Autonomous Robots
Learning discrete probability distributions with a multi-resolution binary tree
IDEAL'06 Proceedings of the 7th international conference on Intelligent Data Engineering and Automated Learning
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This paper shows a Bayesian framework for fuse information. Using this framework we present a robotic system, based on two processing units. The system is used for the development of a task, done by an autonomous agent, arranged in an environment with uncertainty. This agent interacts with the world and is able to detect, only using its sensor readings, any failure of its sensorial system. Even it can continue working properly while discarding the readings obtained by the erroneous sensor/s. A security unit is also provided to make the system even more robust. The Bayesian Units brings up a formalism where implicitly, using probabilities, we work with uncertainly. Some experimental data are provided to validate the correctness of this approach.