INTEGRATING REASONING ABOUT ACTIONS AND BAYESIAN NETWORKS

Yves Martin, Michael Thielscher

2010

Abstract

According to the paradigm of Cognitive Robotics (Reiter, 2001a), intelligent, autonomous agents interacting with an incompletely known world need to reason logically about the effects of their actions and sensor information they acquire over time. In realistic settings, both the effect of actions and sensor data are subject to errors. A cognitive agent can cope with these uncertainties by maintaining probabilistic beliefs about the state of world. In this paper, we show a formalism to represent probabilistic beliefs about states of the world and how these beliefs change in the course of actions. Additionally, we propose an extension to a logic programming framework, the agent programming language FLUX, to actually infer this probabilistic knowledge for agents. Using associated Bayesian networks allows the agents to maintain a single and compact probabilistic knowledge state throughout the execution of an action sequence.

Paper Citation

in Harvard Style

Martin Y. and Thielscher M. (2010). INTEGRATING REASONING ABOUT ACTIONS AND BAYESIAN NETWORKS . In Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-674-021-4, pages 298-304. DOI: 10.5220/0002724602980304

in Bibtex Style

@conference{icaart10,
author={Yves Martin and Michael Thielscher},
title={INTEGRATING REASONING ABOUT ACTIONS AND BAYESIAN NETWORKS},
booktitle={Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2010},
pages={298-304},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002724602980304},
isbn={978-989-674-021-4},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - INTEGRATING REASONING ABOUT ACTIONS AND BAYESIAN NETWORKS
SN - 978-989-674-021-4
AU - Martin Y.
AU - Thielscher M.
PY - 2010
SP - 298
EP - 304
DO - 10.5220/0002724602980304