Abstract
In this paper, a multi-objective optimal interception problem is proposed and solved using a Multi-Objective Evolutionary Algorithm. The traditional setting of an interception engagement between pursuer and evader is targeted either at minimizing a miss distance for a given interception duration or at minimizing an interception time for a given miss distance. Such a setting overlooks an important aspect — the purpose of launching the evader in the first place. Naturally, the evader seeks to evade the pursuer (by keeping away from it), but what about hitting its target? In contrast with the traditional setting, in this paper a multi-objective game is played between a pursuer and an evader. The pursuer aims at keeping a minimum final distance between itself and the evader, which it attempts to keep away from its target. The evader, on the other hand, aims at coming as close as possible to a predefined target while keeping as far away as possible from the pursuer. Both players (pursuer and evader) utilize neural net controllers that evolve during the proposed evolutionary optimization. The game is shown to involve very interesting issues related to the decision-making process while the dilemmas of both opponents are taken into consideration.
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
Glizer, V.Y., Shinar, J.: On the structure of a class of time-optimal trajectories. Optimal Control Applications and Methods 14(4), 271–279 (1993)
Lozovanu, D., Solomon, D., Zelikovsky, A.: Multiobjective games and determining pareto-nash equilibria. Buletinul Academiei de Ştiinţe a Republicii Moldova. Matematica 3(49), 115–122 (2005)
Somasundaram, K., Baras, J.: Pareto nash replies for multi-objective games. The Institute for Systems Research ISR, TR 2009-4 (2008)
Zeleny, M.: Games with multiple payoffs. International Journal of game theory 4(4), 179–191 (1975)
Contini, B., Olivetti, I., Milano, C.: A decision model under uncertainty with multiple payoffs. In: Theory of Games; Techniques and Applications, pp. 50–63 (1966)
Li, S.: Interactive strategy sets in multiple payoff games. Computers & Industrial Engineering 37(3), 613–630 (1999)
Choi, H., Ryu, H., Tahk, M., Bang, H.: A co-evolutionary method for pursuit evasion games with non-zero lethal radii. Engineering Optimization 36(1), 19–36 (2004)
Brown, M., An, B., Kiekintveld, C., Ordóñez, F., Tambe, M.: Multi-objective optimization for security games. In: Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2012, vol. 2, pp. 863–870. International Foundation for Autonomous Agents and Multi-agent Systems, Richland (2012)
Sakawa, M.: Genetic Algorithms and Fuzzy Multiobjective Optimization. Kluwer, Boston (2002)
Avigad, G., Eisenstadt, E., Cohen, M.: Optimal strategies for multi objective games and their search by evolutionary multi objective optimization. In: 2011 IEEE Conference on Computational Intelligence and Games, CIG, pp. 166–173. IEEE (2011)
Avigad, G., Branke, J.: Embedded evolutionary multi-objective optimization for worst case robustness. In: Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pp. 617–624. ACM (2008)
Avigad, G., Eisenstadt, E., Glizer, V.Y.: Evolving a Pareto Front for an Optimal Bi-objective Robust Interception Problem with Imperfect Information. In: Schütze, O., Coello Coello, C.A., Tantar, A.-A., Tantar, E., Bouvry, P., Del Moral, P., Legrand, P. (eds.) EVOLVE - A Bridge Between Probability. AISC, vol. 175, pp. 121–136. Springer, Heidelberg (2012)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Avigad, G., Eisenstadt, E., Glizer, V.Y. (2013). Solution of Multi-objective Min-Max and Max-Min Games by Evolution. In: Purshouse, R.C., Fleming, P.J., Fonseca, C.M., Greco, S., Shaw, J. (eds) Evolutionary Multi-Criterion Optimization. EMO 2013. Lecture Notes in Computer Science, vol 7811. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37140-0_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-37140-0_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37139-4
Online ISBN: 978-3-642-37140-0
eBook Packages: Computer ScienceComputer Science (R0)