http://cogprints.org/1670/
Bayesian robot Programming
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.
Lebeltel, Olivier
Bessiere, Pierre
Diard, Julien
Mazer, Emmanuel
Artificial Intelligence
Robotics
Statistical Models
Olivier
Lebeltel
Pierre
Bessiere
Julien
Diard
Emmanuel
Mazer