Nec Corporation

Shinji Nakadai

Masafumi Oyamada

Satoshi Morinaga

Hiromi Yamaguchi

Hideaki Sato

Junko Watanabe

Masato Asahara

HIroaki Inotsume

Hongbo Zeng

Hao Zhang

Yasser Farouk Othman Mohammad

Ayan Sengupta

Katsufumi Tomobe

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.

Automated negotiation agent adaptation

A path reception meansreceives 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 meansperforms 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 meanstransmits each optimized path.

Path adjustment system, path adjustment device, path adjustment method, and path adjustment program

Provided is an area evaluation system comprising an area evaluation unit () 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.

Area evaluation system, method, and recording medium

The kernel function generation unitdefines a first kernel function by using two-dimensional feature representation that represents a combination of two features of data. The model learning unitdefines 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 unitexpands the learned linear model to define expanded component representation that is new component representation of the data. The expansion model generation unitgenerates an expansion model including an inner product of data by the expanded component representation and a second weight.

Model generation system, model generation method, and model generation program

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.

Negotiation device, estimation method, program, and estimation device

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.

Delivery assistance device, customer terminal, and delivery assistance method

A customer service assistance apparatusis provided with a video image acquisition unitthat acquires a video image of the inside of a store, a specifying unitthat 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 unitthat 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 unitthat transmits the acquired content to a terminal apparatus that is used by a store salesperson of the store.

Customer service assistance apparatus, customer service assistance method, and computer-readable recording medium

The target setting unitsets a target state of the self-driving vehicle. The planned route creating unitcreates a planned route of the self-driving vehiclefor realizing the target state. The transmission unittransmits the planned route to another vehicle. The response receiving unitreceives, 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 unitcontrols the self-driving vehicleso as to cause the self-driving vehicleto travel along the planned route when the response receiving unithas received the notification indicating agreement with the planned route.

Vehicle control system, self-driving vehicle, vehicle control method, and program

The non-traveling area plan creating unitcreates a plan of the non-traveling area, which is an area where the self-driving vehiclecan travel and which is an area set as an area where the self-driving vehicledoes not travel. The negotiation area information receiving unitreceives, 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 unitcalculates, for each negotiation area, the first value which is the value of the negotiation area indicated by the information for the self-driving vehicle, 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.

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 meansperforms 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 meansperforms 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. The prediction model generation processing and the co-clustering processing are repeated until it is determined that a prescribed condition is satisfied.

Prediction model generation system, method, and program

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 meansperforms 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 meansgenerates 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.

The planned route creating unitcreates a planned route of the self-driving vehicle. The non-traveling area plan creating unitcreates a plan of the non-traveling area, which is an area where the self-driving vehiclecan travel and which is an area set as an area where the self-driving vehicledoes not travel. The non-traveling area plan creating unitcreates a plan of the non-traveling area at a frequency lower than the frequency at which the planned route creating unitcreates a planned route. The transmission unittransmits the plan of the non-traveling area to the other vehicle each time the plan of the non-traveling area is created.

A learning meanslearns, based on learning data containing the meaning of a column in a table and the meaning of the table, a model indicating regularity between the meaning of the column in the table and the meaning of the table. An estimation meansestimates the meaning of the table based on the meaning of a column of a table to be input and the model.

Table-meaning estimation system, method, and program

A data acquiring section specifies individual tables to serve as reference destinations of designated respective foreign key columns and, for each combination of key values in primary key columns of the specified individual tables, acquires a total value of relation values in a designated relation value column. A grouping section groups the respective key values in the primary key columns of the individual tables on the basis of the total value acquired for each combination of the key values in the primary key columns of the individual tables and a statistical model of a designated type. An attribute value column adding section adds, to each individual table, an attribute value column of which attribute value is identification information on a group to which each key value in each primary key column of each individual table belongs.

System, method, and program for business intelligence using table operations in a relational database

An object is to provide a clustering system capable of performing clustering of a plurality of types of items to be able to recommend an item whose corresponding textual data exists but relational data with another type of item does not exist, to the other type of item. A first clustering meansperforms clustering of first IDs, based on the relational data. A second clustering meansperforms clustering of second IDs, based on the relational data and textual data associated with the second IDs. A topic assignment meansassigns a topic for each word included in textual data corresponding to each second ID. A parameter decision meansdecides a parameter used for first clustering processing, a parameter used for second clustering processing, and a parameter used for topic assignment processing. The processing described above is repeated until it is determined that a predetermined condition is satisfied.

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Tokyo, Japan

Shinji Nakadai