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Deployment of robotic agents in uncertain environments: game theoretic rules and simulation studies

Published online by Cambridge University Press:  27 February 2009

Kaan Egilmez
Affiliation:
Laboratory for Manufacturing and Productivity, Massachusetts Institute of Technology, Cambridge, MA 02139, U.S.A.
Steven H. Kim
Affiliation:
Laboratory for Manufacturing and Productivity, Massachusetts Institute of Technology, Cambridge, MA 02139, U.S.A.

Abstract

The coordination of intelligent, interacting, agents is rapidly gaining importance as such systems are deployed under diverse conditions. When robots are used in teams rather than as individuals, their coordination can become more critical for system performance than their individual capabilities. The deployment strategies and communication modes play an important role in the coordination of these teams.

This paper examines a Game Theoretic deployment approach to robotic teams in an unstructured environment. A simulation model is developed and used to compare the performance of gaming rules with a non-anticipatory deterministic deployment rule. The initial Game Theoretic rule can be enhanced to exhibit both locally and globally adaptive characteristics. The new rule outperforms both the deterministic algorithm and the straightforward game-theoretic rule. This is achieved by adapting to trends in local regions in the environment as well as anticipating global eventualities.

Type
Research Article
Copyright
Copyright © Cambridge University Press 1992

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References

Alexandris, N. A., Barney, G. C. and Harris, C. J. 1979 a. Multi-car lift systems analysis and design. Applied Mathematical Modelling 3, 269274.CrossRefGoogle Scholar
Alexandris, N. A., Barney, G. C. and Harris, C. J. 1979 b. Derivation of the mean highest reversal floor and expected number of stops in lift systems. Applied Mathematical Modelling 3, 275279.CrossRefGoogle Scholar
Alexandris, N. A., Barney, G. C. and Harris, C. J. 1981. Evaluation of the handling capacity of multi-car lift systems. Applied Mathematical Modelling 5, 4952.CrossRefGoogle Scholar
Aumann, R. J. 1974. Subjectivity and correlation in randomized strategies. Journal of Mathematical Economics 1, 6796.CrossRefGoogle Scholar
Aumann, R. J. 1987. Correlated equilibrium as an expression of bayesian rationality. Econometrica 55, 118.CrossRefGoogle Scholar
Berman, O. 1978. Dynamic positioning of mobile servers on networks, M.I.T. Alfred P. Sloan School of Management PhD Thesis.Google Scholar
Cheng, T. C. E. 1987. A simulation study of automated guided vehicle dispatching. Robotics and Computer Integrated Manufacturing 3, 335338.CrossRefGoogle Scholar
Denardo, E. V. and Fox, B. L. 1979. Shortest-route methods: 1. reaching, pruning and buckets. Operations Research 27, 161186.CrossRefGoogle Scholar
Dreyfus, S. E. 1969. An appraisal of some shortest-path algorithms. Operations Research 17, 395412.CrossRefGoogle Scholar
Egbelu, P. J. 1986. Pull versus push strategy for automated guided vehicle load movement in a batch manufacturing Journal of Manufacturing Systems 6, 209220.CrossRefGoogle Scholar
Egbelu, P. J. and Tanchoco, J. M. A. 1987. Characterizations of AGV dispatching rules. Automated Guided Vehicle Systems, pp 125141.Google Scholar
Franck, E. A. O. 1976. Implementing closest vehicle dispatching strategy on the hypercube model, M.I.T. Alfred P. Sloan School of Management, M.S. Thesis.Google Scholar
Kim, S. H. 1991. Coordination of multiagent systems through explicit valuation of action. Robotics and Computer-Integrated Manufacturing 8, 265291.CrossRefGoogle Scholar
Kim, K. H. and Roush, F. W. 1987. Team Theory, New York: Halfsted Press.Google Scholar
Larson, R. L. and Franck, E. A. 1976. Dispatching the units of emergency service systems using automatic vehicle location: a computer based Markov hypercube model. Technical report No. 2176. M.I.T. Innovative resource planning in urban public safety systems project.Google Scholar
Larson, R. C. 1978. Structural system models for locational decisions: an example using the hypercube queueing model. Operations research center technical report No. 145. M.I.T.Google Scholar
Marschak, J. and Radner, R. 1972. Economic Theory of Teams. New Haven: Yale University Press.Google Scholar
Nof, S. Y. and Rajan, V. N. 1990. Planning collaborative automation for flexible assembly. Techincal Paper MS90–841: 11th International Conference on Assembly Automation. Dearborn, MI.Google Scholar
Nof, S. Y. 1991. Game theoretic models for planning cooperative robotic work. Proceedings of the NSF Design and Manufacturing Systems Conference, Austin, TX.Google Scholar
Owen, Guillermo 1982. Game Theory, 2nd edition. New York: Academic Press.Google Scholar
Rapoport, A. 1970. N-Person Game Theory: Concepts and Applications. Ann Arbor, MI: The University of Michigan Press.Google Scholar
Tsai, J. J.-P., Metea, M. and Cesarone, J. 1990. A knowledge-based navigation scheme for autonomous land vehicles. Applied Artificial Intelligence. 4, 114.CrossRefGoogle Scholar
Williams, J. D. 1966. The Complete Strategyst, revised edition. New York: McGraw Hill Book Company.Google Scholar