Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Automatic computer game balancing: a reinforcement learning approach
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
A Theory of Fun for Game Design
A Theory of Fun for Game Design
Creativity and Artificial Intelligence: A Conceptual Blending Approach
Creativity and Artificial Intelligence: A Conceptual Blending Approach
Automatically acquiring domain knowledge for adaptive game AI using evolutionary learning
IAAI'05 Proceedings of the 17th conference on Innovative applications of artificial intelligence - Volume 3
Towards capturing and enhancing entertainment in computer games
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
A Neural-Evolutionary Model for Case-Based Planning in Real Time Strategy Games
IEA/AIE '09 Proceedings of the 22nd International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: Next-Generation Applied Intelligence
Case Indexing Using PSO and ANN in Real Time Strategy Games
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Case learning and indexing in real time strategy games
ICNC'09 Proceedings of the 5th international conference on Natural computation
Automatic generation of 2-antwars players with genetic programming
EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part I
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Fun in computer games depends on many factors. While some factors like uniqueness and humor can only be measured by human subjects, in a strategical game, the rule system is an important and measurable factor. Classics like chess and GO have a millennia-old story of success, based on clever rule design. They only have a few rules, are relatively easy to understand, but still they have myriads of possibilities. Testing the deepness of a rule-set is very hard, especially for a rule system as complex as in a classic strategic computer game. It is necessary, though, to ensure prolonged gaming fun. In our approach, we use artificial intelligence (AI) to simulate hours of beta-testing the given rules, tweaking the rules to provide more game-playing fun and deepness. To avoid making the AI a mirror of its programmer's gaming preferences, we not only evolved the AI with a genetic algorithm, but also used three fundamentally different AI paradigms to find boring loopholes, inefficient game mechanisms and, last but not least, complex erroneous behavior.