A new deductive approach to planning
New Generation Computing
Approximate reasoning about actions in presence of sensing and incomplete information
ILPS '97 Proceedings of the 1997 international symposium on Logic programming
Artificial Intelligence - Special issue: artificial intelligence 40 years later
Reasoning about noisy sensors and effectors in the situation calculus
Artificial Intelligence
Inferring Implicit State Knowledge and Plans with Sensing Actions
KI '01 Proceedings of the Joint German/Austrian Conference on AI: Advances in Artificial Intelligence
Planning with Sensing for a Mobile Robot
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
ESP: a logic of only-knowing, noisy sensing and acting
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
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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Ignoring the noise of physical sensors and effectors has always been a crucial barrier towards the application of high-level, cognitive robotics to real robots. We present a method of solving planning problems with noisy actions. The approach builds on the Fluent Calculus as a standard first-order solution to the Frame Problem. To model noise, a formal notion of uncertainty is incorporated into the axiomatization of state update and knowledge update. The formalism provides the theoretical underpinnings of an extension of the action programming language FLUX. Using constraints on real-valued intervals to encode noise, our system allows to solve planning problems for noisy sensors and effectors.