The Markov assumption: formalization and impact

Authors:
Alexander Bochman
Affiliations:
Computer Science Department, Holon Institute of Technology, Israel
Venue:
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Year:
2013

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Abstract

We provide both a semantic interpretation and logical (inferential) characterization of the Markov principle that underlies the main action theories in AI. This principle will be shown to constitute a nonmonotonic assumption that justifies the actual restrictions on action descriptions in these theories, as well as constraints on allowable queries. It will be shown also that the well-known regression principle is a consequence of the Markov assumption, and it is valid also for non-deterministic domains.