Artificial intelligence and mathematical theory of computation
Artificial Intelligence
Approximate reasoning about actions in presence of sensing and incomplete information
ILPS '97 Proceedings of the 1997 international symposium on Logic programming
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
Logic programming for robot control
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
What is planning in the presence of sensing?
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Local Conditional High-Level Robot Programs
LPAR '01 Proceedings of the Artificial Intelligence on Logic for Programming
Iterated belief change in the situation calculus
Artificial Intelligence
Handling sequential observations in intelligent surveillance
SUM'11 Proceedings of the 5th international conference on Scalable uncertainty management
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In this paper, we consider the projection task (determining what does or does not hold after performing a sequence of actions) in a general setting where a solution to the frame problem may or may not be available, and where online information from sensors may or may not be applicable. We formally characterize the projection task for actions theories of this sort, and show how a generalized form of regression produces correct answers whenever it can be used. We characterize conditions on action theories, sequences of actions, and sensing information that are sufficient to guarantee that regression can be used, and present a provably correct regressionbased procedure in Prolog for performing the task under these conditions.