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:
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:
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:
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:
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:
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.
Abstract:
An energy-amount estimation device that can predict an energy amount with a high degree of precision is disclosed. Said energy-amount estimation device has a prediction unit that, on the basis of the relationship between energy amount and one or more explanatory variables representing information that can influence said energy amount, predicts an energy amount pertaining to prediction information that indicates a prediction target. The aforementioned relationship is computed on the basis of specific learning information, within learning information in which an objective variable representing the aforementioned energy amount is associated with the one or more explanatory variables, that matches or is similar to the aforementioned prediction information.
Abstract:
This invention helps improve the precision of data mining. This information processing system is provided with an attribute-generating means and an evaluating means, as follows. From among a plurality of inputted attributes, the attribute-generating means selects a combination of attributes to serve as operands for a function that defines an operation that takes a plurality of operands. The attribute-generating means applies said function to that combination of attributes to generate a new attribute that is the result of applying that function to that combination of attributes. The evaluating means inputs said new attribute to an analysis engine, which executes an analysis process on the basis of the attribute, and determines whether or not information outputted by said analysis engine satisfies a prescribed requirement.
Abstract:
To further reduce the number of times draft agreement candidates are provided until a consensus forms, a negotiation apparatus includes a disclosed negotiation strategy provision section that provides a first disclosed negotiation strategy that is a disclosed negotiation strategy of a first negotiation entity and that includes a first utility function or a parameter set defining the first utility function.
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.