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In the General Video Game Playing competitions of the last years, Monte-Carlo tree search as well as Evolutionary Algorithm based controllers have been successful. However, both approaches have certain weaknesses, suggesting that certain hybrids could outperform both. We envision and experimentally compare several types of hybrids of two basic approaches,(More)
Game balancing is an important part of the (computer) game design process, in which designers adapt a game prototype so that the resulting gameplay is as entertaining as possible. In industry, the evaluation of a game is often based on costly playtests with human players. It suggests itself to automate this process using artificial players for the(More)
The workshop is a joint event where undergraduate and graduate students can present their scientific work. Additionally, we aim to provide the students with useful feedback on their papers, their presentations, and possible future research directions. To this end, we encouraged detailed reviews of the papers from our program committee. More importantly, we(More)
Game balancing is a recurring problem that currently requires a lot of manual work, usually following a game designer's intuition or rules-of-thumb. To what extent can or should the balancing process be automated? We establish a process model that integrates both manual and automated balancing approaches. Artificial agents are employed to automatically(More)
In this paper, we propose a novel approach (SAPEO) to support the survival selection process in multi-objective evolutionary algorithms with surrogate models-it dynamically chooses individuals to evaluate exactly based on the model uncertainty and the distinct-ness of the population. We introduce variants that differ in terms of the risk they allow when(More)
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