Abstract: A learning device includes: a plurality of learning modules, each of which performs update learning to update a plurality of model parameters of a pattern learning model that learns a pattern using input data; model parameter sharing means for causing two or more learning modules from among the plurality of learning modules to share the model parameters; and sharing strength updating means for updating sharing strengths between the learning modules so as to minimize learning errors when the plurality of model parameters are updated by the update learning.

Abstract: A learning device includes: a plurality of learning modules, each of which performs update learning to update a plurality of model parameters of a pattern learning model that learns a pattern using input data; model parameter sharing means for causing two or more learning modules from among the plurality of learning modules to share the model parameters; module creating means for creating a new learning module corresponding to new learning data for learning the pattern when the new learning data are supplied as the input data; similarity evaluation means for evaluating similarities among the learning modules after the update learning is performed over all the learning modules including the new learning module; and module integrating means for determining whether to integrate the learning modules on the basis of the similarities among the learning modules and integrating the learning modules.

Abstract: A learning device includes: a plurality of learning modules, each of which performs update learning to update a plurality of model parameters of a pattern learning model that learns a pattern using input data; model parameter sharing means for causing two or more learning modules from among the plurality of learning modules to share the model parameters; and classification means for classifying the plurality of learning modules on the basis of the plurality of model parameters of each of the learning modules after the update learning.

Abstract: A data processing apparatus includes: predicting means for calculating a prediction value of time series data with respect to input of the time series data using a prediction model for predicting the time series data; determining means for determining a target value of the time series data on the basis of the prediction value of the time series data; error calculating means for calculating an error of the prediction value relative to the target value; and retrieving means for retrieving error reduction data as input of the time series data to the prediction model for reducing the error of the prediction value.

Abstract: An information processing device includes an obtaining unit to obtain two pieces of information that are targets for searching for connections; a connection searching unit to use an action model wherein the manner of obtaining, from input information, obtain related information that relates to the input information is modeled, and find connection information to connect the two pieces of information, thereby searching connections between the two pieces of information; and a search result output unit to output the search results of the connections between the two pieces of information.

Abstract: A robot apparatus which, by exploiting limited resources highly efficiently, is capable of making expressions matched to the motion or to complex variegated feeling or instinct states, representing the crucial information in achieving smooth communication with the human being, is disclosed. A method of expression by the robot apparatus is also disclosed. The robot apparatus expresses plural states, such as emotional states or the processing states of perceptual recognition, by a light radiating device loaded at a location of the head part of the humanoid robot apparatus walking on two legs. The light radiating device includes e.g. the color hue, saturation and patterns of light emission as expressive units represented independently of one another.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A robot apparatus which, by exploiting limited resources highly efficiently, is capable of making expressions matched to the motion or to complex variegated feeling or instinct states, representing the crucial information in achieving smooth communication with the human being, is disclosed. A method of expression by the robot apparatus is also disclosed. The robot apparatus expresses plural states, such as emotional states or the processing states of perceptual recognition, by a light radiating device loaded at a location of the head part of the humanoid robot apparatus walking on two legs. The light radiating device includes e.g. the color hue, saturation and patterns of light emission as expressive units represented independently of one another.