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Artificial intelligence in combinatorial game design

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Many new board games are designed each year, ranging from the unplayable to the truly exceptional. For each successful design there are untold numbers of failures; game design is something of an art. Players generally agree on some basic properties that indicate the quality and viability of a game, however these properties have remained subjective and open to interpretation.

This talk summarises my recent PhD studies in which a number of such quality criteria are precisely defined and automatically measured through self-play, in order to estimate the likelihood that a given game will be of interest to human players. Further, it is demonstrated that this information can be used to direct an automated search for new high quality games using genetic programming techniques.

This talk is part of the Inference Group series.

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