Abstract: An inference method according to the present invention in an inference system inferring a probability that an ending state holds based on a starting state and a rule set, the method includes: when a rule set derived by excluding one rule from rules constituting a first rule set is set as a second rule set, a probability that the ending state holds based on the starting state and the first rule set is set as a first inference result, and a probability that the ending state holds based on the starting state and the second rule set is set as a second inference result, calculating an importance being an indicator indicating magnitude of a difference between the first inference result and the second inference result; and outputting the rule and the importance of the rule, being associated with each other for each of the excluded rule.

Abstract: An information processing system for improving detection of a relation between events is provided. A learning system (100) includes a training data storage (120) and a learning module (130). The training data storage (120) stores a training pair of a first and second event, and a relation between the training pair of the first and second events. The relation is a first or second relation. The learning module 130 learns a neural network for classifying a relation between a pair of the first and second events to be classified as the first or second relation, by using the training pair. The neural network includes a first layer to extract a feature of the first relation from features of the first and second events, a second layer to extract a feature of the second relation from the features of the first and second events, and a joint layer to extract a joint feature of the first and second relations from the features of the first and second relations.

Abstract: An information processing system for improving detection of a relation between events is provided. A learning system (100) includes a training data storage (120) and a learning module (130). The training data storage (120) stores a training pair of a first and second event, and a relation between the training pair of the first and second events. The relation is a first or second relation. The learning module 130 learns a neural network for classifying a relation between a pair of the first and second events to be classified as the first or second relation, by using the training pair. The neural network includes a first layer to extract a feature of the first relation from features of the first and second events, a second layer to extract a feature of the second relation from the features of the first and second events, and a joint layer to extract a joint feature of the first and second relations from the features of the first and second relations.

Abstract: An inference method according to the present invention in an inference system inferring a probability that an ending state holds based on a starting state and a rule set, the method includes: when a rule set derived by excluding one rule from rules constituting a first rule set is set as a second rule set, a probability that the ending state holds based on the starting state and the first rule set is set as a first inference result, and a probability that the ending state holds based on the starting state and the second rule set is set as a second inference result, calculating an importance being an indicator indicating magnitude of a difference between the first inference result and the second inference result; and outputting the rule and the importance of the rule, being associated with each other for each of the excluded rule.

Abstract: A parameter optimization apparatus 10 includes a simulator 11, which executes a simulation on a specific event by using a parameter as an input, a data interpreter 2, which converts the result of the output from the simulator 11 into a logical expression, an inference unit 13, which estimates a phenomenon that occurs in the specific event by using the logical expression, a query representing a target state of the specific event, and knowledge information prepared in advance for the specific event and generates an inference path from the estimated phenomenon, and a parameter determiner 14, which determines from the inference path a new parameter that is an input in the simulation, and when the new parameter is determined, the simulator 11 executes the simulation on the specific event by using the new parameter as an input.

Abstract: An information processing system for learning new probabilistic rules even if only one training sample is given. A learning system (100) includes a KB (knowledge base) storage (110), a rule generator (130), and a weight calculator (140). The KB storage (110) stores a KB including a knowledge storage for storing rules between events among a plurality of events. The rule generator (130) generates one or more new rules based on the rules and an implication score between the events. The weight calculator (140) calculates a weight of the one or more new rules for probabilistic reasoning based on the implication score.

Abstract: An inference system according to the present invention relates to inference from a starting state and a first rule set to an ending state. The inference system includes: a memory; and at least one processor coupled to the memory. The processor performs operations. The operations includes: receiving a parameter for use in selecting a second rule set from the first rule set; and visualizing the second rule set associated with the parameter.

Abstract: A reasoning system that enables reasoning when there is a shortage of knowledge. An input unit receives a start state and an end state. A rule candidate generation unit identifies a first state, obtained by tracking one or more known rules from the start state, and a second state, obtained by backtracking one or more known rules from the end state, respectively. The generation unit generates a rule candidate relating to the first state and the second state or generates a rule candidate relating to the first state and a rule candidate relating to the second state. A rule selection unit selects, based on feasibility of the generated rule candidate, which is calculated based on one or more known rules, the generated rule candidate as a new rule. A derivation unit derives the end state from the start state, based on one or more known rules and the new rule.