Optimizing Strategy Parameters in a Game Bot

Optimizing Strategy Parameters in a Game Bot
A. Fernández-Ares, A.M. Mora, J.J. Merelo,
P. García-Sánchez, and C.M. Fernandes
viernes 10 de junio de 2011

Agenda
Introduction
Problem description
Baseline: GoogleBot
First Approach: AresBot
Second Approach: GeneBot
Conclusions


Introduction
Google AI Challenge
Planet Wars Game


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

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


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


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


Operation GeneBot

Using intergalactic techniques from planet Ares to
evolve AresBot: a Genetic Algorithm!
Gene: parameters array
2-Tournament
BLX-alpha crossover
Ellitism


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


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...)


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


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.


Thanks!
(and questions, I guess)


Optimizing Strategy Parameters in a Game Bot

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