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# Optimizing Strategy Parameters in a Game Bot

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Using a GA to optimize a hand-coded bot to play Planet Wars game.

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### Optimizing Strategy Parameters in a Game Bot

1. 1. Optimizing Strategy Parameters in a Game Bot A. Fernández-Ares, A.M. Mora, J.J. Merelo, P. García-Sánchez, and C.M. Fernandesviernes 10 de junio de 2011
2. 2. Agenda Introduction Problem description Baseline: GoogleBot First Approach: AresBot Second Approach: GeneBot Conclusionsviernes 10 de junio de 2011
3. 3. Introduction Google AI Challenge Planet Wars Gameviernes 10 de junio de 2011
4. 4. viernes 10 de junio de 2011
5. 5. Problem description Actions Current Bot to status perform Restrictions RTS->Turn based game. A turn = 1 sec No memory to store knowledge about the game Current status: planets and ﬂeets Actions: a text ﬁle with actions to be read by the gameviernes 10 de junio de 2011
6. 6. Baseline: GoogleBot Included in the framework to use as a comparator of quality How does it work: Seeks the planet that hosts most ships (base) Target planet calculated using the ratio of growth-rate and number of ships Waits until the ﬂeet reachs the targetviernes 10 de junio de 2011
7. 7. First Approach: AresBot In each turn: Select the base according a score function (the rest are colonies) Select a target planet to attack For each colony: Reinforcement of base planet (tithe) or Contribute to attackingviernes 10 de junio de 2011
8. 8. AresBot parameters titheperc and titheprob: percentage of starships the bot sends/probability it happens. ωNS−DIS and ωGR: weight of the number of starships, distance to target from base and planet growth rate (used in the score function of target planet) poolperc and supportperc: percentage ships that the bot sends from the base planet or colonies to the target planet. supportprob: probability of sending extra ﬂeets from the colonies to the target planetviernes 10 de junio de 2011
9. 9. Operation GeneBot Using intergalactic techniques from planet Ares to evolve AresBot: a Genetic Algorithm! Gene: parameters array 2-Tournament BLX-alpha crossover Ellitismviernes 10 de junio de 2011
10. 10. Fitness Function Each individual runs in ﬁve relevant maps Ranked ﬁtness: LT: Number of games a bot wins if equal-> WT: number of turns to winviernes 10 de junio de 2011
11. 11. Experiments Population of 400 individuals 40 sec to evaluate each one, so just one evaluation to enter in the competition (4 hours per generation)->(yes, parallelism in future...)viernes 10 de junio de 2011
12. 12. Results titheperc titheprob ωNS−DIS ωGR poolperc supportperc supportprob AresBot 0,1 0,5 1 1 0,25 0,5 0,9 GeneBot 0,294 0,0389 0,316 0,844 0,727 0,822 0,579 Turns Victories Average and Std. Dev. Min Max AresBot 210 ± 130 43 1001 99 GeneBot 159 ± 75 22 458 100viernes 10 de junio de 2011
13. 13. Conclusions and Future Work GeneBot ended in 1454 position in the contest (36%). 1000 positions better than AresBot More work to do: Paralellism Multi-objective approachs Genetic Programming Stop when the bot acquire self-awareness, of course.viernes 10 de junio de 2011
14. 14. Thanks! (and questions, I guess)viernes 10 de junio de 2011
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