Abstract: A method of meeting a power demand of a power consumption unit is disclosed. A forecasted power demand for a power demand scenario for the power consumption unit is determined and a probability of occurrence of the power demand scenario is determined. An objective function for operating at least one power supply device is created that includes the forecasted power demand of the power demand scenario and the determined probability of occurrence of the power demand scenario. A substantial minimum of the objective function is located to determine a schedule for operating the at least one power supply device to meet the forecasted power demand. The at least one power supply device may be operated according to the determined schedule to meet the power demand of the power consumption unit.

Abstract: A generation apparatus generates gain vectors for calculating cumulative expected gains for a transition model in which transition from a current state to a next state occurs in response to an action. The apparatus includes: an acquisition section that acquires gain vectors for a time point next to a target time point that includes cumulative expected gains for and after the next time point for each state at the next time point; a first determination section that determines a value of a transition parameter used for transitioning from the target time point to the next time point, from a valid range of the transition parameter, based on the cumulative expected gains obtained for the gain vectors for the next time point; and a first generation section that generates gain vectors for the target time point from the gain vectors for the next time point, using the transition parameter.

Abstract: A dynamic time-evolution Boltzmann machine capable of learning is provided. Aspects include acquiring a time-series input data and supplying a plurality of input values of input data of the time-series input data at one time point to a plurality of nodes of the mode. Aspects also include computing, based on an input data sequence before the one time point in the time-series input data and a weight parameter between each of a plurality of input values of input data of the input data sequence and a corresponding one of the plurality of nodes of the model, a conditional probability of the input value at the one time point given that the input data sequence has occurred. Aspects further include adjusting the weight parameter so as to increase a conditional probability of occurrence of the input data at the one time point given that the input data sequence has occurred.

Abstract: An information processing apparatus includes a history acquisition section configured to acquire history data including a history indicating that a plurality of selection subjects have selected selection objects; a learning processing section configured to allow a choice model to learn a preference of each selection subject for a feature and an environmental dependence of selection of each selection object in each selection environment using the history data, where the choice model uses a feature value possessed by each selection object, the preference of each selection subject for the feature, and the environmental dependence indicative of ease of selection of each selection object in each of a plurality of selection environments to calculate a selectability with which each of the plurality of selection subjects selects each selection object; and an output section configured to output results of learning by the learning processing section.

Abstract: An apparatus and method for solving mathematical programming problems. The apparatus includes a first-time-point-solution generating unit generating at least one solution to a mathematical programming problem, a second-time-point-problem generating unit generating a plurality of mathematical programming problems to be on the basis of the solution to the mathematical programming problem to be solved at the first time point, a second-time-point optimum value calculating unit calculating an optimum value of each of a plurality of mathematical programming problems to be solved at the second time point, a relational expression estimating unit estimating a relational expression between the solution to the mathematical programming problem to be solved at the first time point and an optimum value of a mathematical programming problem to be solved at the second time point, and a correcting unit correcting the mathematical programming problem at the first time point based on the relational expression.

Abstract: A dynamic time-evolution Boltzmann machine capable of learning is provided. Aspects include acquiring a time-series input data and supplying a plurality of input values of input data of the time-series input data at one time point to a plurality of nodes of the mode. Aspects also include computing, based on an input data sequence before the one time point in the time-series input data and a weight parameter between each of a plurality of input values of input data of the input data sequence and a corresponding one of the plurality of nodes of the model, a conditional probability of the input value at the one time point given that the input data sequence has occurred. Aspects further include adjusting the weight parameter so as to increase a conditional probability of occurrence of the input data at the one time point given that the input data sequence has occurred.

Abstract: A method, device and computer program for efficiently identifying items having a high frequency of occurrence among items included in a large-volume text data stream. Identification information for identifying an item and a count of items are stored in a higher level of memory and only identification information is stored in a lower level. Text data stream input is received, the increment of the count of an item is increased in response to storage in the higher level memory of identification information for an item included in a bucket divided from the received text data stream input, identification information for the item is transferred with the initial count to the higher level of memory in response to storage in the lower level and the identification information for the item is newly stored with the initial count in the higher level in response to not being stored on any level.

Abstract: An information processing apparatus includes a history acquisition section configured to acquire history data including a history indicating that a plurality of selection subjects have selected selection objects; a learning processing section configured to allow a choice model to learn a preference of each selection subject for a feature and an environmental dependence of selection of each selection object in each selection environment using the history data, where the choice model uses a feature value possessed by each selection object, the preference of each selection subject for the feature, and the environmental dependence indicative of ease of selection of each selection object in each of a plurality of selection environments to calculate a selectability with which each of the plurality of selection subjects selects each selection object; and an output section configured to output results of learning by the learning processing section.

Abstract: A method for virtual machine (VM) consolidation includes providing a plurality of resource usage levels for a set of VMs to be consolidated including a first resource usage level and a last resource usage level. An optimization problem is formulated to minimize an objective function such that any of one or more VMs of a set of VMs to be allocated to a target server may be assigned to the first resource level and remaining VMs of the set may be assigned to the last resource level while not exceeding a resource capacity of the target server. The set of VMs are allocated to a number of servers is accordance with the formulating to consolidate the set of VMs.

Abstract: A technique that can estimate purchase behavior of a customer in a store is provided. The technique includes acquiring article information on at least one article that a target customer purchases in the store, and layout information on each store and shelving allocation information on each store. The technique includes reading at least one of previous path information on actual travel of one or more customers in the store, and previous path information on actual travel of the target customer in the store or across the stores. The technique includes estimating a traffic line of the target customer in the store or across the stores, according to a tendency acquired from the path information read, based on each piece of the information acquired.

Abstract: Apparatus and method use a Markov decision process (MDP) to reduce the cost of variations in electric power usage. The user notifies a power company of a predicted value for a period. The period is divided into subsections. For each subsection, on the basis of a MDP including a state that depends on an electric power usage amount error, charge amount, and set target, the amount of charging and discharging of a storage battery as an action at any given time is optimally decided depending on the electric power usage amount error, charge amount, time, and set target at that time. A predetermined time in a subsection is a target setting time, at which a future target is further set as the action. The action includes deciding the charging and discharging amount in that subsection and deciding a future target in a subsection whose target should be set.

Abstract: 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.

Abstract: 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.

Abstract: A processing apparatus, a processing method, and a program that generates a selection model obtained by modeling selection behavior of a target to a given choice. The processing apparatus includes an acquiring unit configured to acquire learning data including at least one selection behavior for learning in which choices given to the target are input choices and choices selected out of the input choices are output choices, an input vector generating unit configured to generate an input vector that indicates whether each of a plurality of kinds of choices is included in the input choices, and a learning processing unit configured to learn the selection model using the input vector corresponding to an input choice for learning and the output choices.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.