AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Weighing and Integrating Evidence for Stochastic Simulation in Bayesian Networks
UAI '89 Proceedings of the Fifth Annual Conference on Uncertainty in Artificial Intelligence
Machine Learning
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
MEBN: A language for first-order Bayesian knowledge bases
Artificial Intelligence
Sound and efficient inference with probabilistic and deterministic dependencies
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Lifted first-order belief propagation
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
First-order probabilistic inference
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Lifted first-order probabilistic inference
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
An importance sampling algorithm based on evidence pre-propagation
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Towards performing everyday manipulation activities
Robotics and Autonomous Systems
Probabilistic logics in expert systems: approaches, implementations, and applications
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part I
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Transactions on Large-Scale Data- and Knowledge-Centered Systems VI
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An autonomous robot system that is to act in a real-world environment is faced with the problem of having to deal with a high degree of both complexity as well as uncertainty. Therefore, robots should be equipped with a knowledge representation system that is able to soundly handle both aspects. In this paper, we thus introduce an architecture that provides a coupling between plan-based robot controllers and a probabilistic knowledge representation system based on recent developments in statistical relational learning, which possesses the required level of expressiveness and generality. We outline possible applications of the corresponding models in the context of robot control, discussing suitable representation formalisms, inference and learning methods as well as transparent extensions of a robot planning language that allow robot control programs to soundly integrate the results of probabilistic inference into their plan generation process.