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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:	Refereed Conference Paper
Keywords:	search based procedural content generation, puzzles, games, adaptive generative representation, self-avoiding walks
Research Division:	Information and Computing Sciences
Research Group:	Machine learning
Research Field:	Neural networks
Objective Division:	Expanding Knowledge
Objective Group:	Expanding knowledge
Objective Field:	Expanding knowledge in the information and computing sciences
UTAS Author:	Montgomery, J (Dr James Montgomery)
ID Code:	132157
Year Published:	2019
Deposited By:	Information and Communication Technology
Deposited On:	2019-04-24
Last Modified:	2020-05-15
Downloads:	13 View Download Statistics