Artificial Intelligence
Representing incomplete knowledge in abductive logic programming
ILPS '93 Proceedings of the 1993 international symposium on Logic programming
Generalized update: belief change in dynamic settings
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Representing and reasoning about concurrent actions with abductive logic programs
Annals of Mathematics and Artificial Intelligence
JELIA '00 Proceedings of the European Workshop on Logics in Artificial Intelligence
Metatheory of actions: Beyond consistency
Artificial Intelligence
Elaborating domain descriptions
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
The complexity of belief update
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Updating action domain descriptions
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Updating action domain descriptions
Artificial Intelligence
Journal of Artificial Intelligence Research
Artificial Intelligence
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This paper presents a formal and computational methodology for incorporation of new knowledge into knowledge bases about actions and changes. We employ Gelfond and Lifschitz' action description language A to describe domains of actions. The knowledge bases on domains of actions are defined and obtained by a new translation from domain descriptions in A into abductive normal logic programs, where a time dimension is incorporated. The knowledge bases are shown to be both sound and complete with respect to their domain descriptions. In particular, we propose a possible causes approach (PCA) to belief update based on the slogan: What is believed is what is explained. A possible cause of new knowledge consists of abduced occurrences of actions and value propositions about the initial state of the domain of actions, that would allow to derive the new knowledge. We show how to compute possible causes with abductive logic programming, and present some techniques to improve search efficiency. We use examples to compare our possible causes approach with Ginsberg's possible worlds approach (PWA) and Winslett's possible models approach (PMA).