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Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology IEEE EC1 Generating War Game Strategies Using A Genetic.

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Presentation on theme: "Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology IEEE EC1 Generating War Game Strategies Using A Genetic."— Presentation transcript:

1 Intelligent Database Systems Lab 國立雲林科技大學 National Yunlin University of Science and Technology IEEE EC1 Generating War Game Strategies Using A Genetic Algorithm1 Advisor : Dr. Hsu Presenter : Jia-Hao Yang Author :Timothy E. Revello, Robert McCartney

2 Intelligent Database Systems Lab N.Y.U.S.T. I. M. IEEE EC 2 Outline  Motivation  Objective  Introduction  Method (GA)  Experiment  Conclusion

3 Intelligent Database Systems Lab N.Y.U.S.T. I. M. IEEE EC 3 Motivation  Unlike most games which have fixed rules, the rules for war games can contain uncertainty.  Although there are many approach about game AI have been developed, but all based on the game structure and rules being fixed.

4 Intelligent Database Systems Lab N.Y.U.S.T. I. M. IEEE EC 4 Objective  In this paper we explore the use of genetic algorithms for generating war game strategies.

5 Intelligent Database Systems Lab N.Y.U.S.T. I. M. IEEE EC 5 Introduction  Gaming is used extensively by the military and business for decision making…etc.  So game play has been a topic of research in computational science for some time. ─ EX : tic-tac-toe, eight tile puzzles, Checkers and chess, war game.  One of the challenges currently faced is to better address uncertainty. ─ Variables working. ex: what moves the opposition will make ─ Rules of the game themselves. ex: what is required to win are generally not known. 653 847 12

6 Intelligent Database Systems Lab N.Y.U.S.T. I. M. IEEE EC 6 Method  The use of a GA for generating war game strategies using a naval blockade scenario as a tool.  Naval forces are modeled using a time step simulation. Blue win criteria

7 Intelligent Database Systems Lab N.Y.U.S.T. I. M. IEEE EC 7 Method  GA configuration : encoding Parent selection Crossover Mutation population Include 396 bits long, consisting of 12 groups of 33 bits/per ship group, which include the type, number, and movement of ship groups 1.0 0.001 5000 Fitness function Include 30 individuals random Criterion method Count method elitism Criterion method Count method

8 Intelligent Database Systems Lab N.Y.U.S.T. I. M. IEEE EC 8 Experiment  The result showed that the most robust configuration was the use of elitism with the count method. V1V4M4 The best score 95.9799.7599.86 Average time step 2.925.724.45 Total purchase cost 28.810.75.8

9 Intelligent Database Systems Lab N.Y.U.S.T. I. M. IEEE EC 9 Conclusion  During this investigation we gave demonstrated that a GA can be used to generate strategies for a war game in which the rules are uncertain.


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