title: Bayesian robot Programming creator: Lebeltel, Olivier creator: Bessiere, Pierre creator: Diard, Julien creator: Mazer, Emmanuel subject: Artificial Intelligence subject: Robotics subject: Statistical Models description: We propose a new method to program robots based on Bayesian inference and learning. The capacities of this programming method are demonstrated through a succession of increasingly complex experiments. Starting from the learning of simple reactive behaviors, we present instances of behavior combinations, sensor fusion, hierarchical behavior composition, situation recognition and temporal sequencing. This series of experiments comprises the steps in the incremental development of a complex robot program. The advantages and drawbacks of this approach are discussed along with these different experiments and summed up as a conclusion. These different robotics programs may be seen as an illustration of probabilistic programming applicable whenever one must deal with problems based on uncertain or incomplete knowledge. The scope of possible applications is obviously much broader than robotics. date: 2000 type: Departmental Technical Report type: NonPeerReviewed format: application/postscript identifier: http://cogprints.org/1670/1/Lebeltel2000.ps format: application/pdf identifier: http://cogprints.org/1670/5/Lebeltel2000.pdf identifier: Lebeltel, Olivier and Bessiere, Pierre and Diard, Julien and Mazer, Emmanuel (2000) Bayesian robot Programming. [Departmental Technical Report] relation: http://cogprints.org/1670/