TY - JOUR AU - Krishnan, Abhijeet AU - Williams, Aaron AU - Martens, Chris PY - 2021/04/12 Y2 - 2024/03/28 TI - Towards Action Model Learning for Player Modeling JF - Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment JA - AIIDE VL - 16 IS - 1 SE - Poster Papers DO - 10.1609/aiide.v16i1.7436 UR - https://ojs.aaai.org/index.php/AIIDE/article/view/7436 SP - 238-244 AB - <p class="abstract">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 <em>action model learning</em> (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.</p> ER -