Un Mécanisme Constructiviste d'Apprentissage Automatique d'Anticipations pour des Agents Artificiels Situés

This research is characterized, first, by a theoretical discussion on the concept of autonomous agent, based on elements taken from the Situated AI and the Affective AI paradigms. Secondly, this thesis presents the problem of learning world models, providing a bibliographic review regarding some related works. From these discussions, the CAES architecture and the CALM mechanism are presented. The CAES (Coupled Agent-Environment System) is an architecture for describing systems based on the agent-environment dichotomy. It defines the agent and the environment as two partially open systems, in dynamic coupling. The agent is composed of two sub-systems, mind and body, following the principles of situativity and intrinsic motivation. CALM (Constructivist Learning Anticipatory Mechanism) is based on the constructivist approach to Artificial Intelligence. It allows a situated agent to build a model of the world in environments partially deterministic and partially observable in the form of Partially Observable and Factored Markov Decision Process (FPOMDP). The model of the world is constructed and used for the agent to define a policy for action in order to improve its own performance.Cette recherche se caractérise, premièrement, par une discussion théorique sur le concept d'agent autonome, basée sur des éléments issus des paradigmes de l'Intelligence Artificielle Située et de l'Intelligence Artificielle Affective. Ensuite, cette thèse présente le problème de l'apprentissage de modèles du monde, en passant en revue la littérature concernant les travaux qui s'y rapportent. À partir de ces discussions, l'architecture CAES et le mécanisme CALM sont présentés. CAES (Coupled Agent-Environment System) constitue une architecture pour décrire des systèmes basés sur la dichotomie agent-environnement. Il définit l'agent et l'environnement comme deux systèmes partiellement ouverts, en couplage dynamique. L'agent, à son tour, est composé de deux sous-systèmes, l'esprit et le corps, suivant les principes de la situativité et de la motivation intrinsèque. CALM (Constructivist Anticipatory Learning Mechanism) est un mécanisme d'apprentissage fondé sur l'approche constructiviste de l'Intelligence Artificielle. Il permet à un agent situé de construire un modèle du monde dans des environnements partiellement observables et partiellement déterministes, sous la forme d'un processus de décision markovien partiellement observable et factorisé (FPOMDP). Le modèle du monde construit est ensuite utilisé pour que l'agent puisse définir une politique d'action visant à améliorer sa propre performance

Integrating Active Perception with an Autonomous Robot Architecture

Today's robotics applications require complex, real-time, high-bandwidth sensor systems. Although many such systems have been developed, integrating them into an autonomous agent architecture remains an area of active research. We have integrated an active stereo vision system with an autonomous agent architecture using a system of perceptual memory. Perceptual memory is an important class of memory because it is designed for the "behavior-based" portion of the agent's architecture, and not the deliberative portion. This memory maintains current and recent task-dependent perceptual information, as well as expectations about the agent's immediate environment. Our system of perceptual memory is composed of visual primitives from our stereo system, called proximity spaces. Each proximity space represents a virtual fovea or locus of the agent's attention. As an application, we present a robot that uses our system of perceptual memory and proximity spaces to "attend to" multiple humans in a..

Integrating active perception with an autonomous robot architecture

Today’s robotics applications require complex, real-time, high-bandwidth sensor systems. Although many such systems have been developed [12][14][17][10], integrating them into an autonomous agent architecture remains an area of active research. We will discuss how active perception systems can be integrated with agent architectures to perform complex tasks. We present an active stereo vision system integrated with a multi-tier agent architecture onboard a mobile robot. This robot “attends to ” multiple humans in a complex and unstructured indoor environment