Abstract
Variation tendency of situation of an interaction system combined by multi intelligent agent is pushed forward by their respective decision process, pure probability and evidence theory are not satisfying available tools for this type of prediction. For these predictions, game theory can provide preferable decision and recognition, and is a tool of Process Planning. By combining influence diagrams with game theory, we proposed a new architecture—Bayesian Game Model for decision support to enhance capability of the awareness and prediction for a complex situation caused by multi interactive intelligent agents. In this paper, we discuss Bayesian game architecture and the related algorithm for a situation evolution under the circumstance of multi-source information fusion containing multi intelligent agents. Compared with other high level information fusion models containing decision makers and environmental status, this method can provide better decision effect. By combining with game theory, the proposed method provide a Joint probability distribution for whole situation in the form of Bayesian game and make the decision maker’s game strategies to be reflected in situation evolution.
This work is partially supported by NSF Grant #2003168 to H. Simpson and CNSF Grant #9972988 to M. King.
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.
References
Steinberg, A., Bowman, C., White Jr., F.E.: Revisions to the JDL data fusion model. In: Proceedings of SPIE AeroSense (Sensor Fusion: Architectures, Algorithms, and Applications III), Orlando, FL, vol. 3719, pp. 430–441 (April 1999)
Murphy, K.P.: Dynamic Bayesian Networks: Representation, Inference and Learning. Ph.D. thesis, University of California, Berkeley (2002)
SuzÃc, R.: Representation and recognition of uncertain enemy policies using statistical models. In: Proceedings of the NATO RTO Symposium on Military Data and Information Fusion, Prague, Czech Republic (October 2003)
Cottle, R.W., Pang, J.-S., Stone, R.E.: The Linear Complementarity Problem. Academic Press, Boston (1992)
Myerson, R.B.: Game Theory: Analysis of Conflict. Harvard University Press, Cambridge (1991)
Harsanyi, J.C.: Games with incomplete information played by Bayesian players. Management Science 14, 3, 5, 7, 159–182, 320–334, 486–502 (1967-1968)
von Stengel, B.: Computing Equilibrium for two-person games. In: Aumann, R.J., Hart, S. (eds.) Handbook of Game Theory with Economic Applications. Handbooks in Economics, vol. 3, pp. 1723–1759. Elsevier Science, Amsterdam (2002)
Brynielsson, J., Arnborg, S.: Refinements of the command and control game component. In: Proceedings of the Eighth International Conference on Information Fusion (FUSION 2005), Philadelphia, PA (July 2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Guo, C., Zhong, L., Liu, T. (2011). A Study on Game Theory in Decision Interaction for Multi Intelligent Agents Based on Information Fusion. In: Wu, Y. (eds) Computing and Intelligent Systems. ICCIC 2011. Communications in Computer and Information Science, vol 233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24010-2_60
Download citation
DOI: https://doi.org/10.1007/978-3-642-24010-2_60
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24009-6
Online ISBN: 978-3-642-24010-2
eBook Packages: Computer ScienceComputer Science (R0)