Information processing device, information processing method, and storage medium

Abstract

An information processing device 100 according to the present disclosure comprises: a generation unit 121 that generates a candidate hypothesis on the basis of an observation logical expression and the background knowledge; a determination unit 122 that calculates, on the basis of the observation logical expression and the background knowledge, an upper bound on the number of logical constraints to be satisfied by the candidate hypothesis, and determines a processing method for logical constraints at the time of execution hypothetical inference according to the calculated upper bound; and an inference unit 123 that processes the logical constraints through the determined processing method to execute the hypothetical inference for the candidate hypothesis.

Description

Information processing device, information processing method, and storage medium

[0001] The present disclosure relates to an information processing device, an information processing method, and a storage medium.

[0002] Abduction (Abductive reasoning) is a reasoning method that takes an observation formula and background knowledge, and outputs the best (solution hypothesis) among the logical formulas (hypotheses) that are consistent with the background knowledge and that deductively derive the observation formula, based on a function (evaluation function) that expresses the merits of each candidate as a real value.

[0003] An existing method for implementing abductive reasoning on a computer is to enumerate best hypothesis candidates (candidate hypotheses) from an observation logical formula and background knowledge, convert the problem of searching for the best hypothesis from there into an equivalent constrained combinatorial optimization problem such as an integer linear programming problem, and obtain the best hypothesis using an external solver, as shown in Non-Patent Document 1. In this existing method, constraints (logical constraints) that must be satisfied for each candidate hypothesis to be consistent with the background knowledge are enumerated at the same time as the candidate hypotheses are enumerated, and when an optimal solution is searched for using an external solver, these are expressed as constraints in the target constrained combinatorial optimization problem, thereby ensuring that the obtained optimal solution is consistent.

[0004] Another existing method for handling logical constraints in abductive reasoning, as described in Non-Patent Document 2, does not enumerate logical constraints when enumerating candidate hypotheses, but instead uses an external solver to obtain a tentative solution and then determines whether the solution contradicts the logical constraints. This existing method targets logical constraints related to transitive relations that hold between equivalence relations among the logical constraints. Instead, it does not enumerate logical constraints when enumerating candidate hypotheses. Instead, it uses an external solver to obtain a tentative solution. The equivalence relations that hold in the solution are then checked for consistency with the logical constraints related to transitive relations. If consistency is confirmed, the solution is output as the optimal solution. On the other hand, if a logical constraint contradicts any logical constraint, the logical constraint is added as a constraint to the constrained combinatorial problem being solved by the external solver, and a tentative solution is again obtained by the external solver. By repeating this procedure until an optimal solution is obtained, a consistent solution hypothesis can be obtained without explicitly enumerating specific types of logical constraints. This type of inference method is called Cutting Plane Inference.

[0005] Naoya Inoue and Kentaro Inui, "ILP-based Reasoning for Weighted Abduction", Proceedings of AAAI Workshop on Plan, Activity and Intent Recognition, August 2011, pp. 25-32Naoya Inoue and Kentaro Inui, "Large-scale cost-based abduction in full-fledged first-order predicate logic with cutting plane inference", Proceedings of the 13th European conference on Logics in Artificial Intelligence, September 2012, pp. 281-293

[0006] In the above-described method, Cutting Plane Inference is applied only to specific types of logical constraints that are determined in advance. Therefore, there is a problem that the calculation speed drops significantly in cases where a large number of logical constraints of types that are not subject to Cutting Plane Inference are listed. On the other hand, it is possible to apply Cutting Plane Inference to all logical constraints, but in that case, it is necessary to run the external solver multiple times to obtain a tentative solution until a consistent solution is obtained, which is also inefficient.

[0007] Therefore, an object of the present disclosure is to provide an information processing device that can solve the above-mentioned problem of reduced computational efficiency in abductive reasoning.

[0008] An information processing device according to one embodiment of the present disclosure includes: a generation unit that generates candidate hypotheses based on an observation logical formula and background knowledge; a determination unit that calculates an upper bound on the number of logical constraints that the candidate hypotheses must satisfy based on the observation logical formula and background knowledge, and determines a processing method for the logical constraints when performing abductive inference according to the calculated upper bound; and an inference unit that processes the logical constraints using the determined processing method to perform abductive inference on the candidate hypotheses.

[0009] Furthermore, an information processing method that is one form of the present disclosure has the following configuration: generate a candidate hypothesis based on an observation logical formula and background knowledge; calculate an upper bound on the number of logical constraints that the candidate hypothesis must satisfy based on the observation logical formula and background knowledge; determine a processing method for the logical constraints when performing abductive inference based on the calculated upper bound; and process the logical constraints using the determined processing method to perform abductive inference on the candidate hypothesis.

[0010] Furthermore, a program that is one aspect of the present disclosure has a configuration that causes a computer to execute the following processes: generate a candidate hypothesis based on an observation logical formula and background knowledge; calculate an upper bound on the number of logical constraints that the candidate hypothesis must satisfy based on the observation logical formula and background knowledge; determine a processing method for the logical constraints when performing abductive inference based on the calculated upper bound; and process the logical constraints using the determined processing method to perform abductive inference on the candidate hypothesis.

[0011] With the above-described configuration, the present disclosure can improve the calculation efficiency in abductive reasoning.

[0012] FIG. 2 is a block diagram showing the configuration of the information processing device disclosed in FIG. 1. FIG. 3 is a diagram showing the state of processing by the information processing device disclosed in FIG. 1. FIG. 4 is a diagram showing the state of processing by the information processing device disclosed in FIG. 1. FIG. 5 is a block diagram showing the hardware configuration of an information processing device according to a second embodiment of the present disclosure. FIG. 6 is a block diagram showing the configuration of an information processing device according to a second embodiment of the present disclosure.

[0013] First Embodiment A first embodiment of the present disclosure will be described with reference to Fig. 1 to Fig. 4. Fig. 1 is a diagram for explaining the configuration of an information processing device, and Fig. 2 to Fig. 4 are diagrams for explaining the processing operation of the information processing device.

[0014] [Configuration] The information processing device in this embodiment has a function of performing abductive reasoning to derive the best solution that is consistent with background knowledge from candidate hypotheses based on observation logical formulas. As a result, the information processing device in this embodiment can be applied to applications such as explanation generation and situation understanding using background knowledge and observation information. More specifically, the information processing device can be applied to automated systems that perform medical systems, legal consultations, risk detection, etc.

[0015] FIG. 1 illustrates the configuration and processing flow of an information processing device according to this embodiment. The information processing device is configured with one or more information processing devices each including a computing device and a storage device. As shown in FIG. 1 , the information processing device includes a candidate hypothesis generation unit 1, a constraint processing method determination unit 2, a constraint enumeration unit 3, a candidate hypothesis conversion unit 4, an optimal solution search unit 5, and a consistency determination unit 6. The functions of the candidate hypothesis generation unit 1, the constraint processing method determination unit 2, the constraint enumeration unit 3, the candidate hypothesis conversion unit 4, the optimal solution search unit 5, and the consistency determination unit 6 can be realized by the computing device executing a program for realizing each function stored in the storage device. The information processing device is configured by storing an observation logical formula D1, background knowledge D2, a candidate hypothesis set D3, a logical constraint set D4, and a solution hypothesis D5 in the storage device. Each component will be described in detail below.

[0016] The candidate hypothesis generation unit 1 (generation unit) receives as input an observation formula D1 and background knowledge D2 and outputs a candidate hypothesis set D3. The observation formula D1 is a conjunction of first-order predicate logic literals that represent observation information. A first-order predicate logic literal is either an atomic formula in first-order predicate logic or its negation. The background knowledge D2 is a set of inference rules. Inference rules are generally expressed as Horn clauses in first-order predicate logic. More specifically, starting with a candidate hypothesis consisting only of an observation formula, the system applies inference rules to the candidate hypothesis to generate a new candidate hypothesis, repeating this process until no new operations can be performed. The operations used in this process are basically those used in existing methods, but other methods can also be used. The candidate hypothesis set D3 is a set of candidate hypotheses output by the candidate hypothesis generation unit 1. Each candidate hypothesis is expressed as a conjunction of first-order predicate logic literals.

[0017] The constraint processing method determination unit 2 (determination unit) determines a processing method for the logical constraints to be satisfied by each candidate hypothesis included in the candidate hypothesis set D3. Here, the logical constraint processing methods include a first processing method in which logical constraints are listed in advance along with candidate hypotheses, as described in Non-Patent Document 1, and a second processing method in which the consistency of the logical constraints of the candidate hypotheses is determined after the fact, as described in Non-Patent Document 2. The constraint processing method determination unit 2 then calculates an upper bound on the number of logical constraints to be satisfied by the candidate hypotheses based on the observation logical formula and background knowledge, and determines which of the above processing methods to use to process the logical constraints when performing hypothetical reasoning, depending on the calculated upper bound. In this case, the constraint processing method determination unit 2 determines the first processing method if the calculated upper bound on the number of logical constraints is less than a set threshold, and determines the second processing method if the calculated upper bound on the number of logical constraints is equal to or greater than a set threshold. In other words, if the constraint processing method determination unit 2 determines that the number of logical constraints is small based on a preset criterion, it decides to adopt a first processing method that pre-lists the logical constraints, and if the number of logical constraints is determined to be large based on a preset criterion, it decides to adopt a second processing method that does not pre-list the logical constraints but makes a post-determination.

[0018] Here, the constraint processing method determination unit 2 calculates the upper bound of the number of logical constraints, for example, as follows. First, the constraint processing method determination unit 2 focuses on each first-order predicate logic literal included in the observation logical formula, and calculates an upper bound on the number of sets of logical constraints related to the first-order predicate logic literal of interest. At this time, the constraint processing method determination unit 2 calculates the upper bound based on background knowledge including the first-order predicate logic literal of interest and the first-order predicate logic literal in the observation logical formula. For example, the constraint processing method determination unit 2 identifies background knowledge including the first-order predicate logic literal of interest, and calculates the upper bound based on the number of combinations of first-order predicate logic literals in the observation logical formula that correspond to the first-order predicate logic literal included in the identified background knowledge. Details of the method by which the constraint processing method determination unit 2 calculates the upper bound on the number of logical constraints will be described later.

[0019] The constraint enumeration unit 3 (inference unit) enumerates the logical constraints that must be satisfied by each candidate hypothesis included in the candidate hypothesis set D3. That is, the constraint enumeration unit 3 enumerates the logical constraints when the constraint processing method determination unit 2 determines that the logical constraints should be processed using the first processing method. The constraint enumeration unit 3 then stores the enumerated logical constraints in a logical constraint set D4. The logical constraint set D4 is a set of logical constraints that must be satisfied by each candidate hypothesis. Each logical constraint is generally expressed as a conjunction of combinations that must not be satisfied among the first-order predicate logic literals that make up the candidate hypothesis.

[0020] The candidate hypothesis conversion unit 4 (inference unit) receives the candidate hypothesis set D3 and the constraint set D4 as input, and outputs a constrained combinatorial optimization problem equivalent to a procedure for selecting the best candidate hypothesis that satisfies all constraints from the candidate hypothesis set. The conversion procedure in this case basically uses the existing method described above, but other methods can also be used.

[0021] The optimal solution search unit 5 (inference unit) receives as input the constrained combinatorial optimization problem generated by the candidate hypothesis conversion unit 4, and searches for an optimal solution to it using an external solver. This solution is the optimal solution to the constrained combinatorial optimization problem, but since it does not take into account all logical constraints, the candidate hypothesis selected by this solution is treated as a provisional solution hypothesis for abductive reasoning.

[0022] The consistency determination unit 6 (inference unit) determines whether the provisional solution obtained by the optimal solution search unit 5 satisfies the logical constraints determined to be the second processing method by the constraint processing method determination unit 2, i.e., the logical constraints that are the subject of Cutting Plane Inference. If the provisional solution satisfies all of these logical constraints, the solution is output as a solution hypothesis D5. On the other hand, if there are any unsatisfied logical constraints, the logical constraints are added to the logical constraint set D4, and the process loops back to the optimal solution search unit 5. This loop is repeated until a solution that satisfies all logical constraints is obtained, or until all logical constraints are finally added to the logical constraint set D4. The best hypothesis D5 is the candidate hypothesis included in the candidate hypothesis set D3 that satisfies all logical constraints that should be satisfied and is the best based on the evaluation function.

[0023] [Operation] Next, the operation of the information processing device described above will be described with reference to FIG.

[0024] First, according to a method based on Non-Patent Document 1, for example, the candidate hypothesis generation unit 1 generates a new candidate hypothesis by selecting and applying one applicable operation to an initial candidate hypothesis consisting only of an observation logical formula. The operations that can be performed here are backward chaining and unification. Backward chaining is an operation that applies rules contained in background knowledge backward to an existing candidate hypothesis. It matches the consequent (right-hand side, consequence) of the rule with a literal in the candidate hypothesis, and generates a new candidate hypothesis by adding a literal corresponding to the antecedent (left-hand side, premise). Unification targets a pair of literals in an existing candidate hypothesis that have the same predicate, and generates a new candidate hypothesis by adding an equivalence relationship between the arguments so that the literals are identical. The candidate hypotheses generated by the candidate hypothesis generation unit 1 are stored in a candidate hypothesis set D3. An example of generating candidate hypotheses will be described later in the specific example with reference to Figures 2 and 3.

[0025] Next, the constraint processing method determination unit 2 determines a processing method for the logical constraints to be satisfied by each of the candidate hypotheses included in the candidate hypothesis set D3. More specifically, this process determines whether the logical constraints should be enumerated by the constraint enumeration unit 3, or whether the logical constraints should not be enumerated and should instead be subjected to Cutting Plane Inference and judged by the consistency determination unit 6. Any method may be used as the criterion for this judgment, but basically, the upper bound of the number of logical constraints that can be enumerated is evaluated and the determination is made based on whether the value exceeds some threshold. An example of the evaluation of the upper bound of logical constraints will be described later in the explanation of a specific example with reference to FIGS. 2 and 3.

[0026] Next, the constraint enumeration unit 3 enumerates the logical constraints that should be satisfied for each candidate hypothesis. This process is basically the same as the existing method described above, and is realized by searching for combinations of literals that contradict each rule contained in the background knowledge and each literal contained in the candidate hypothesis. The enumerated logical constraints are stored in a logical constraint set D4.

[0027] Next, the candidate hypothesis conversion unit 4 outputs a corresponding constrained combinatorial optimization problem from the candidate hypothesis set D3 and the logical constraint set D4. For example, according to the method based on Non-Patent Document 1, the output constrained combinatorial optimization problem is expressed as an integer linear programming problem, but it may also be expressed as something else, such as a weighted satisfiability maximization problem. The variables in the combinatorial optimization problem correspond to the presence or absence of literals in the candidate hypotheses, and the constraints in the combinatorial optimization problem are used to represent the logical constraints in the candidate hypotheses and prerequisites arising from the evaluation function. Furthermore, the objective function in the combinatorial optimization problem is designed to be equivalent to the evaluation function in abductive reasoning.

[0028] Next, the optimal solution search unit 5 searches for and outputs a tentative solution hypothesis from the candidate hypothesis set D3. This process is realized by using an external solver that corresponds to the type of problem output by the candidate hypothesis conversion unit 4. According to the method based on Non-Patent Document 1, an optimal solution is obtained using a solver for an integer linear programming problem, and the candidate hypothesis that corresponds to this optimal solution is used as a tentative solution hypothesis.

[0029] Next, the consistency determination unit 6 determines whether the provisional solution obtained by the optimum solution search unit 5 satisfies the logical constraints that were the subject of Cutting Plane Inference in the constraint processing method determination unit 2. If the provisional solution satisfies all of these logical constraints, this solution is output as the best hypothesis D5. On the other hand, if there is an unsatisfied logical constraint, that logical constraint is added to the logical constraint set D4, and the process loops back to the optimum solution search unit 5. This loop is repeated until a solution that satisfies all of the logical constraints is obtained, or until all of the logical constraints have finally been added to the logical constraint set D4.

[0030] [Specific Example] Next, the operation of the information processing device described above will be described using a specific example. Here, a case where abductive reasoning is performed on the observation logical formula and background knowledge as shown in FIG.

[0031] First, the candidate hypothesis generation unit 1 generates a set of candidate hypotheses from the observation logical formula and background knowledge. More specifically, starting with a candidate hypothesis consisting only of the observation logical formula, a set of candidate hypotheses is constructed by applying backward inference operations and unification operations as shown in Figure 3. In this way, candidate hypotheses such as those shown in Figure 3 are generated.

[0032] Next, the constraint processing method determination unit 2 determines a processing method for the logical constraints to be satisfied by each candidate hypothesis. At this time, a processing method is determined for each group of logical constraints defined by some criteria. For example, if the literals included in the candidate hypotheses are sorted by their order of appearance, and then "logical constraints that include a specific literal and are composed only of literals that appeared before that literal" are listed as a group, the logical constraints for the candidate hypotheses shown in Figure 3 are processed as groups such as those shown in Figure 4. At this time, the constraint processing method determination unit 2 roughly calculates the upper bound of the number of enumerations for each group, and determines how to handle the group of logical constraints based on this value.

[0033] For example, consider the case where a logical constraint processing method for the literal penguin(b) is determined, with an upper bound value of 8 or greater being treated as the target of Cutting Plane Inference. In this case, in the background knowledge shown in Figure 2, there are two inference rules containing the predicate penguin: penguin(x) → !fly(x) and penguin(x) ∧ swallow(x) → ⊥. Therefore, the upper bound on the number of enumerations of logical constraints is the sum of the upper bounds of literal combinations that contradict each of these inference rules. For example, the upper bound on literal combinations that contradict penguin(x) → !fly(x) can be calculated as the number of literal combinations that have the predicate penguin and the predicate fly in the literal to be considered, which is 1 × 3 = 3. Similarly, the upper bound of the combinations of literals that contradict penguin(x) ∧ swallow(x) → ⊥ is 2 × 3 = 6, since there are three literals with the predicate penguin and two with the predicate swallow. Therefore, the upper bound of the number of logical constraints related to the literal penguin(b) is 9, which exceeds the threshold of 8 set above, and therefore these logical constraints are not enumerated but are subject to Cutting Plane Inference.

[0034] Next, the constraint enumeration unit 3 enumerates specific literal combinations for the group of logical constraints that have been pre-enumerated by the constraint processing method determination unit. The logical constraints for the candidate hypotheses shown in Figure 3 are enumerated as shown in Figure 4.

[0035] The candidate hypothesis conversion unit 4 expresses as a constrained combinatorial optimization problem a procedure for searching for the best hypothesis among the candidate hypotheses that are included in the candidate hypothesis set D3 and do not satisfy the combinations included in the logical constraint set D4, and outputs the expressed procedure. According to the method of Non-Patent Document 1, the procedure for searching for the best hypothesis is expressed as an equivalent integer linear programming problem.

[0036] The optimal solution search unit 5 receives the constrained combinatorial optimization problem as an input from the candidate hypothesis conversion unit, searches for the best hypothesis, and outputs it. According to the method of Non-Patent Document 1, an integer linear programming problem is received as an input, and the best hypothesis is searched for by solving it using an external integer linear programming problem solver.

[0037] The consistency determination unit 6 determines whether the best hypothesis obtained by the optimal solution search unit 5 is consistent with the logical constraints that were the subject of Cutting Plane Inference in the constraint processing method determination unit, and if consistent, outputs the hypothesis as a solution hypothesis D5. On the other hand, if a contradictory logical constraint is found, the logical constraint is added to the logical constraint set D4, and the process is looped back to the optimal solution search unit 5. For example, consider the case where the following hypothesis is obtained as the best hypothesis for the input shown in Figure 2: bird(a) ∧ fly(a) ∧ bird(b) ∧ fly(b) ∧ penguin(c) ∧ swallow(d) ∧ swallow(a) ∧ (a = d) ∧ swallow(b) ∧ (b = d). The consistency determination unit 6 then determines whether the hypothesis is consistent with the logical constraint set (9) and (10) shown in Figure 4. First, logical constraint (9) regarding the literal penguin(b) is obviously consistent because this literal is not included in the hypothesis. Next, logical constraint (10) regarding the literal swallow(b) is verified for consistency by determining whether there is a combination of this literal with other literals included in the best hypothesis that contradicts the inference rules. In this case, the combination swallow(b) ∧ !fly(b) contradicts the inference rules, so this combination is added as a logical constraint to logical constraint set D4, and the processing is looped again to optimal solution search unit 5.

[0038] [Effects] As described above, the information processing device described above has a first effect of enabling selection of an appropriate processing method for each logical constraint depending on the situation. The reason for this is that, before enumerating each logical constraint, it is possible to determine whether or not the logical constraint should be subject to Cutting Plane Inference by evaluating the upper bound of the number to be enumerated. Empirically, the greater the number of enumerated logical constraints, the greater the benefit of Cutting Plane Inference. Therefore, by selecting targets using the upper bound of the enumeration number as a judgment criterion, it is possible to select only logical constraints that will greatly benefit from Cutting Plane Inference.

[0039] The second effect is that the inference process in abductive reasoning can be performed more efficiently than existing methods in terms of both memory and computation time. This is because, due to the first effect described above, logical constraints that are enumerated in large numbers are subject to Cutting Plane Inference, eliminating the need to explicitly enumerate the logical constraints, which would otherwise require a large amount of memory and computation time.

[0040] The third effect is that the entire procedure for hypothetical reasoning can be performed more efficiently than existing methods in terms of both memory and calculation time. This is because the second effect mentioned above reduces the memory and calculation time required to enumerate logical constraints, and the second effect also reduces the number of constraints in the constrained combinatorial optimization problem given to the solver, thereby reducing the memory and calculation time required in the procedure for searching for an optimal solution using an external solver.

[0041] <Embodiment 2> Next, a second embodiment of the present disclosure will be described with reference to Fig. 5 to Fig. 6. Fig. 5 to Fig. 6 are block diagrams showing the configuration of an information processing device in embodiment 2. Note that this embodiment shows an outline of the configuration of the information processing device described in the above embodiment.

[0042] First, the hardware configuration of the information processing device 100 in this embodiment will be described with reference to Fig. 5. The information processing device 100 is configured as a general information processing device, and is equipped with the following hardware configuration, for example: CPU (Central Processing Unit) 101 (arithmetic unit); ROM (Read Only Memory) 102 (storage device); RAM (Random Access Memory) 103 (storage device); programs 104 loaded into RAM 103; a storage device 105 that stores the programs 104; a drive device 106 that reads and writes data from and to a storage medium 110 external to the information processing device; a communication interface 107 that connects to a communication network 111 external to the information processing device; an input / output interface 108 that inputs and outputs data; and a bus 109 that connects the various components.

[0043] 5 shows an example of the hardware configuration of the information processing device 100, and the hardware configuration of the information processing device is not limited to the above-described case. For example, the information processing device may be configured with only a part of the above-described configuration, such as excluding the drive device 106. Furthermore, the information processing device may use a GPU (Graphics Processing Unit), a DSP (Digital Signal Processor), an MPU (Micro Processing Unit), an FPU (Floating Point Number Processing Unit), a PPU (Physics Processing Unit), a TPU (Tensor Processing Unit), a quantum processor, a microcontroller, or a combination thereof, instead of the above-described CPU.

[0044] The information processing device 100 can be equipped with the generation unit 121, determination unit 122, and inference unit 123 shown in FIG. 6 by having the CPU 101 acquire and execute the program group 104. The program group 104 is stored in advance in the storage device 105 or the ROM 102, for example, and is loaded into the RAM 103 and executed by the CPU 101 as needed. The program group 104 may be supplied to the CPU 101 via the communication network 111, or may be stored in advance in the storage medium 110, and the drive device 106 may read and supply the programs to the CPU 101. However, the generation unit 121, determination unit 122, and inference unit 123 described above may be constructed using dedicated electronic circuits for realizing such means.

[0045] The generation unit 121 generates candidate hypotheses based on the observation formula and background knowledge.

[0046] The determination unit 122 calculates an upper bound on the number of logical constraints that a candidate hypothesis must satisfy based on the observation logical formula and the background knowledge, and determines a processing method for the logical constraints when performing abductive reasoning based on the calculated upper bound. For example, the determination unit 122 determines, based on the calculated upper bound, one of a first processing method that lists logical constraints in advance and a second processing method that determines after the fact whether the candidate hypothesis is consistent with the logical constraints. At this time, the determination unit 122 calculates an upper bound for each predicate included in the observation logical formula based on the background knowledge including the predicate and the observation logical formula.

[0047] The inference unit 123 processes the logical constraints in the determined processing method to perform abductive inference on the candidate hypotheses.

[0048] With the above-described configuration, the present disclosure can select an appropriate processing method for each logical constraint depending on the situation when performing abductive reasoning on a candidate hypothesis, thereby improving the computational efficiency of abductive reasoning.

[0049] The above-described program can be stored and supplied to a computer using various types of non-transitory computer-readable media. Non-transitory computer-readable media include various types of tangible storage media. Examples of non-transitory computer-readable media include magnetic recording media (e.g., flexible disks, magnetic tapes, hard disk drives), magneto-optical recording media (e.g., magneto-optical disks), CD-ROMs (Read Only Memory), CD-Rs, CD-R / Ws, and semiconductor memories (e.g., mask ROMs, PROMs (Programmable ROMs), EPROMs (Erasable PROMs), flash ROMs, and RAMs (Random Access Memory)). The program may also be supplied to a computer by various types of transitory computer-readable media. Examples of transitory computer-readable media include electrical signals, optical signals, and electromagnetic waves. The transitory computer-readable media can be supplied to a computer via wired communication paths such as electric wires and optical fibers, or via wireless communication paths.

[0050] Although the present disclosure has been described above with reference to the above-described embodiments, the present disclosure is not limited to the above-described embodiments. Various modifications that are understandable to those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure. Furthermore, at least one or more of the functions of the generation unit 121, the determination unit 122, and the inference unit 123 described above may be executed by an information processing device installed and connected anywhere on a network, i.e., may be executed by so-called cloud computing.

[0051] <Supplementary Notes> Some or all of the above embodiments can also be described as in the following supplementary notes. Below, an outline of the configurations of an information processing device, an information processing method, and a program according to the present disclosure will be described. However, the present disclosure is not limited to the following configurations. (Supplementary Note 1) An information processing device comprising: a generation unit that generates candidate hypotheses based on an observation logical formula and background knowledge; a determination unit that calculates an upper bound on the number of logical constraints that the candidate hypotheses should satisfy based on the observation logical formula and background knowledge, and determines a processing method for the logical constraints when performing abductive inference according to the calculated upper bound; and an inference unit that processes the logical constraints using the determined processing method to perform abductive inference on the candidate hypotheses. (Supplementary Note 2) The information processing device according to Supplementary Note 1, wherein the determination unit determines one of a first processing method that lists the logical constraints in advance and a second processing method that ex post determines the consistency of the candidate hypotheses with respect to the logical constraints according to the calculated upper bound. (Supplementary Note 3) The information processing device according to Supplementary Note 2, wherein the determination unit determines the first processing method when the calculated value of the upper bound is less than a predetermined threshold, and determines the second processing method when the calculated value of the upper bound is equal to or greater than the threshold. (Supplementary Note 4) The information processing device according to any of Supplements 1 to 3, wherein the determination unit calculates the upper bound of the number of logical constraints for each set of logical constraints organized based on a predicate included in the observation logical formula, and determines the processing method for each set of logical constraints in accordance with the calculated number of upper bounds, and the inference unit processes the logical constraints in the set using the processing method determined for each set of logical constraints to perform hypothetical inference on the candidate hypothesis. (Supplementary Note 5) An information processing device according to any one of Supplementary Notes 1 to 4, wherein the determination unit calculates, for each predicate included in the observation logical formula, an upper bound on the number of logical constraints associated with the predicate, and determines the processing method for the logical constraints according to the calculated upper bound; and the inference unit processes the logical constraints associated with the predicate using the processing method determined for each predicate, to perform hypothetical inference on the candidate hypothesis.(Supplementary Note 6) The information processing device according to Supplementary Note 5, wherein the determination unit calculates the upper bound for each antecedent descriptive word included in the observation logical formula based on the background knowledge including the predicate and the observation logical formula. (Supplementary Note 7) The information processing device according to Supplementary Note 6, wherein the determination unit, for each antecedent descriptive word included in the observation logical formula, identifies the background knowledge including the predicate, and calculates the upper bound based on the number of combinations of antecedent descriptive words in the observation logical formula that correspond to the antecedent descriptive words included in the identified background knowledge. (Supplementary Note 8) An information processing method comprising: generating a candidate hypothesis based on the observation logical formula and the background knowledge; calculating an upper bound on the number of logical constraints that the candidate hypothesis must satisfy based on the observation logical formula and the background knowledge; determining a processing method for the logical constraints when performing abductive inference according to the calculated upper bound; and processing the logical constraints using the determined processing method to perform abductive inference on the candidate hypothesis. (Supplementary Note 9) The information processing method according to Supplementary Note 8, wherein either one of the processing method of listing the logical constraints in advance and the processing method of determining ex post whether the candidate hypothesis is consistent with the logical constraints is determined according to the calculated upper bound. (Supplementary Note 10) The information processing method according to Supplementary Note 8 or 9, wherein the upper bound on the number of logical constraints is calculated for each set of logical constraints compiled based on predicates included in the observation logical formula, the processing method is determined for each set of logical constraints according to the calculated number of upper bounds, and the logical constraints in the set are processed using the processing method determined for each set of logical constraints to perform hypothetical inference for the candidate hypothesis. (Supplementary Note 11) An information processing method according to any one of Supplementary Notes 8 to 10, comprising: calculating, for each predicate included in the observation logical formula, an upper bound on the number of logical constraints associated with the predicate; determining a processing method for the logical constraints according to the calculated upper bound; and processing the logical constraints associated with the predicate using the processing method determined for each predicate to perform hypothetical inference on the candidate hypothesis.(Supplementary Note 12) A computer-readable storage medium storing a program for causing a computer to execute the following processes: generating a candidate hypothesis based on an observation logical formula and background knowledge; calculating an upper bound on the number of logical constraints that the candidate hypothesis must satisfy based on the observation logical formula and background knowledge; determining a processing method for the logical constraints when performing abductive inference according to the calculated upper bound; and processing the logical constraints using the determined processing method to perform abductive inference on the candidate hypothesis.

[0052] REFERENCE SIGNS LIST 1 Candidate hypothesis generation unit 2 Constraint processing method determination unit 3 Constraint enumeration unit 4 Candidate hypothesis conversion unit 5 Optimal search unit 6 Consistency determination unit D1 Observation logical formula D2 Background knowledge D3 Candidate hypothesis set D4 Logical constraint set D5 Solution hypothesis 100 Information processing device 101 CPU 102 ROM 103 RAM 104 Program group 105 Storage device 106 Drive device 107 Communication interface 108 Input / output interface 109 Bus 110 Storage medium 111 Communication network 121 Generation unit 122 Determination unit 123 Inference unit

Claims

1. a generation unit that generates candidate hypotheses based on the observation formula and background knowledge; a determination unit that calculates an upper bound on the number of logical constraints that the candidate hypotheses should satisfy based on the observation logical formula and background knowledge, and determines a method for processing the logical constraints when performing abductive reasoning in accordance with the calculated upper bound; an inference unit that processes the logical constraints using the determined processing method to perform a hypothetical inference on the candidate hypotheses; An information processing device comprising:

2. 2. The information processing device according to claim 1, the determination unit determines, according to the calculated upper bound, one of a first processing method for listing the logical constraints in advance and a second processing method for determining afterward whether the candidate hypothesis is consistent with the logical constraints. Information processing device.

3. 3. The information processing device according to claim 2, the determination unit determines the first processing method when the calculated upper bound value is less than a predetermined threshold, and determines the second processing method when the calculated upper bound value is equal to or greater than the threshold. Information processing device.

4. 2. The information processing device according to claim 1, the determination unit calculates the upper bound of the number of logical constraints for each set of logical constraints organized based on a predicate included in the observation logical formula, and determines the processing method for each set of logical constraints in accordance with the calculated upper bound; the inference unit processes the logical constraints in the set using the processing method determined for each set of logical constraints, and performs abductive inference on the candidate hypotheses. Information processing device.

5. 2. The information processing device according to claim 1, the determination unit calculates, for each predicate included in the observation logical formula, an upper bound on the number of the logical constraints associated with the predicate, and determines the processing method for the logical constraints in accordance with the calculated upper bound; the inference unit processes the logical constraints related to the predicates in the processing method determined for each of the predicates, and performs a hypothetical inference on the candidate hypotheses. Information processing device.

6. 6. The information processing device according to claim 5, the determination unit calculates the upper bound for each predicate included in the observation logical formula based on the background knowledge including the predicate and the observation logical formula. Information processing device.

7. 7. The information processing device according to claim 6, the determination unit identifies the background knowledge including the predicate for each antecedent included in the observation logical formula, and calculates the upper bound based on the number of combinations of antecedent descriptive words in the observation logical formula corresponding to the antecedent descriptive words included in the identified background knowledge. Information processing device.

8. An information processing device, generating candidate hypotheses based on the observation formula and background knowledge; calculating an upper bound on the number of logical constraints that the candidate hypothesis should satisfy based on the observation logical formula and background knowledge, and determining a method for processing the logical constraints when performing abductive reasoning in accordance with the calculated upper bound; processing the logical constraints in the determined processing manner to perform abductive reasoning on the candidate hypotheses; Information processing methods.

9. 9. The information processing method according to claim 8, The information processing device, determining, according to the calculated upper bound, one of the first processing method for listing the logical constraints in advance and the second processing method for determining afterward whether the candidate hypothesis is consistent with the logical constraints; Information processing methods.

10. generating candidate hypotheses based on the observation formula and background knowledge; calculating an upper bound on the number of logical constraints that the candidate hypothesis should satisfy based on the observation logical formula and background knowledge, and determining a method for processing the logical constraints when performing abductive reasoning in accordance with the calculated upper bound; processing the logical constraints in the determined processing manner to perform abductive reasoning on the candidate hypotheses; A program that causes a computer to execute a process.