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Yannick Bourrier, Francis Jambon, Catherine Garbay, Vanda Luengo
No abstract is available for this record.
ubiant, Lyon, France, Sébastien Mazac, Frédéric Armetta, Salima Hassas
The theory of cognitive development from Jean Piaget (1923) is a constructivist perspective of learning that has substantially influenced cognitive science domain.Indeed it seems that constructivism is a possible trail in order to overcome the limitations of classical techniques stemming from cognitivism or connectionism and create autonomous agents, fitted with strong adaptation ability within their environment, modelled on biological organisms.Potential applications concern intelligent agents in interaction with a complex environment, with objectives that cannot be predefined.There are numerous interesting works in developmental robotics going in this direction.In this work we investigate the application of these principles to a close domain: Ambient intelligence, which is extremely challenging but which also presents interesting aspects to exploit, like the participation of human users.From the perspective of a constructivist theory, the learning agent has to build a representation of the world that relies on the learning of sensori-motor patterns starting from its own experience only.This step is difficult to set up for systems evolving in continuous environments, using raw data from sensors without a priori modelling, primarily because they face a bootstrap problem.In this paper we address this particular issue and propose a decentralized approach based on a multi-agent framework, where the system's representations are constructed through a self-organization process that handles the dynamics between experience discretization and learning.
Vladimir Jotsov
No abstract is available for this record.
Daniel D. Corkill, Victor Lesser
Distributed problem solving networks provide an interesting application area for high-level network coordination through the use of organizational structuring. We describe a decentralized approach to the coordination of these networks that relies on each node making sophisticated local activity decisions. Each node is guided by a high-level strategic plan for cooperation among nodes in the network and must balance its own perceptions of appropriate problems solving activity with activities deemed important by other nodes. The high-level strategic plan, which is a form of meta-level control, is represented as a network organizational structure specifying in a general way the information and control relationships among the nodes. In addition to its application to Distributed Artificial Intelligence, this research has implications for organizing and controlling complex knowledge-based systems that involve semi-autonomous problem solving agents.