Bayesian Modeling and Reasoning for Real World Robotics: Basics and Examples

voir basilic : http://emotion.inrialpes.fr/bibemotion/2004/BSBTCD04/ address: Dagstuhl (DE) editor: Iida, F. and Pfeifer, R. and Steels, L. and Kuniyoshi, Y. publisher: Springer-VerlagCognition and Reasoning with uncertain and partial knowledge is prob- ably the biggest challenge for autonomous mobile robotics. Previous robotics sys- tems based on a purely logical or geometrical paradigm are limited in their ability to deal with partial or uncertain knowledge, adaptation to new environments and noisy sensors. Representing knowledge as a joint probability distribution increases the possibility for robotics systems to increase their quality of perception on their environment and helps them to take the right actions towards a more realistic and robust behavior. Dealing with uncertainty is thus a ma jor challenge for robotics in a real and unconstrained environment. Here, we propose a new formalism and method- ology called Bayesian Programming which aims at the design of efficient robotics systems evolving in a real and uncontrolled environment. This original formalism will be exemplified by two interesting experiments where robots are driven by a Bayesian Program (BP). These examples represents situations where the robot can sense only a small part of its global environment using noisy sensors. The second fact about these environments is they cannot be constrained so that to ease the control of the robot