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What are the aesthetics of platform games and what makes a platform level engaging, challenging and/or frustrating? We attempt to answer such questions through mining a large-set of crowd-sourced gameplay data of a clone of the classic platform game Super Mario Bros. The data consists of 40 short game levels that differ along six key level design parameters. Collectively, these levels are played 1560 times over the Internet and the perceived experience is annotated by experiment participants via self-reported ranking (pairwise preferences). Given the wealth of this crowd-sourced data, as all details about players in-game behaviour are logged, the problem becomes one of extracting meaningful numerical features at the appropriate level of abstraction for the construction of generic computational models of player experience and, thereby, game aesthetics. We explore dissimilar types of features, including direct measurements of event and item frequencies, and features constructed through frequent sequence mining and go through an in-depth analysis of the interrelationship between level content, players behavioural patterns and reported experience. Furthermore, the fusion of the extracted features allows us to predict reported player experience with a high accuracy even from short game segments. In addition to advancing our insight on the factors that contribute to platform game aesthetics, the results are useful for the personalisation of game experience via automatic game adaptation.
Computational Intelligence and AI in Games, IEEE Transactions on (Volume:PP , Issue: 99 )