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.

References

  1. Bacchus, F., Halpern, J., and Levesque, H. (1999). Reasoning about noisy sensors and effectors in the situation calculus. Artificial Intelligence, 111(1-2):171-208.
  2. Baier, J. A. and Pinto, J. (2003). Planning under uncertainty as Golog programs. J. Exp. Theor. Artif. Intell., 15(4):383-405.
  3. Boutilier, C. and Goldszmidt, M. (1996). The frame problem and Bayesian network action representations. In Proceedings of the Canadian Conference on Artificial Intelligence (CSCSI).
  4. Boutilier, C. and Poole, D. (1996). Computing optimal policies for partially observable decision processes using compact representations. In Proceedings of the 13-th National Conference on Artificial Intelligence (AAAI), pages 1168-1175, Portland, Oregon, USA.
  5. Boutillier, C., Dean, T., and Hanks, S. (1999). DecisionTheoretic Planning: Structural Assumptions and Computational Leverage. Journal of Artificial Intelligence Research, 11:1-94.
  6. de Salvo Braz, R., Amir, E., and Roth, D. (2007). Lifted first-order probabilistic inference. In Getoor, L. and Taskar, B., editors, Introduction to Statistical Relational Learning. MIT Press.
  7. Gardiol, N. H. and Kaelbling, L. P. (2004). Envelope-based planning in relational MDPs. In Advances in Neural Information Processing Systems 16 (NIPS-03), Vancouver, CA.
  8. Grosskreutz, H. and Lakemeyer, G. (2000). Turning highlevel plans into robot programs in uncertain domains. In Proceedings of the European Conference on Artificial Intelligence (ECAI).
  9. Jin, Y. and Thielscher, M. (2004). Representing beliefs in the fluent calculus. In Proceedings of the European Conference on Artificial Intelligence (ECAI), pages 823-827, Valencia, Spain. IOS Press.
  10. Kushmerick, N., Hanks, S., and Weld, D. S. (1995). An algorithm for probabilistic planning. Artificial Intelligence, 76(1-2):239-286.
  11. Levesque, H. (2005). Planning with loops. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), Edinburgh, Scotland.
  12. Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, San Mateo, CA.
  13. Pearl, J. (2000). Causality: Models, Reasoning, and Inference. Cambridge University Press.
  14. Poole, D. and Zhang, N. L. (2003). Exploiting contextual independence in probabilistic inference. Journal of Artificial Intelligence Research, 18:263-313.
  15. Reiter, R. (2001a). Knowledge in Action. MIT Press.
  16. Reiter, R. (2001b). On knowledge-based programming with sensing in the situation calculus. ACM Transactions on Computational Logic, 2(4):433-457.
  17. Shanahan, M. and Witkowski, M. (2000). High-level robot control through logic. In Proceedings of the International Workshop on Agent Theories Architectures and Languages (ATAL), volume 1986 of LNCS, pages 104-121, Boston, MA. Springer.
  18. Thielscher, M. (1999). From situation calculus to fluent calculus: State update axioms as a solution to the inferential frame problem. Artificial Intelligence, 111(1- 2):277-299.
  19. Thielscher, M. (2000). Representing the knowledge of a robot. In Proceedings of the International Conference on Principles of Knowledge Representation and Reasoning (KR), pages 109-120, Breckenridge, CO. Morgan Kaufmann.
  20. Thielscher, M. (2005a). FLUX: A logic programming method for reasoning agents. Theory and Practice of Logic Programming, 5(4-5):533-565.
  21. Thielscher, M. (2005b). Reasoning Robots: The Art and Science of Programming Robotic Agents, volume 33 of Applied Logic Series. Kluwer.
  22. Tran, N. and Baral, C. (2004). Encoding probabilistic causal model in probabilistic action language. In Proceedings of the 19-th National Conference on Artificial Intelligence (AAAI), pages 305-310.
Download


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