Towards Action Model Learning for Player Modeling

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@article{krishnan2021towards, 
  title={Towards Action Model Learning for Player Modeling}, 
  volume={16}, 
  url={https://ojs.aaai.org/index.php/AIIDE/article/view/7436}, 
  DOI={10.1609/aiide.v16i1.7436}, 
  abstractNote={Player modeling attempts to create a computational model which accurately approximates a player’s behavior in a game. Most player modeling techniques rely on domain knowledge and are not transferable across games. Additionally, player models do not currently yield any explanatory insight about a player’s cognitive processes, such as the creation and refinement of mental models. In this paper, we present our findings with using &lt;em&gt;action model learning&lt;/em&gt; (AML), in which an action model is learned given data in the form of a play trace, to learn a player model in a domain-agnostic manner. We demonstrate the utility of this model by introducing a technique to quantitatively estimate how well a player understands the mechanics of a game. We evaluate an existing AML algorithm (FAMA) for player modeling and develop a novel algorithm called Blackout that is inspired by player cognition. We compare Blackout with FAMA using the puzzle game Sokoban and show that Blackout generates better player models.}, 
  number={1}, 
  journal={Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment}, 
  author={Krishnan, Abhijeet and Williams, Aaron and Martens, Chris}, 
  year={2021}, 
  month={Apr.}, 
  pages={238-244} 
}