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 cogni-tivism 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.