Abstract: The calculation means 82 calculates an accumulated material number by accumulating the number of materials in time series from a predetermined point in the past, and calculates the available number of materials by subtracting a cumulative production capacity accumulated up to an immediately preceding unit period among cumulative production capacities, each of which is a value obtained by accumulating a production capacity in each unit period in time series from the accumulated material number. Then, the calculation means 82 determines the smaller one of the number of products that can be produced, which is calculated based on the process capability, and the number of products that can be produced, which is calculated based on the available number of materials, as the production capacity, and accumulates the production capacities in time series from a predetermined point in the past to calculate the cumulative production capacity for each unit period.

Abstract: A negotiation apparatus capable of efficiently conducting appropriate negotiations is provided. A negotiation apparatus (1) includes a condition acquisition unit (2), a calculation unit (4), and an operation decision unit (6). The condition acquisition unit (2) acquires a demand condition including one or more elements regarding demand for a commodity or a service. The calculation unit (4) calculates a utility value of the acquired demand condition using a predetermined utility function based on resource information. The resource information indicates availability status of one or more resources required to provide the commodity or the like. The operation decision unit (6) decides a negotiation operation, which is an operation regarding a negotiation for providing the commodity or the like based on the calculated utility value.

Abstract: A negotiation system 20 for negotiating with an order-placing side who presents, to an order-receiving side that provides any product or service, an order that represents a request for provision of the any product or service, includes: a determination unit 21 which determines whether execution conditions according to an existing order from the order-placing side can be changed; a planning unit 22 which prepares an order-receiving plan for execution conditions according to a new order from the order-placing side and execution conditions according to the existing order after changing of an execution condition determined to be changeable; and a utility computation unit 23 which computes a utility based on the order-receiving plan.

Abstract: An order-receiving-side negotiation device 20 for negotiating with an order-placing source who presents, to an order-receiving side that provides any product or service, an order proposal that represents a request for provision of the any product or service under predetermined negotiation conditions, includes: a planning unit 21 which prepares one or more negotiation candidates based on the predetermined negotiation conditions presented in the order proposal; an order-receiving-side's utility computation unit 22 which computes utility values for the order-receiving side with respect to the negotiation candidates; an order-placing source's utility estimation unit 23 which estimates utility values for the order-placing source with respect to the negotiation candidates; and a negotiation candidate determination unit 24 which determines a negotiation candidate with respect to the order proposal from among the plurality of negotiation candidates based on both the utility values for the order-receiving side and the

Abstract: A negotiation candidate generation unit 82 generates a negotiation candidate of a receiving side for an assumed order from an ordering side and makes store the negotiation candidate in a negotiation candidate storage unit 81. A negotiation condition receiving unit 83 receives negotiation conditions from the ordering side. A negotiation candidate sending unit 84 sends a corresponding negotiation candidate stored in the negotiation candidate storage unit 81 to the ordering side in response to the received negotiation conditions.

Abstract: The target setting unit 7 sets a target state of the self-driving vehicle 10. The planned route creating unit 3 creates a planned route of the self-driving vehicle 10 for realizing the target state. The transmission unit 6 transmits the planned route to another vehicle. The response receiving unit 9 receives, from another vehicle, a notification indicating agreement with the planned route or disagreement with the planned route as a response to the planned route. The traveling control unit 8 controls the self-driving vehicle 10 so as to cause the self-driving vehicle 10 to travel along the planned route when the response receiving unit 9 has received the notification indicating agreement with the planned route.

Abstract: The non-traveling area plan creating unit 4 creates a plan of the non-traveling area, which is an area where the self-driving vehicle 10 can travel and which is an area set as an area where the self-driving vehicle 10 does not travel. The negotiation area information receiving unit 9 receives, from another vehicle, information on one or more negotiation areas each of which is an area other than the non-traveling area and is a subject of negotiation to be included in the non-traveling area. The permissible area determining unit 35 calculates, for each negotiation area, the first value which is the value of the negotiation area indicated by the information for the self-driving vehicle 10, and determines one negotiation area permissible to be included in the non-traveling area, or determines not to include any negotiation area in the non-traveling area.

Abstract: An automatic negotiation apparatus is an apparatus for automatically negotiating with the other party. The automatic negotiation apparatus includes: a proposal candidate setting unit configured to set proposal candidates to be presented to the other party in a negotiation; a utility value calculation unit configured to calculate a utility value indicating a utility for when the candidate is accepted by the other party for each of the set candidates; a priority setting unit configured to select a candidate that meets set criteria using the utility value for each of the candidates, and further set a priority of each of the selected candidates; and a proposal unit configured to propose one of the selected candidates to the other party according to the priority.

Abstract: A prediction data input unit 91 inputs prediction data that is one or more explanatory variables that are information likely to affect future sales. An exposure pattern generation unit 92 generates an exposure pattern which is an explanatory variable indicating the content of a commercial message scheduled to be performed during a period from predicted time to future prediction target time. A component determination unit 93 determines the component used for predicting the sales, on the basis of a hierarchical latent structure that is a structure in which latent variables are represented by a tree structure and components representing probability models are located at nodes of a lowest level of the tree structure, gating functions for determining a branch direction in the nodes of the hierarchical latent structure, and the prediction data and the exposure pattern.

Abstract: The planned route creating unit 3 creates a planned route of the self-driving vehicle 10. The non-traveling area plan creating unit 4 creates a plan of the non-traveling area, which is an area where the self-driving vehicle 10 can travel and which is an area set as an area where the self-driving vehicle 10 does not travel. The non-traveling area plan creating unit 4 creates a plan of the non-traveling area at a frequency lower than the frequency at which the planned route creating unit 3 creates a planned route. The transmission unit 6 transmits the plan of the non-traveling area to the other vehicle each time the plan of the non-traveling area is created.

Abstract: The non-traveling area plan creating unit 4 creates a plan of the non-traveling area, which is an area where the self-driving vehicle 10 can travel and which is an area set as an area where the self-driving vehicle 10 does not travel. The negotiation area information receiving unit 9 receives, from another vehicle, information on one or more negotiation areas each of which is an area other than the non-traveling area and is a subject of negotiation to be included in the non-traveling area. The permissible area determining unit 35 calculates, for each negotiation area, the first value which is the value of the negotiation area indicated by the information for the self-driving vehicle 10, and based on the first value calculated for each negotiation area, determines one negotiation area permissible to be included in the non-traveling area, or determines not to include any negotiation area in the non-traveling area.

Abstract: The target setting unit 7 sets a target state of the self-driving vehicle 10. The planned route creating unit 3 creates a planned route of the self-driving vehicle 10 for realizing the target state. The transmission unit 6 transmits the planned route to another vehicle. The response receiving unit 9 receives, from another vehicle, a notification indicating agreement with the planned route or disagreement with the planned route as a response to the planned route. The traveling control unit 8 controls the self-driving vehicle 10 so as to cause the self-driving vehicle 10 to travel along the planned route when the response receiving unit 9 has received the notification indicating agreement with the planned route.

Abstract: An apparatus that extracts an explanatory variable used as a condition from a classification model classified by the condition for selecting a component used for prediction, displays the explanatory variable in association with any of dimensional axes of a multi-dimensional space in which a prediction value is displayed, specifies the component that corresponds to a position in the multi-dimensional space specified by each of the explanatory variables associated with the dimensional axis, displays the prediction value calculated based on the specified component, on the same position and displays the multi-dimensional space that corresponds to the position in which the prediction value is displayed, in a mode that corresponds to the component used for calculating the prediction value.

Abstract: A method of extracting a combination of a drug and an adverse event related to the drug includes: for each of positive example combinations, negative example combinations and combinations that are neither positive examples nor negative examples, which are combinations of drug and disease, extracting medical events from medical information data about a patient and generating attribute data based on time-series information about the medical events; and learning a discriminant model based on attribute data of the positive and negative examples; and inputting attribute data corresponding to the combinations that are neither positive examples nor negative examples to the discriminant model to determine scores.

Abstract: The planned route creating unit 3 creates a planned route of the self-driving vehicle 10. The non-traveling area plan creating unit 4 creates a plan of the non-traveling area, which is an area where the self-driving vehicle 10 can travel and which is an area set as an area where the self-driving vehicle 10 does not travel. The non-traveling area plan creating unit 4 creates a plan of the non-traveling area at a frequency lower than the frequency at which the planned route creating unit 3 creates a planned route. The transmission unit 6 transmits the plan of the non-traveling area to the other vehicle each time the plan of the non-traveling area is created.

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: There is provided an automatic negotiation system capable of generating an indicator for defining a favorable target candidate for a supplier. A supply utility function generation means 53 generates a supply utility function expressing a change in profit and loss of the supplier relative to a change in a total demand for each current time and future time. A demand utility function generation means 55 generates a demand utility function expressing a change in profit and loss of the supplier relative to a change in a demand of a consumer for each current time and future time.

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: 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.

Abstract: Provided is an optimization system capable of creating a large amount of data for optimization and specifying values of control variables in order to acquire an optimum result in consideration of uncertainty of predictive values. A simulation means 2 is given a model which is information modeling an object to be analyzed therein and including a parameter containing predictive values and their error ranges, control variables and an objective variable, determines values of the control variables per simulation for specifying a value of the objective variable, and conducts simulation multiple times based on the model. Further, the simulation means 2 determines definite values of the predictive values based on a random number and the parameter per simulation, and conducts simulation by use of values of the control variables and definite values of the predictive values. A control variable value specification means 3 specifies values of the control variables when the objective variable takes an optimum value.