---
title: "The Markov Assumption: Formalization and Impact"
authors:
  - "Alexander Bochman"
doi: ""
url: "https://ijcai.org/proceedings/2013/122"
pdf: "https://ijcai.org/proceedings/2013/0122.pdf"
venue: "Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence"
year: 2013
pages: "782-787"
track: ""
keywords:
---

# The Markov Assumption: Formalization and Impact

Alexander Bochman

*Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence*, pages 782-787.

## 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.