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Mécanismes d’apprentissage développemental et intrinsèquement motivés en intelligence artificielle : étude des mécanismes d'intégration de l'espace environnemental

Abstract : This thesis is a part of the IDEAL project (Implementing DEvelopmentAl Learning) funded by the Agence Nationale de la Recherche (ANR). The ability of perceiving, memorizing and interpreting the surrounding environment is a vital ability found in numerous living beings. This ability allows them to generate context adapted behaviors, or escaping from a predator that escape from their sensory system. The objective of this thesis consists in implementing such a capacity in artificial agents. We propose a theoretical model that allows artificial agent to generate a usable knowledge of elements that compose its environment and a structure able to characterize the structure of surrounding space. This model is based on the sensorimotor contingency theory, and implements a form of intrinsic motivation. Indeed, this model begin with a set of indivisible structures, called interactions, that characterize the interaction possibilities between the agent and its environment. The learning is developmental and emerges from the interaction that occurs between the agent and the environment, without the need of any external intervention (like reward). Our model propose a set of mechanisms that allow to organize and exploit emerging knowledge in order to generate behaviors. We propose implementations of our model to demonstrate the emerging knowledge based on agent-environment interaction, and behaviors that can emerge from this knowledge
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Simon Gay. Mécanismes d’apprentissage développemental et intrinsèquement motivés en intelligence artificielle : étude des mécanismes d'intégration de l'espace environnemental. Autre [cs.OH]. Université Claude Bernard - Lyon I, 2014. Français. ⟨NNT : 2014LYO10300⟩. ⟨tel-01166721⟩

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