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Abstract

This study uses a real-coded representation to encode discrete strategies for playing a simple grid based game. This representation is able to adapt itself on the fly to the local situation in the game. This adaptability makes the representation particularly suitable for finding good play strategies which, in turn, permit us to explore biases in the play strategies and even to compare instances of the game for difficulty. Variability in the best results achieved by the solver can be used to gauge difficulty, while the shape of the distribution of best results can indicate how interesting an instance is. Results indicate that the variety and combination of affordances produce instances of the game with varying degrees of anticipated difficulty and interestingness, and confirm that the solver can be used to evaluate the quality of different affordance combinations for producing good game instances. Design principles may also be discovered through post hoc analysis of instances with high or low average best score.

Item Type:	Conference or Workshop Item (Paper)
Authors/Creators:	Ashlock, D and Montgomery, EJ
Keywords:	search based procedural content generation, puzzles, games, adaptive generative representation, self-avoiding walks
Journal or Publication Title:	Proceedings of the 2019 IEEE Congress on Evolutionary Computation
Publisher:	Institute of Electrical and Electronics Engineers
Copyright Information:	Copyright 2019 IEEE
Related URLs:	Google Scholar: Montgomery, EJ UTAS Profile: Montgomery, EJ
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