Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Representing and reasoning with probabilistic knowledge: a logical approach to probabilities
Planning and control
Knowledge, probability, and adversaries
Journal of the ACM (JACM)
Probabilistic logic programming
Information and Computation
Reasoning about knowledge and probability
Journal of the ACM (JACM)
Reasoning about knowledge
An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
Reasoning about noisy sensors and effectors in the situation calculus
Artificial Intelligence
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Probabilistic Situation Calculus
Annals of Mathematics and Artificial Intelligence
AI '01 Proceedings of the 14th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Decision-Theoretic, High-Level Agent Programming in the Situation Calculus
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Knowledge, action, and the frame problem
Artificial Intelligence
Machine Learning
Bayesian Filtering for Location Estimation
IEEE Pervasive Computing
Event Modeling and Recognition Using Markov Logic Networks
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Unifying logical and statistical AI
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
ESP: a logic of only-knowing, noisy sensing and acting
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
BLOG: probabilistic models with unknown objects
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Reasoning about continuous processes
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Monitoring a complex physical system using a hybrid dynamic bayes net
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Action networks: a framework for reasoning about actions and change under uncertainty
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
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Among the many approaches for reasoning about degrees of belief in the presence of noisy sensing and acting, the logical account proposed by Bacchus, Halpern, and Levesque is perhaps the most expressive. While their formalism is quite general, it is restricted to fluents whose values are drawn from discrete countable domains, as opposed to the continuous domains seen in many robotic applications. In this paper, we show how this limitation in their approach can be lifted. By dealing seamlessly with both discrete distributions and continuous densities within a rich theory of action, we provide a very general logical specification of how belief should change after acting and sensing in complex noisy domains.