Computational complexity of planning and approximate planning in the presence of incompleteness
Artificial Intelligence
Formalizing sensing actions—a transition function based approach
Artificial Intelligence
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
What is planning in the presence of sensing?
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
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Many domains that we wish to model and reason about are subject to change due to the execution of actions. Representing and reasoning about dynamic domains play an important role in AI because they serve as a fundamental basis for many applications, including planning, diagnosis, and modelling. Research in the field focuses on the development of formalisms for reasoning about action and change (RAC). Such a formalism normally consists of two components: a representation language and a reasoning mechanism. It has been well known in the field that two criteria for the success of a formalism are its expressiveness and efficiency. The former means that the representation language is rich enough to describe complicated domains; the latter implies the reasoning mechanism is computationally efficient, making it possible to be implemented on a machine. Besides, in daily life, we have to face the absence of complete information and thus any formalism should take this matter into account.