Abstract: Provided is an area evaluation system comprising an area evaluation unit (601) that evaluates, when a first mission that uses a first area and a second mission that does not use the first area are provided, utility of the first area based on a difference between utility of the first mission and utility of the second mission.

Abstract: In a kernel learning apparatus, a data preprocessing circuitry preprocesses and represents each data example as a collection of feature representations that need to be interpreted. An explicit feature mapping circuit designs a kernel function with an explicit feature map to embed the feature representations of data into a nonlinear feature space and to produce the explicit feature map for the designed kernel function to train a predictive model. A convex problem formulating circuitry formulates a non-convex problem for training the predictive model into a convex optimization problem based on the explicit feature map. An optimal solution solving circuitry solves the convex optimization problem to obtain a globally optimal solution for training an interpretable predictive model.

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: In a kernel learning apparatus, a data preprocessing circuitry preprocesses and represents each data example as a collection of feature representations that need to be interpreted. An explicit feature mapping circuit designs a kernel function with an explicit feature map to embed the feature representations of data into a nonlinear feature space and to produce the explicit feature map for the designed kernel function to train a predictive model. A convex problem formulating circuitry formulates a non-convex problem for training the predictive model into a convex optimization problem based on the explicit feature map. An optimal solution solving circuitry solves the convex optimization problem to obtain a globally optimal solution for training an interpretable predictive model.

Abstract: In a kernel learning apparatus, a data preprocessing circuitry preprocesses and represents each data example as a collection of feature representations that need to be interpreted. An explicit feature mapping circuit designs a kernel function with an explicit feature map to embed the feature representations of data into a nonlinear feature space and to produce the explicit feature map for the designed kernel function to train a predictive model. A convex problem formulating circuitry formulates a non-convex problem for training the predictive model into a convex optimization problem based on the explicit feature map. An optimal solution solving circuitry solves the convex optimization problem to obtain a globally optimal solution for training an interpretable predictive model.

Abstract: Automated negotiation agent adaptation is performed by detecting change in a utility function involved in automated negotiation between a supporting agent and an opposing agent while the supporting agent operates according to a first negotiation strategy model, generating a plurality of training samples from automated negotiation between the supporting agent and the opposing agent while the supporting agent operates according to a baseline negotiation strategy model, and training an initialized negotiation strategy model using the plurality of training samples to produce a second negotiation strategy model.

Abstract: The kernel function generation unit 81 defines a first kernel function by using two-dimensional feature representation that represents a combination of two features of data. The model learning unit 82 defines a linear model including an inner product of a mapping used in the first kernel function and a first weight and performs learning with the defined linear model. The component expanding unit 83 expands the learned linear model to define expanded component representation that is new component representation of the data. The expansion model generation unit 84 generates an expansion model including an inner product of data by the expanded component representation and a second weight.

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 device conducting automated negotiation by transmitting and receiving proposals to and from a counterpart device includes an estimating unit configured to estimate a negotiation characteristic of the counterpart device by using information corresponding to a proposal transmitted by the negotiation device.

Abstract: A path reception means 81 receives path evaluation data including each of paths of a plurality of moving bodies and an evaluation value of a business operator for each of the paths. A multi-path optimization means 82 performs optimization based on a utility function that represents adequacy of a business operator for a path learned based on the path evaluation data such that a number of the moving bodies existing in an identical area is a predetermined number or less and determines each of the paths of the plurality of moving bodies to adjust the paths of the plurality of moving bodies. A path transmission means 83 transmits each optimized path.

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: 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: Provided is a delivery assistance device enabling shortening of the time required for delivery in purchase of an item by online shopping. This delivery assistance device is provided with: management means for managing stock information about stock of an item held by each of a plurality of traveling vehicles; first reception means for receiving current position information indicating a current position of the traveling vehicle; first transmission means for transmitting the stock information to a customer terminal; second reception means for receiving order information and delivery address information; specification means for specifying a traveling vehicle arriving earliest at the delivery address; and second transmission means for transmitting the order information to the specified traveling vehicle, wherein the management means updates the stock information about stock of an item held by the specified traveling vehicle, based on the order information.

Abstract: A customer service assistance apparatus 10 is provided with a video image acquisition unit 11 that acquires a video image of the inside of a store, a specifying unit 12 that specifies a product that a customer in the store is interested in and attributes of the customer based on the acquired video image, an information acquisition unit 13 that searches a specific database using the specified product and attributes as queries, and acquires content corresponding to the product and attributes that serve as queries, and the transmission unit 14 that transmits the acquired content to a terminal apparatus that is used by a store salesperson of the store.

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: A prediction model generation system is provided that is capable of generating a prediction model for accurately predicting a relationship between an ID of a record in first master data and an ID of a record in second master data. Co-clustering means 71 performs co-clustering processing for performing co-clustering on first IDs and second IDs in accordance with first master data, second master data, and fact data indicating a relationship between each of the first IDs and each of the second IDs. Prediction model generation means 72 performs prediction model generation processing for generating a prediction model for each combination of a first ID cluster and a second ID cluster. The prediction model uses the relationship between each of the first IDs and each of the second IDs as an objective variable. The first ID cluster serves as a cluster of the first IDs. The second ID cluster serves as a cluster of the second IDs.

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 model generation system is provided that is capable of generating a prediction model for accurately predicting a relationship between an ID of a record in first master data and an ID of a record in second master data. Co-clustering means 71 performs co-clustering on first IDs and second IDs in accordance with first master data, second master data, and fact data indicating a relationship between each of the first IDs and each of the second IDs. Each of the first IDs serves as an ID of a record in the first master data. Each of the second IDs serves as an ID of a record in the second master data. Prediction model generation means 72 generates a prediction model for each combination of a first ID cluster and a second ID cluster. The prediction model uses the relationship between each of the first IDs and each of the second IDs as an objective variable. The first ID cluster serves as a cluster of the first IDs. The second ID cluster serves as a cluster of the second IDs.

Abstract: A customer service assistance apparatus 10 is provided with a video image acquisition unit 11 that acquires a video image of the inside of a store, a specifying unit 12 that specifies a product that a customer in the store is interested in and attributes of the customer based on the acquired video image, an information acquisition unit 13 that acquires information indicating need of the product for the customer, based on the specified product and attributes, and a transmission unit 14 that transmits the acquired content to a terminal apparatus that is used by a store salesperson of the store.