Abstract: A language tutoring machine communicates with a user for teaching of a linguistic sub-system in a particular language. The language tutoring system comprises at least one computational module that functions to produce and comprehend utterances which employ the linguistic sub-system. The at least one computational module embodies two models which operationalizes the linguistic sub-system; a student model approximating a specified user's performance when producing and comprehending language involving the linguistic sub-system, and a teacher model which represents an archetypal configuration of the language-system. The teacher model and student model use the same formalisms, advantageously Incremental Recruitment Language (for conceptualising and interpreting) and Fluid Construction Grammar (for expression and parsing).

Abstract: In the exchange of information in a peer-to-peer information system network, the problem of semantic interoperability of the communication system used by the agent associated with every peer is addressed not by imposing a universal pre-defined ontology over universally defined conceptual schemata, but instead using mechanisms, inspired from natural language, that enable each agent to develop a repertoire of grounded categories and labels for these categories and to negotiate their use and semantics with other agents. The communication system, as well as its semantics, is hence emergent and adaptive instead of predefined.

Abstract: A peer-to-peer approach is used for the transfer of electrical power between devices (10,30). A communications link (24) is used for negotiation of the details of a desired power-supply transaction between a power-supplying device and a power-receiving device, including the electrical specification of the power to be transferred. When the transaction details are settled, a power supply link (23) is used for implementing the agreed transfer of power. The negotiated electrical specification can include one or more of: the voltage, the waveform (ac or dc), the ac frequency, the power factor, the maximum current, and the total power for the proposed transfer.

Abstract: Developmental systems (1/11) are provided with an autotelic mechanism for driving their development. An autotelic component (1) in the system uses a mapping mechanism (2) to produce an output based on a set of inputs. The mapping mechanism (2) implements a mapping that is dependent upon a state associated therewith. The content of the state is changed by a learning/repair module (3) based on interactions between the system and the environment, and so reflects knowledge gained by this component as a result of its experience. The autotelic component (1) monitors its own performance with reference to the level of a set of one or more challenge parameters whose levels quantify the complexity of different parameters relating to the autotelic component (1). The state associated with the mapping mechanism 2 is altered depending upon the component's performance as evaluated during the monitoring. A controller (5) controls the levels of the challenge parameters.

Abstract: In the exchange of information in a peer-to-peer information system network, the problem of semantic interoperability of the communication system used by the agent associated with every peer is addressed not by imposing a universal pre-defined ontology over universally defined conceptual schemata, but instead using mechanisms, inspired from natural language, that enable each agent to develop a repertoire of grounded categories and labels for these categories and to negotiate their use and semantics with other agents. The communication system, as well as its semantics, is hence emergent and adaptive instead of predefined.

Abstract: Developmental systems (1/11) are provided with an autotelic mechanism for driving their development. An autotelic component (1) in the system uses a mapping mechanism (2) to produce an output based on a set of inputs. The mapping mechanism (2) implements a mapping that is dependent upon a state associated therewith. The content of the state is changed by a learning/repair module (3) based on interactions between the system and the environment, and so reflects knowledge gained by this component as a result of its experience. The autotelic component (1) monitors its own performance with reference to the level of a set of one or more challenge parameters whose levels quantify the complexity of different parameters relating to the autotelic component (1). The state associated with the mapping mechanism 2 is altered depending upon the component's performance as evaluated during the monitoring. A controller (5) controls the levels of the challenge parameters.

Abstract: A storage unit stores data representative of one or several sound sources and a listener of said sound sources. These data comprise position data corresponding to respective positions of the sound sources and the listener. An interface is provided for enabling a user to select the listener or a sound source and to control a change in the position data corresponding to the selected listener or sound source. A constraint solver means changes, in response to the position data change controlled by the user, at least some of the position data corresponding to the element(s), among the listener and the sound sources, other than said selected listener or sound source, in accordance with predetermined constraints. The system further comprises a command generator for delivering control data exploitable by a music spatialisation unit as a function of the position data corresponding to the sound sources and the listener.

Abstract: An autonomous system comprises an agent, constituting the behavioural identity of a robot, and a supporting device that is a physical installation which can implement the agent. The agent has a state which has a controlling influence on, and changes during, system operation. The agent's state is distinguishable from, and isolatable from, the permanent parts of the supporting device. The supporting device comprises hardware, software components for operating the hardware, software components for maintaining the agent environment and non-changing parts of the agent implementation, and software components for reading/writing the isolable agent state. An extraction module can extract the agent state and transform it into a representation for transmission across a network. An inserting module can insert a received agent state into the supporting device so as to embody a received agent on that supporting device.

Abstract: A method of extracting features characterising objects consists in discriminating an object from a context, the discriminating step comprising, determining the context consisting of at least two objects, choosing a topic object from the context, deriving feature sets of the topic object and the other objects in the context, judging whether there is at least one distinctive feature set in the feature sets, deriving a new feature set if there is not a distinctive feature set amongst the feature sets, and registering one of the distinctive feature sets as an outcome; and restarting the discriminating step when there exists an object which is not discriminated from the other objects. This method represents a selectionist approach to the characterisation of objects, driven by a discrimination task. There is also an apparatus putting this method into practice and a system for autonomous development of a common vocabulary by a plurality of agents incorporating the method and/or apparatus.