Abstract: In an inference device, an observation input means receives an observation as an input. A hypothesis candidate generation means generates hypothesis candidates by applying inference knowledge to the observation in the backward direction. The problem conversion means converts the hypothesis candidate into an ILP problem or a SAT problem. An equivalent problem generation means generates a specified number of equivalent ILP problems or equivalent SAT problems in which an order of the variables included in the converted ILP problem or SAT problem is changed. The solver parallelization means solves the generated equivalent ILP problems or equivalent SAT problems by executing a specified number of identical ILP solvers or SAT solvers in parallel. The optimal solution output means outputs a result of the ILP solver or the SAT solver that output the result first, among the specified number of ILP solvers or SAT solvers, as the optimal solution.

Abstract: An abduction apparatus 1 includes: a probability calculation unit 2 configured to, with respect to each of candidate hypotheses generated using observation information and knowledge information, calculate a probability that the candidate hypothesis holds true as an explanation of the observation information; and a reward selection unit 3 configured to, when the candidate hypothesis holds true, select a reward value regarding the candidate hypothesis that has held true by referring to reward definition information in which a condition that the candidate hypothesis holds true is associated with the reward value.

Abstract: A logical expression processing apparatus includes at least one processor, the at least one processor carrying out: an acquisition process of acquiring background knowledge information and query information; a processing execution process of processing the logical formulas of at least one of the background knowledge information and the query information that have been acquired in the acquisition process, and acquiring processed background knowledge information and processed query information; an inference execution process of executing logical inference using, as inputs, the processed background knowledge information and the processed query information; and an evaluation process of outputting at least one of the processed background knowledge information and the processed query information, depending on whether or not an execution result of the logical inference satisfies a predetermined requirement.

Abstract: In order to more accurately categorize a relation between a plurality of entities, an information processing apparatus (1) includes a relation vector generation section (11), a feature vector generation section (12), and a relation categorization section (13). The relation vector generation section (11) generates a relation vector representing a relation between a plurality of entities of interest from at least one sentence which has been selected from a sentence set and in which the plurality of entities of interest occur. The feature vector generation section (12) generates, for each entity of interest, a feature vector representing a feature of that entity of interest from at least one sentence which has been selected from the sentence set and in which that entity of interest occurs. The relation categorization section (13) categorizes a relation between the plurality of entities of interest with use of a relation vector and feature vectors.

Abstract: An abductive reasoning apparatus (1) includes a generation unit (11) configured to generate a candidate hypothesis from background knowledge and a query logical expression, the background knowledge being a set of given rules that if an antecedent holds, a consequent holds, and the query logical expression indicating observed information by using a logical expression, a constraint determination unit (12) configured to determine whether or not the generated candidate hypothesis contradicts a logical constraint to be satisfied by the generated candidate hypothesis and excluding the candidate hypothesis from a set of the generated candidate hypotheses when the candidate hypothesis contradicts the logical constraint, and a search unit (13) configured to search for a best hypothesis based on the set after the exclusion and then outputting the best hypothesis.

Abstract: An information processing apparatus comprises the constraint enumeration unit that enumerates, for a plurality of candidate hypotheses generated from a query formula and background knowledge, constraints to be satisfied by the candidate hypotheses, the redundant constraint deletion unit that searches for and deletes redundant constraints not to affect an inference result from the constraints enumerated by the constraint enumeration means, and the candidate hypothesis conversion unit generates a combinatorial optimization problem from the plurality of candidate hypotheses and a set of constraints enumerated by the constraint enumeration unit that remain after the deletion of redundant constraints by the redundant constraint deletion unit.

Abstract: In order to efficiently and rigorously enumerate the combinations of logical formulas that lead to a contradiction with the background knowledge for each hypothesis, the information processing apparatus 1 includes the acquisition unit 2 that acquires the contradiction determination function having a data structure that expresses a combination of logical formulas that contradict the background knowledge, and the potential contradiction enumeration unit 3 enumerates the potential contradiction set, in which literals that contradict the background knowledge in the solution candidate set are combined, by using the contradiction determination function.

Abstract: An abduction apparatus 1 includes: a probability calculation unit 2 configured to, with respect to each of candidate hypotheses generated using observation information and knowledge information, calculate a probability that the candidate hypothesis holds true as an explanation of the observation information; and a reward selection unit 3 configured to, when the candidate hypothesis holds true, select a reward value regarding the candidate hypothesis that has held true by referring to reward definition information in which a condition that the candidate hypothesis holds true is associated with the reward value.

Abstract: An abduction apparatus 1 includes: a probability calculation unit 2 configured to calculate, with respect to each of candidate hypotheses generated using observation information and knowledge information, a probability that the candidate hypothesis is an explanation regarding the observation information; a closed world assumption probability calculation unit 3 configured to calculate, with respect to the candidate hypotheses, a closed world assumption probability that the candidate hypothesis is an explanation regarding a first-order predicate logic literal to which a new truth value is determined as a result of assuming a closed world assumption; and a solution hypothesis determination unit 4 configured to determine a solution hypothesis that is a best explanation regarding the observation information from the candidate hypotheses using the probability and the closed world assumption probability.

Abstract: An abduction apparatus 1 includes: a posterior probability calculation unit 2 that calculates, a posterior probability regarding the observation information D1 with respect to each of candidate hypotheses D3 generated using observation information D1 and knowledge information D2; a deduction probability calculation unit 3 that calculates, a deduction probability of deriving observation information D1 as a consequence of applying the knowledge information with respect to each of the candidate hypotheses; a consistency probability calculation unit 4 that calculates, a consistency probability of not contradicting the knowledge information D2 with respect to each of the candidate hypotheses; and a solution hypothesis determination unit 5 that calculates an evaluation value using the posterior probability, the deduction probability, and the consistency probability, and determines a solution hypothesis D4 that is a best explanation regarding the observation information D1, from the candidate hypotheses, based on th

Abstract: The abductive inference device includes a hypothesis generation unit for creating a set of candidate hypotheses, observed information including a hypothesis from which a logical expression is derived based on a knowledge database including knowledge information expressing the logical expression for deriving a consequent from an antecedent, a conversion unit for calculating a constraint condition for the created set of candidate hypotheses and a weight for the constraint condition in accordance with predetermined processing; and a solver unit for calculating a hypothesis when a predetermined condition is satisfied based on the calculated constraint condition and the calculated weight.

Abstract: A determination device is provided with: a hypothesis preparation unit which prepares, according to a prescribed hypothesis preparation procedure, a hypothesis that includes a plurality of logical expressions that indicate a relationship between first information for indicating a certain state among a plurality of states related to a target system, and second information for indicating a target state related to the target system; a conversion unit which obtains, according to a prescribed conversion procedure, an intermediate state that indicates a logical expression different from a logical expression related to the first information among the plurality of logical expressions included in the hypothesis; and a low level planner which determines actions up to the intermediate state obtained from the certain state on the basis of a state-related reward in the plurality of states.

Abstract: A abductive inference apparatus (1) includes a candidate hypothesis generation unit (2) configured to apply, to an observation that indicates an observed situation using a logical expression, inference knowledge provided with the reliability for making a forward inference and the reliability for making a reverse inference, make an inference, and generate a candidate hypothesis by which the observation can be derived; and a candidate hypothesis evaluation unit (3) configured to specify an inference direction for each piece of the inference knowledge applied to the generated candidate hypothesis, and calculate an evaluation value of the candidate hypothesis using the reliability of each piece of the inference knowledge that corresponds to the specified inference direction.