An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Extending Graphplan to handle uncertainty and sensing actions
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
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
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
Probabilistic complex actions in GOLOG
Fundamenta Informaticae
Semantics for a useful fragment of the situation calculus
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Reasoning about Movement in Two-Dimensions
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
Reasoning about continuous uncertainty in the situation calculus
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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When reasoning about actions and sensors in realistic domains, the ability to cope with uncertainty often plays an essential role. Among the approaches dealing with uncertainty, the one by Bacchus, Halpern and Levesque, which uses the situation calculus, is perhaps the most expressive. However, there are still some open issues. For example, it remains unclear what an agent's knowledge base would actually look like. The formalism also requires second-order logic to represent uncertain beliefs, yet a first-order representation clearly seems preferable. In this paper we show how these issues can be addressed by incorporating noisy sensors and actions into an existing logic of only-knowing.