Game AI is difficult to program, especially as games are frequently changing due to updates from the designers and the evolving behavior of human players. It would be useful if AI agents were able to automatically learn to reason about their environment. A major part of the environment is geospatial information. An agent’s geospatial coordinates can suggest likelihoods of encountering important objects such as items or enemies, even when those objects are not in sight. Difficulties arise when these probabilities are not nicely demarcated into areas predefined and provided by the game API, creating the need to learn geospatial models automatically. This paper argues for models that divide game environments into discrete areas, proposes appropriate evaluation measures for such models, and tests a few clustering approaches on detailed creature sighting data extracted from a large number of players of a modern multi-player first-person shooter game. Two methods are shown to work better than simple baselines, demonstrating how these techniques can be used to automatically divide the game environment by its observed attributes.

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To Appear In The 2nd Knowledge Extraction from Games Workshop, 2019. AAAI.

@inproceedings{braylan:aaaiworkshop19, title={Discretization of Game Space by Environment Attributes}, author={Alexander Braylan and Risto Miikkulainen}, booktitle={The 2nd Knowledge Extraction from Games Workshop}, publisher={AAAI}, url="http://nn.cs.utexas.edu/?braylan:aaaiworkshop19", year={2019} }