrate these hypotheses using the symbol XΩ that represents the condition. Therefore, the information processing device 100 can suppress the increase in the amount of data in the extended knowledge database.

[0189] The information processing device 100 can provisionally delete hypotheses using the symbol XΦ representing condition XΩ when a relatively large number of hypotheses showing the same causal relationship under different conditions encompassed by condition XΩ have accumulated in the extended knowledge database. Therefore, the information processing device 100 can suppress the increase in the amount of data in the extended knowledge database. The information processing device 100 may selectively perform the operations shown in Figures 11 and 12 and the operation shown in Figure 13, depending on the user's extended knowledge database update policy.

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

[0191] Figure 14 is a flowchart of an example of the first additional processing procedure. In Figure 14, the current extended knowledge database is denoted as "D". First, the information processing device 100 receives the input of the hypothesis "P: A → B" (step S1401).

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

[0193] In step S1403, the information processing device 100 generates a record for 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 completes the first addition process. This allows the information processing device 100 to properly update the extended knowledge DB.

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

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

[0196] If no record exists (step S1501: No), the information processing device 100 proceeds to step S1507. On the other hand, if a record exists (step S1501: Yes), the information processing device 100 proceeds to step S1502.

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

[0198] The information processing device 100 then determines whether the calculated condition ratio is greater than or equal to ε (step S1504). If the condition ratio is greater than or equal to ε (step S1504: Yes), the information processing device 100 proceeds to the process in step S1506. On the other hand, if the condition ratio is not greater than or equal to ε (step S1504: No), the information processing device 100 proceeds to the process in step S1505.

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

[0200] In step S1506, the information processing device 100 adds a new record to D2 that aggregates the one or more extracted records and shows the causal relationship "Ω: A → B" (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 terminates the first reduction process. This allows the information processing device 100 to suppress the increase in the amount of data in the knowledge database.

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

[0203] Figure 16 is a flowchart showing an example of the second additional processing procedure. In Figure 16, the current extended knowledge database is denoted as "D". First, the information processing device 100 receives the input of the hypothesis "P: A → B" (step S1601).

[0204] Next, the information processing device 100 determines whether at least one of the following exists in D: a record for hypothesis "P: A → B" or a record for hypothesis "Ω: A → B" (step S1602). If a record exists (step S1602: Yes), the information processing device 100 proceeds to step S1604. On the other hand, if no record exists (step S1602: No), the information processing device 100 proceeds to step S1603.

[0205] In step S1603, the information processing device 100 generates a record for 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 completes the second addition process. This allows the information processing device 100 to properly update the extended knowledge DB.

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

[0207] Figure 17 is a flowchart showing an example of the second reduction process. In Figure 17, the current extended knowledge database is referred to as "D1". The information processing device 100 prepares an empty extended knowledge database "D2".

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

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

[0210] The information processing device 100 then 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 (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 matching condition Φ exists among the one or more extracted records (step S1705). If no record exists (step S1705: No), the information processing device 100 proceeds to the process in step S1706. On the other hand, if a record exists (step S1705: Yes), the information processing device 100 proceeds to the process in step S1707.

[0212] In step S1706, the information processing device 100 adds 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 the records other than the one or more extracted records that do not meet condition Φ to D2 (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 terminates the second reduction process. This allows the information processing device 100 to suppress the increase in the amount of data in the knowledge database.

[0215] Here, the information processing device 100 may execute some steps of the flowcharts in Figures 14 to 17 in a different order. Also, the information processing device 100 may omit some steps of the flowcharts in Figures 14 to 17.

[0216] As explained above, the information processing device 100 can calculate the proportion of the union of multiple hypotheses stored in the memory unit that show the same first causal relationship under different conditions, with respect to the whole set. The information processing device 100 can determine whether the calculated proportion is above a threshold. If the proportion is above the threshold, the information processing device 100 can add a first hypothesis to the memory unit that is different from the multiple hypotheses and unconditionally shows a first causal relationship within the whole set. This allows the information processing device 100 to appropriately update the memory unit.

[0217] According to the information processing device 100, if the ratio exceeds a threshold, multiple hypotheses can be deleted from the memory unit. This allows the information processing device 100 to suppress the increase in the amount of data in the memory unit.

[0218] According to the information processing device 100, if the ratio is above a threshold, and a second hypothesis that indicates a first causal relationship with undetermined conditions, different from the multiple hypotheses, is stored in the memory, the second hypothesis can be deleted from the memory. In this way, the information processing device 100 can suppress the increase in the amount of data in the memory.

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

[0220] According to the information processing device 100, for multiple hypotheses stored in the memory unit, the ratio of the number of elements used in the union to the number of elements that can be used to express the conditions can be calculated. According to the information processing device 100, it is possible to determine whether the calculated ratio is above a threshold. In this way, the information processing device 100 can calculate the ratio with high accuracy.

[0221] According to the information processing device 100, the contents of the memory unit can be output. The information processing device 100 can make the contents of the memory unit available for use. The information processing device 100 can make the contents of the memory unit accessible to users.

[0222] According to the information processing device 100, it is possible to calculate the proportion of the union of multiple hypotheses stored in the memory that show the same first causal relationship under different conditions included in the first condition. According to the information processing device 100, it is possible to determine whether the calculated proportion is above a threshold. According to the information processing device 100, if the proportion is above a threshold, it is possible to add a first hypothesis to the memory that is different from the multiple hypotheses and unconditionally shows the first causal relationship within the first condition. This allows the information processing device 100 to appropriately update the memory.

[0223] According to the information processing device 100, if the ratio is above a threshold, and a second hypothesis that differs from the multiple hypotheses and provisionally indicates a first causal relationship in the first condition under undetermined conditions is stored in the memory, the second hypothesis can be deleted from the memory. In this way, the information processing device 100 can suppress the increase in the amount of data in the memory.

[0224] According to the information processing device 100, for multiple hypotheses stored in the memory that show the same first causal relationship under different conditions, the proportion of the union of each different condition relative to the total set that includes each different condition can be calculated. According to the information processing device 100, it can determine whether the calculated proportion is above a threshold. According to the information processing device 100, if the proportion is above a threshold, and if there is a hypothesis stored in the memory that shows a first causal relationship under undetermined conditions but is different from the multiple hypotheses, then that hypothesis can be deleted from the memory. In this way, the information processing device 100 can suppress the increase in the amount of data in the memory.

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

[0226] With regard to the embodiments described above, the following additional information is disclosed.

[0227] (Note 1) For multiple hypotheses that show the same first causal relationship under different conditions, stored in a memory unit that stores hypotheses showing causal relationships between elements, it is determined whether the ratio of the union of the combined sets of the different conditions to the whole set that includes the different conditions is greater than or equal to a threshold. If the aforementioned ratio is greater than or equal to the aforementioned threshold, a first hypothesis that is different from the aforementioned multiple hypotheses and unconditionally demonstrates the first causal relationship within the entire set is added to the memory unit. An information processing program characterized by having a computer perform the processing.

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

[0229] (Note 3) 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 memory unit, the second hypothesis is deleted from the memory unit. The information processing program described in Appendix 2, characterized in that it causes the computer to perform the processing.

[0230] (Note 4) When a third hypothesis indicating the first causal relationship under new conditions, which differs from the aforementioned multiple hypotheses, is received, if the first hypothesis is not stored in the memory unit, the input third hypothesis is added to the memory unit; if the first hypothesis is stored in the memory unit, the input third hypothesis is discarded. An information processing program according to any one of the appendices 1 to 3, characterized in that it causes the computer to perform the processing.

[0231] (Note 5) Each of the above different conditions is expressed by a combination of one or more elements, For the plurality of hypotheses stored in the memory unit, the ratio of the number of elements used in the union to the number of elements that can be used to express the conditions is calculated. The computer is made to perform the process, The process for making the determination is as follows: An information processing program according to any one of the appendices 1 to 3, characterized by determining whether the calculated ratio is equal to or greater than the threshold.

[0232] (Appendix 6) Output the contents stored in the memory unit. An information processing program according to any one of the appendices 1 to 3, characterized in that it causes the computer to perform the processing.

[0233] (Note 7) The above multiple hypotheses show the same first causal relationship in different conditions that are included in the first condition, The process for making the determination is as follows: For the plurality of hypotheses stored in the memory unit, it is determined whether the proportion of the union of the combined conditions for the first condition is greater than or equal to a threshold. The aforementioned additional processing is: The information processing program according to Appendix 1 or 2, characterized in that, if the ratio is greater than or equal to the threshold, a first hypothesis that is different from the multiple hypotheses and unconditionally shows the first causal relationship within the first condition is added to the memory unit.

[0234] (Note 8) If the ratio is equal to or greater than the threshold, and if the memory unit stores a second hypothesis that is different from the multiple hypotheses, has undetermined conditions and provisionally indicates the first causal relationship in the first condition, the second hypothesis shall be deleted from the memory unit. The information processing program described in Appendix 7, characterized in that it causes the computer to perform the processing.

[0235] (Note 9) For multiple hypotheses that show the same first causal relationship under different conditions, stored in a memory unit that stores hypotheses showing causal relationships between elements, it is determined whether the ratio of the union of the combined sets of the different conditions to the whole set that includes the different conditions is greater than or equal to a threshold. If the aforementioned ratio is greater than or equal to the aforementioned threshold, a first hypothesis that is different from the aforementioned multiple hypotheses and unconditionally demonstrates the first causal relationship within the entire set is added to the memory unit. An information processing method characterized in that the processing is performed by a computer.

[0236] (Note 10) For multiple hypotheses that show the same first causal relationship under different conditions, stored in a memory unit that stores hypotheses showing causal relationships between elements, it is determined whether the ratio of the union of the combined sets of the different conditions to the whole set that includes the different conditions is greater than or equal to a threshold. If the aforementioned ratio is greater than or equal to the aforementioned threshold, a first hypothesis that is different from the aforementioned multiple hypotheses and unconditionally demonstrates the first causal relationship within the entire set is added to the memory unit. An information processing device characterized by having a control unit.

[0237] (Note 11) For multiple hypotheses that show the same first causal relationship under different conditions, stored in a memory unit that stores hypotheses showing causal relationships between elements, it is determined whether the ratio of the union of the combined sets of the different conditions to the whole set that includes the different conditions is greater than or equal to a threshold. If the ratio is equal to or greater than the threshold, and if the memory unit stores a hypothesis that is different from the multiple hypotheses and indicates the first causal relationship with undetermined conditions, then delete that hypothesis from the memory unit. An information processing program characterized by having a computer perform the processing.

[0238] (Note 12) For multiple hypotheses that show the same first causal relationship under different conditions, stored in a memory unit that stores hypotheses showing causal relationships between elements, it is determined whether the ratio of the union of the combined sets of the different conditions to the whole set that includes the different conditions is greater than or equal to a threshold. If the ratio is equal to or greater than the threshold, and if the memory unit stores a hypothesis that is different from the multiple hypotheses and indicates the first causal relationship with undetermined conditions, then delete that hypothesis from the memory unit. An information processing method characterized in that the processing is performed by a computer.

[0239] (Note 13) For multiple hypotheses that show the same first causal relationship under different conditions, stored in a memory unit that stores hypotheses showing causal relationships between elements, it is determined whether the ratio of the union of the combined sets of the different conditions to the whole set that includes the different conditions is greater than or equal to a threshold. If the ratio is equal to or greater than the threshold, and if the memory unit stores a hypothesis that is different from the multiple hypotheses and indicates the first causal relationship with undetermined conditions, then delete that hypothesis from the memory unit. An information processing device characterized by having a control unit. [Explanation of Symbols]

[0240] 100 Information Processing Devices 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 medium interface 405 Recording media 601 Acquisition Department 602 Judgment section 603 Update Department 604 Output Section 701 Knowledge DB 702 Hypothesis Database 703,800,900,1000,1100,1200,1300 Extended Knowledge Database 711 Conditional Ratio Calculation Unit 712 Knowledge Reduction Department 713 Additional Hypothesis 721 Conditional Assignment Section 722 Knowledge Comparison Department

Claims

1. For multiple hypotheses that show the same first causal relationship under different conditions, stored in a memory unit that stores hypotheses showing causal relationships between elements, it is determined whether the ratio of the union of the combined sets of the different conditions to the total set that includes the different conditions is greater than or equal to a threshold. If the aforementioned ratio is greater than or equal to the aforementioned threshold, a first hypothesis that is different from the aforementioned multiple hypotheses and unconditionally demonstrates the first causal relationship within the entire set is added to the memory unit. An information processing program characterized by having a computer perform the processing.

2. If the ratio is greater than or equal to the threshold, the plurality of hypotheses are deleted from the storage unit. The information processing program according to claim 1, characterized in that it causes the computer to perform the processing.

3. If the ratio is greater than or equal to the threshold, and if a second hypothesis indicating the first causal relationship, which is different from the multiple hypotheses and whose conditions are not yet determined, is stored in the memory unit, the second hypothesis is deleted from the memory unit. The information processing program according to claim 2, characterized in that it causes the computer to perform the processing.

4. When a third hypothesis, which represents the first causal relationship under new conditions different from the aforementioned multiple hypotheses, is received, if the first hypothesis is not stored in the memory unit, the input third hypothesis is added to the memory unit; if the first hypothesis is stored in the memory unit, the input third hypothesis is discarded. An information processing program according to any one of claims 1 to 3, characterized in that it causes the computer to perform the processing.

5. Each of the aforementioned different conditions can be expressed by a combination of one or more elements. For the plurality of hypotheses stored in the memory unit, the ratio of the number of elements used in the union to the number of elements that can be used to express the conditions is calculated. The computer is made to perform the process, The process for making the determination is as follows: An information processing program according to any one of claims 1 to 3, characterized in that it determines whether the calculated ratio is equal to or greater than the threshold.

6. For multiple hypotheses that show the same first causal relationship under different conditions, stored in a memory unit that stores hypotheses showing causal relationships between elements, it is determined whether the ratio of the union of the combined sets of the different conditions to the total set that includes the different conditions is greater than or equal to a threshold. If the aforementioned ratio is greater than or equal to the aforementioned threshold, a first hypothesis that is different from the aforementioned multiple hypotheses and unconditionally demonstrates the first causal relationship within the entire set is added to the memory unit. An information processing method characterized in that the processing is performed by a computer.

7. For multiple hypotheses that show the same first causal relationship under different conditions, stored in a memory unit that stores hypotheses showing causal relationships between elements, it is determined whether the ratio of the union of the combined sets of the different conditions to the total set that includes the different conditions is greater than or equal to a threshold. If the aforementioned ratio is greater than or equal to the aforementioned threshold, a first hypothesis that is different from the aforementioned multiple hypotheses and unconditionally demonstrates the first causal relationship within the entire set is added to the memory unit. An information processing device characterized by having a control unit.

8. For multiple hypotheses that show the same first causal relationship under different conditions, stored in a memory unit that stores hypotheses showing causal relationships between elements, it is determined whether the ratio of the union of the combined sets of the different conditions to the total set that includes the different conditions is greater than or equal to a threshold. If the ratio is equal to or greater than the threshold, and if the memory unit stores a hypothesis that is different from the multiple hypotheses and indicates a first causal relationship with undetermined conditions, then the memory unit shall delete the hypothesis. An information processing program characterized by having a computer perform the processing.

9. For multiple hypotheses that show the same first causal relationship under different conditions, stored in a memory unit that stores hypotheses showing causal relationships between elements, it is determined whether the ratio of the union of the combined sets of the different conditions to the total set that includes the different conditions is greater than or equal to a threshold. If the ratio is equal to or greater than the threshold, and if the memory unit stores a hypothesis that is different from the multiple hypotheses and indicates a first causal relationship with undetermined conditions, then the memory unit shall delete the hypothesis. An information processing method characterized in that the processing is performed by a computer.

10. For multiple hypotheses that show the same first causal relationship under different conditions, stored in a memory unit that stores hypotheses showing causal relationships between elements, it is determined whether the ratio of the union of the combined sets of the different conditions to the total set that includes the different conditions is greater than or equal to a threshold. If the ratio is equal to or greater than the threshold, and if the memory unit stores a hypothesis that is different from the multiple hypotheses and indicates a first causal relationship with undetermined conditions, then the memory unit shall delete the hypothesis. An information processing device characterized by having a control unit.

Images