The Markov Assumption: Formalization and Impact
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.
Alexander Bochman. 2013. The Markov Assumption: Formalization and Impact. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13. IJCAI, 782–787. https://ijcai.org/proceedings/2013/122
Bochman, A. (2013). The Markov Assumption: Formalization and Impact. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 (pp. 782–787). IJCAI. https://ijcai.org/proceedings/2013/122
@inproceedings{ijcai2013p122,
title = {The Markov Assumption: Formalization and Impact},
author = {Bochman, Alexander},
booktitle = {Proceedings of the Twenty-Third International Joint Conference on
Artificial Intelligence, {IJCAI-13}},
publisher = {AAAI Press / International Joint Conferences on Artificial Intelligence},
editor = {Francesca Rossi},
pages = {782--787},
year = {2013},
month = {8},
url = {https://ijcai.org/proceedings/2013/122},
}
TY - CPAPER TI - The Markov Assumption: Formalization and Impact AU - Bochman, Alexander T2 - Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, IJCAI-13 PB - AAAI Press / International Joint Conferences on Artificial Intelligence PY - 2013 SP - 782 EP - 787 UR - https://ijcai.org/proceedings/2013/122 ER -Copied
Machine-readable versions of this paper, for tools and AI agents:
