ECAI '92 Proceedings of the 10th European conference on Artificial intelligence
Artificial Intelligence
A Computing Procedure for Quantification Theory
Journal of the ACM (JACM)
The logic of knowledge bases
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Knowlege in action: logical foundations for specifying and implementing dynamical systems
Generalizing DPLL and satisfiability for equalities
Information and Computation
A Semantical Account of Progression in the Presence of Defaults
Conceptual Modeling: Foundations and Applications
Computing strongest necessary and weakest sufficient conditions of first-order formulas
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
On first-order definability and computability of progression for local-effect actions and beyond
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
How to progress a database III
Artificial Intelligence
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In a seminal paper, Lin and Reiter introduced the notion of progression of basic action theories. Unfortunately, progression is second-order in general. Recently, Liu and Lakemeyer improve on earlier results and show that for the local-effect and normal actions case, progression is computable but may lead to an exponential blow-up. Nevertheless, they show that for certain kinds of expressive first-order knowledge bases with disjunctive information, called proper, it is efficient. However, answering queries about the resulting state is still undecidable. In this paper, we continue this line of research and extend proper KBs to include functions. We prove that their progression wrt local-effect, normal actions, and range-restricted theories, is first-order definable and efficiently computable. We then provide a new logically sound and complete decision procedure for certain kinds of queries.