Case-Based Planning and Execution for Real-Time Strategy Games
ICCBR '07 Proceedings of the 7th international conference on Case-Based Reasoning: Case-Based Reasoning Research and Development
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
A modular parametric architecture for the TORCS racing engine
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
Dynamic strategies in a real-time strategy game
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Learning to win: case-based plan selection in a real-time strategy game
ICCBR'05 Proceedings of the 6th international conference on Case-Based Reasoning Research and Development
Designing and evolving an unreal Tournament™ 2004 expert bot
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
Evolving the strategies of agents for the ANTS game
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
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This work describes an evolutionary algorithm (EA) for evolving the constants, weights and probabilities of a rule-based decision engine of a bot designed to play the Planet Wars game. The evaluation of the individuals is based on the result of some non-deterministic combats, whose outcome depends on random draws as well as the enemy action, and is thus noisy. This noisy fitness is addressed in the EA and then, its effects are deeply analysed in the experimental section. The conclusions shows that reducing randomness via repeated combats and re-evaluations reduces the effect of the noisy fitness, making then the EA an effective approach for solving the problem.