Obtaining a Bayesian map for data fusion and failure detection under uncertainty
IEA/AIE'2005 Proceedings of the 18th international conference on Innovations in Applied Artificial Intelligence
Bayesian space conceptualization and place classification for semantic maps in mobile robotics
Robotics and Autonomous Systems
BROA: A Bayesian Robotic Agents Architecture
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
Using Gaussian Processes in Bayesian Robot Programming
IWANN '09 Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part II: Distributed Computing, Artificial Intelligence, Bioinformatics, Soft Computing, and Ambient Assisted Living
Further Steps towards Driver Modeling According to the Bayesian Programming Approach
ICDHM '09 Proceedings of the 2nd International Conference on Digital Human Modeling: Held as Part of HCI International 2009
Probabilistic and Empirical Grounded Modeling of Agents in (Partial) Cooperative Traffic Scenarios
ICDHM '09 Proceedings of the 2nd International Conference on Digital Human Modeling: Held as Part of HCI International 2009
Bayesian network-based behavior control for Skilligent robots
ICRA'09 Proceedings of the 2009 IEEE international conference on Robotics and Automation
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Brain-coupled interaction for semi-autonomous navigation of an assistive robot
Robotics and Autonomous Systems
Bayesian emotions: developing an interface for robot/human communication
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
Robot security and failure detection using bayesian fusion
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
3D robot mapping: combining active and non active sensors in a probabilistic framework
CAEPIA'05 Proceedings of the 11th Spanish association conference on Current Topics in Artificial Intelligence
Specifying complex systems with bayesian programming. an alife application
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part I
A cognitive model for autonomous agents based on bayesian programming
BVAI'05 Proceedings of the First international conference on Brain, Vision, and Artificial Intelligence
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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We propose a new method to program robots based on Bayesian inference and learning. It is called BRP for Bayesian Robot Programming. 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 combination, 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 BRP 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.