On bootstrapping sensori-motor patterns for a constructivist learning system in continuous environments

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.