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Bayesian robot Programming

Lebeltel, Olivier and Bessiere, Pierre and Diard, Julien and Mazer, Emmanuel (2000) Bayesian robot Programming. [Departmental Technical Report]

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Abstract

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.

Item Type:Departmental Technical Report
Keywords:Robotics Bayes Pereception Inference Action
Subjects:Computer Science > Artificial Intelligence
Computer Science > Robotics
Computer Science > Statistical Models
ID Code:1670
Deposited By:Bessiere, Pierre
Deposited On:05 Jul 2001
Last Modified:11 Mar 2011 08:54

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