Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Blondie24: playing at the edge of AI
Blondie24: playing at the edge of AI
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Competitive Environments Evolve Better Solutions for Complex Tasks
Proceedings of the 5th International Conference on Genetic Algorithms
New methods for competitive coevolution
Evolutionary Computation
Evolving coordinated spatial tactics for autonomous entities using influence maps
CIG'09 Proceedings of the 5th international conference on Computational Intelligence and Games
PADS: enhancing gaming experience using profile-based adaptive difficulty system
Proceedings of the 5th ACM SIGGRAPH Symposium on Video Games
Game team balancing by using particle swarm optimization
Knowledge-Based Systems
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We attack the problem of game balancing by using a coevolutionary algorithm to explore the space of possible game strategies and counter strategies. We define balanced games as games which have no single dominating strategy. Balanced games are more fun and provide a more interesting strategy space for players to explore. However, proving that a game is balanced mathematically may not be possible and industry commonly uses extensive and expensive human testing to balance games. We show how a coevolutionary algorithm can be used to test game balance and use the publicly available continuous state, capture-the-flag CaST game as our testbed. Our results show that we can use coevolution to highlight game imbalances in CaST and provide intuition towards balancing this game. This aids in eliminating dominating strategies, thus making the game more interesting as players must constantly adapt to opponent strategies.