摘要:
An information processing device includes: a learning section configured to learn a state transition probability model defined by state transition probability for each action of a state making a state transition due to an action performed by an agent capable of performing action and observation probability of a predetermined observed value being observed from the state, using an action performed by the agent and an observed value observed in the agent when the agent has performed the action.
摘要:
An HMM (Hidden Markov Model) learning device includes: a learning unit for learning a state transition probability as the function of actions that an agent can execute, with learning with HMM performed based on actions that the agent has executed, and time series information made up of an observation signal; and a storage unit for storing learning results by the learning unit as internal model data including a state-transition probability table and an observation probability table; with the learning unit calculating frequency variables used for estimation calculation of HMM state-transition and HMM observation probabilities; with the storage unit holding the frequency variables corresponding to each of state-transition probabilities and each of observation probabilities respectively, of the state-transition probability table; and with the learning unit using the frequency variables held by the storage unit to perform learning, and estimating the state-transition probability and the observation probability based on the frequency variables.
摘要:
An HMM (Hidden Markov Model) learning device includes: a learning unit for learning a state transition probability as the function of actions that an agent can execute, with learning with HMM performed based on actions that the agent has executed, and time series information made up of an observation signal; and a storage unit for storing learning results by the learning unit as internal model data including a state-transition probability table and an observation probability table; with the learning unit calculating frequency variables used for estimation calculation of HMM state-transition and HMM observation probabilities; with the storage unit holding the frequency variables corresponding to each of state-transition probabilities and each of observation probabilities respectively, of the state-transition probability table; and with the learning unit using the frequency variables held by the storage unit to perform learning, and estimating the state-transition probability and the observation probability based on the frequency variables.
摘要:
An information processing device includes: a calculating unit configured to calculate a current-state series candidate that is a state series for an agent capable of actions reaching the current state, based on a state transition probability model obtained by performing learning of the state transition probability model stipulated by a state transition probability that a state will be transitioned according to each of actions performed by an agent capable of actions, and an observation probability that a predetermined observation value will be observed from the state, using an action performed by the agent, and an observation value observed at the agent when the agent performs an action; and a determining unit configured to determine an action to be performed next by the agent using the current-state series candidate in accordance with a predetermined strategy.
摘要:
A data processing device for processing time-sequence data includes a learning unit for performing self-organizing learning of a SOM (self-organization map) making up a hierarchical SOM in which a plurality of SOMs are connected so as to construct a hierarchical structure, using, as SOM input data which is input to the SOM, a time-sequence of node information representing a winning node of a lower-order SOM which is at a lower hierarchical level from the SOM.
摘要:
A data processing device for processing time-sequence data includes a learning unit for performing self-organizing learning of a SOM (self-organization map) making up a hierarchical SOM in which a plurality of SOMs are connected so as to construct a hierarchical structure, using, as SOM input data which is input to the SOM, a time-sequence of node information representing a winning node of a lower-order SOM which is at a lower hierarchical level from the SOM.
摘要:
An information processing device includes a learning unit that performs, using an action performed by an object and an observation value of an image as learning data, learning of a separation learning model that includes a background model that is a model of the background of the image and one or more foreground model(s) that is a model of a foreground of the image, which can move on the background, in which the background model includes a background appearance model indicating the appearance of the background, and at least one among the one or more foreground model(s) includes a transition probability, with which a state corresponding to the position of the foreground on the background is transitioned by an action performed by the object corresponding to the foreground, for each action, and a foreground appearance model indicating the appearance of the foreground.
摘要:
A behavior control system and a behavior control method for a robot apparatus are disclosed. The behavior control system and the behavior control method for a robot apparatus include a function of adaptively switching between a behavior selection standard, taking into account the own state, required of an autonomous robot, and a behavior selection standard, taking into account the state of a counterpart, responsive to a situation. A behavior selection control system in a robot apparatus includes a situation-dependent behavior layer (SBL), capable of selecting a particular behavior from plural behaviors, and outputting the so selected behavior, and an AL calculating unit 120 for calculating the AL (activation level), indicating the priority of execution of the behaviors, for behavior selection. This AL calculating unit 120 includes a self AL calculating unit 122 and a counterpart AL calculating unit 124 for calculating the self AL and the counterpart AL, and an AL integrating unit 125 for summing the self AL and the counterpart AL with weighting by a parameter used for determining whether emphasis is to be placed on the self state or on the counterpart state, to output an ultimate AL. The counterpart is a subject of interaction of the robot apparatus. The self AL and the counterpart AL indicate the priority of execution of the behavior with the self and with the co8unbterpart as a reference, respectively.
摘要:
Teaching to a robot for an action on an external object can be made easily and efficiently. According to the present invention, there is provided an information processing apparatus including a learning unit for learning a predetermined time-series pattern, an output unit for outputting a time-series pattern corresponding to the result of learning by the learning unit, and an adjusting unit supplied with a time-series pattern obtained from an action made by an action unit on the basis of a time-series pattern supplied from the output unit and external teaching for the action to adjust a time-series pattern supplied from the output unit correspondingly to the input time-series pattern, the learning unit learning the time-series pattern supplied from the output unit and adjusted by the adjusting unit.
摘要:
An information processing apparatus including a learning unit that learns a predetermined time-series pattern. An output unit outputs a time-series pattern corresponding to the result of learning by the learning unit. An adjusting unit supplied with a time-series pattern obtained from an action by an action unit on the basis of a time-series pattern supplied from the output unit and external teaching for the action adjusts a time-series pattern supplied from the output unit correspondingly to the input time-series pattern. The learning unit learns the time-series pattern supplied from the output unit and adjusted by the adjusting unit.