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Computational Intelligence and Games: Creating, Balancing, Learning to Play


Mike Preuss (Westfälische Wilhelms-Universität Münster)
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We are currently seeing the redefinition of the term Game AI. Modern Game AI is understood to be much more encompassing than just controlling opponent AI. A huge amount of research currently deals with supporting game creation by means of Procedural Content Generation. A closely linked and largely untackled problem is (semi-)automated balancing: if we let algorithms modify game content, we also need the tools to make a game playable (again). This in turn requires general, adaptable AI components that can represent players, as featured in the General Video Game AI (GVGAI) environment and competitions. We can find such tools in Computational Intelligence/Machine Learning: Evolutionary Algorithms (EA) and Monte-Carlo tree search (MCTS) are flexible, easy-to-use methods that heavily rely on controlled randomness.
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Session Type: 
Code / Platforms / Tools
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Time slot: 
2016-04-20 16:00
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