Artificial Intelligence
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Tractable reasoning in first-order knowledge bases with disjunctive information
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
On the progression of situation calculus basic action theories: resolving a 10-year-old conjecture
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Variable forgetting in reasoning about knowledge
Journal of Artificial Intelligence Research
Property persistence in the situation calculus
Artificial Intelligence
A description logic based situation calculus
Annals of Mathematics and Artificial Intelligence
LPAR'10 Proceedings of the 17th international conference on Logic for programming, artificial intelligence, and reasoning
On progression and query evaluation in first-order knowledge bases with function symbols
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
On the progression of knowledge in the situation calculus
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
How to progress a database III
Artificial Intelligence
A classification of first-order progressable action theories in situation calculus
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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In a seminal paper, Lin and Reiter introduced the notion of progression for basic action theories in the situation calculus. Unfortunately, progression is not first-order definable in general. Recently, Vassos, Lakemeyer, and Levesque showed that in case actions have only local effects, progression is first-order representable. However, they could show computability of the first-order representation only for a restricted class. Also, their proofs were quite involved. In this paper, we present a result stronger than theirs that for local-effect actions, progression is always first-order definable and computable. We give a very simple proof for this via the concept of forgetting. We also show first-order definability and computability results for a class of knowledge bases and actions with non-local effects. Moreover, for a certain class of local-effect actions and knowledge bases for representing disjunctive information, we show that progression is not only first-order definable but also efficiently computable.