This document summarizes research on optimizing strategy parameters for a game bot. It describes a baseline GoogleBot strategy, an initial AresBot strategy using manually tuned parameters, and a GeneBot strategy that uses a genetic algorithm to evolve better parameters for AresBot. GeneBot was able to win more games and do so faster on average than the baseline strategies, showing that evolutionary computation can optimize bot parameters. Future work could explore parallelization, multi-objective optimization, and genetic programming to further improve the bot's strategies.

1. Optimizing Strategy Parameters in a Game Bot
A. Fernández-Ares, A.M. Mora, J.J. Merelo,
P. García-Sánchez, and C.M. Fernandes
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2. Agenda
Introduction
Problem description
Baseline: GoogleBot
First Approach: AresBot
Second Approach: GeneBot
Conclusions
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3. Introduction
Google AI Challenge
Planet Wars Game
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4. viernes 10 de junio de 2011

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 game
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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 target
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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 attacking
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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 planet
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9. Operation GeneBot
Using intergalactic techniques from planet Ares to
evolve AresBot: a Genetic Algorithm!
Gene: parameters array
2-Tournament
BLX-alpha crossover
Ellitism
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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 win
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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...)
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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 22 458 100
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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.
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14. Thanks!
(and questions, I guess)
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