Towards Action Model Learning for Player Modeling
This work addresses the need for more generalizable and insightful player modeling in games, though it is incremental as it builds on existing AML techniques.
The paper tackles the problem of creating transferable and explanatory player models by applying action model learning (AML) to learn domain-agnostic models from play traces, resulting in a novel algorithm called Blackout that outperforms an existing AML method (FAMA) in generating better player models for the puzzle game Sokoban.
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 action model learning (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.