Abstract
This paper addresses the problem of working with uncertain or incomplete information in multiagent systems. When we work with real life systems (like a robotic agent) we normally use models to “insert” our knowledge to the robot. Nevertheless, any model of a real phenomenon will always be incomplete due to the existence of unknown, hidden variables that will influence the phenomenon, causing the model and the phenomenon to have different behavioral patterns. In this paper we provide a new intelligent Bayesian agent architecture oriented towards Bayesian robotics that provides a framework for developing intelligent agent applications using Bayesian theory. This architecture allows the programmer to use a common framework for developing agents that needs to work with uncertain or incomplete information, using one data type, join probabilistic distributions, which allows to use a common exchanging uncertainty assessments in multi-agent systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Thrun, S.: Towards Programming Tools for Robots that Integrate Probabilistic Computation and Learning ICRA, pp. 306–312 (2000)
Park, S., Pfenning, F., Thrun, S.: A probabilistic language based upon sampling functions SIGPLAN, vol. 40, pp. 171–182. ACM, New York (2005)
Lebeltel, O., Bessière, P., Diard, J., Mazer, E.: Bayesian Robot Programming Auton. Robots, vol. 16, pp. 49–79. Kluwer Academic Publishers, Dordrecht (2004)
Chalkiadakis, G., Boutilier, C.: Coordination in multiagent reinforcement learning: a Bayesian approach. In: AAMAS 2003: Proceedings of the second international joint conference on Autonomous agents and multiagent systems, pp. 709–716. ACM, New York (2003)
Chalkiadakis, G., Boutilier, C.: Bayesian Reinforcement Learning for Coalition Formation under Uncertainty. In: AAMAS 2004: Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 1090–1097. IEEE Computer Society, Los Alamitos (2005)
Chalkiadakis, G., Markakis, E., Boutilier, C.: Coalition formation under uncertainty: bargaining equilibria and the Bayesian core stability concept. In: AAMAS 2007: Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems, pp. 1–8. ACM, New York (2007)
Ueno, M., Okamoto, T.: Bayesian Agent in e-Learning ICALT, pp. 282–284 (2007)
Santos, E.R., Fagundes, M.S., Vicari, R.M.: An ontology-based approach to interoperability for Bayesian agents. In: AAMAS 2007: Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems, pp. 1–3. ACM, New York (2007)
Steels, L., Kaplan, F., McIntyre, A., Looveren, J.V.: Crucial factors in the origins of word-meaning. In: Wray, A. (ed.) The Transition to Language. Oxford University Press, Oxford (2002)
Florian, R.V.: Autonomous artificial intelligent agents Center of Cognitive and Neural Studies (CONEURAL) (2003), http://www.coneural.org/reports/Coneural-03-01.pdf
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Aznar, F., Pujol, M., Rizo, R. (2009). Autonomous Artificial Intelligent Agents for Bayesian Robotics. In: Corchado, J.M., Rodríguez, S., Llinas, J., Molina, J.M. (eds) International Symposium on Distributed Computing and Artificial Intelligence 2008 (DCAI 2008). Advances in Soft Computing, vol 50. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85863-8_30
Download citation
DOI: https://doi.org/10.1007/978-3-540-85863-8_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85862-1
Online ISBN: 978-3-540-85863-8
eBook Packages: EngineeringEngineering (R0)