Patent application number | Description | Published |
20130085974 | USING CYCLIC MARKOV DECISION PROCESS TO DETERMINE OPTIMUM POLICY - A method for determining an optimum policy by using a Markov decision process in which T subspaces each have at least one state having a cyclic structure includes identifying, with a processor, subspaces that are part of a state space; selecting a t-th (t is a natural number, t≦T) subspace among the identified subspaces; computing a probability of, and an expected value of a cost of, reaching from one or more states in the selected t-th subspace to one or more states in the t-th subspace in a following cycle; and recursively computing a value and an expected value of a cost based on the computed probability and expected value of the cost, in a sequential manner starting from a (t −1)-th subspace. | 04-04-2013 |
20130085983 | USING CYCLIC MARKOV DECISION PROCESS TO DETERMINE OPTIMUM POLICY - A method for determining an optimum policy by using a Markov decision process in which T subspaces each have at least one state having a cyclic structure includes identifying, with a processor, subspaces that are part of a state space; selecting a t-th (t is a natural number, t≦T) subspace among the identified subspaces; computing a probability of, and an expected value of a cost of, reaching from one or more states in the selected t-th subspace to one or more states in the t-th subspace in a following cycle; and recursively computing a value and an expected value of a cost based on the computed probability and expected value of the cost, in a sequential manner starting from a (t−1)-th subspace. | 04-04-2013 |
20130179380 | PREDICTION METHOD, PREDICTION SYSTEM AND PROGRAM - A method for predicting an output variable from explanatory values provided as sets of combinations of discrete variables and continuous variables includes receiving input data that contains the explanatory variables to predict the output variable; searching for each element in the combinations for elements in a plurality of sets with matching discrete variables using training data which the output variable has been observed; applying a function giving the degree of similarity between two sets weighed by a scale variable to each element in the input data, and to one or more elements found in the elements of the input data to calculate function values, and calculating the sum of the function values for all of the elements in the input data; and applying the calculated sum for each element to a prediction equation for predicting the output variable to calculate a prediction value of the output variable for each element. | 07-11-2013 |
20130184992 | METHOD, APPARATUS AND COMPUTER PROGRAM FOR ESTIMATING DRIVER'S PERSONALITY OF ROUTE SELECTION - A method for selecting a route from a departure point to an arrival point includes acquiring information concerning a departure point and an arrival point and information concerning a route from the departure point to the arrival point; generating a plurality of basic routes; calculating a parameter of an evaluation function that yields the selected route as an optimum route; generating a new route using the calculated parameter, determining whether or not the generated new route is identical to the selected route; on a condition that the generated new route is not identical to the selected route, adding the generated new route to the basic routes, recalculating the parameter, generating a new route, and comparing the new route with the selected route; and if the new route is identical to the selected route, storing the parameter when the new data becomes identical to the selected route. | 07-18-2013 |
20130318023 | UPDATING POLICY PARAMETERS UNDER MARKOV DECISION PROCESS SYSTEM ENVIRONMENT - Embodiments relate to updating a parameter defining a policy under a Markov decision process system environment. An aspect includes updating the policy parameter stored in a storage section of a controller according to an update equation. The update equation includes a term for decreasing a weighted sum of expected hitting times over a first state (s) and a second state (s′) of a statistic on the number of steps required to make a first state transition from the first state (s) to the second state (s′). | 11-28-2013 |
20130325764 | UPDATING POLICY PARAMETERS UNDER MARKOV DECISION PROCESS SYSTEM ENVIRONMENT - Embodiments relate to updating a parameter defining a policy under a Markov decision process system environment. An aspect includes updating the policy parameter stored in a storage section of a controller according to an update equation. The update equation includes a term for decreasing a weighted sum of expected hitting times over a first state (s) and a second state (s′) of a statistic on the number of steps required to make a first state transition from the first state (s) to the second state (s′). | 12-05-2013 |
20150220741 | PROCESSING INFORMATION BASED ON POLICY INFORMATION OF A TARGET USER - An information processing apparatus includes a policy acquisition unit configured to acquire a policy on disclosure of information on a target user; a collection unit configured to collect attributes that may be related to the target user from public information disclosed on a network to create an attribute set related to the target user; and a determination unit configured to determine whether or not the attribute set satisfies the policy. | 08-06-2015 |
20150262218 | GENERATING APPARATUS, SELECTING APPARATUS, GENERATION METHOD, SELECTION METHOD AND PROGRAM - A generating apparatus is arranged to generate a set of gain vectors with respect to a transition model having observable visible states and unobservable hidden states and expressing a transition from a present visible state to a subsequent visible state according to an action, the set of gain vectors being generated for each visible state and used for calculation of a cumulative expected gain at and after a reference point in time. The apparatus includes a generation section for recursively generating, by retroacting from a future point in time to the reference point in time, a set of gain vectors containing at least one gain vector including a component of a cumulative expected gain with respect to each hidden state, from which set of gain vectors the gain vector giving the maximum of the cumulative expected gain is to be selected. | 09-17-2015 |
20150262231 | GENERATING APPARATUS, GENERATION METHOD, INFORMATION PROCESSING METHOD AND PROGRAM - A generating apparatus generates a set of gain vectors with respect to a transition model having observable visible states and unobservable hidden states and expressing a transition from a present visible state to a subsequent visible state according to an action, the set of gain vectors being generated for each visible state and used for calculation of a cumulative expected gain at and after a reference point in time, the apparatus including a setting section for setting, with respect to each hidden state, a probability distribution over the hidden states for selection used to select vectors to be included in the set of gain vectors from the gain vectors including a component for a cumulative gain, and a selection section for including, in the set of gain vectors, with priority, the gain vector giving the maximum of the cumulative expected gain with respect to the probability distribution for selection. | 09-17-2015 |