Generating War Game Strategies Using A Genetic Algorithm 1

1. Generating War Game Strategies Using A Genetic Algorithm1 Advisor : Dr. Hsu Presenter : Jia-Hao Yang Author :Timothy E. Revello, Robert McCartney IEEE EC

2. Outline • Motivation • Objective • Introduction • Method (GA) • Experiment • Conclusion 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. IEEE EC

4. Objective • In this paper we explore the use of genetic algorithms for generating war game strategies. 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. 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 IEEE EC

7. Criterion method elitism Count method Criterion method random Count method Method • GA configuration : Include 396 bits long, consisting of 12 groups of 33 bits/per ship group, which include the type, number, and movement of ship groups encoding Include 30 individuals population Fitness function 5000 Parent selection Crossover 1.0 0.001 Mutation IEEE EC

8. Experiment • The result showed that the most robust configuration was the use of elitism with the count method. 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. IEEE EC