Can we infer player behavior tendencies from a player's decision-making data? Integrating Theory of Mind to Player Modeling

TL;DR

This paper introduces an inverse Bayesian inference approach to deduce player behavior tendencies from decision-making data, integrating Theory of Mind into player modeling for better understanding of cognition.

Contribution

It proposes a novel method combining inverse Bayesian inference with cognitive modeling to infer player behavior tendencies from decision data.

Findings

Successfully inferred behavior tendency parameters from synthetic game data.

Demonstrated the model's ability to interpret player decision-making and underlying tendencies.

Applicable to understanding player cognition in game AI systems.

Abstract

Game AI systems need the theory of mind, which is the humanistic ability to infer others' mental models, preferences, and intent. Such systems would enable inferring players' behavior tendencies that contribute to the variations in their decision-making behaviors. To that end, in this paper, we propose the use of inverse Bayesian inference to infer behavior tendencies given a descriptive cognitive model of a player's decision making. The model embeds behavior tendencies as weight parameters in a player's decision-making. Inferences on such parameters provide intuitive interpretations about a player's cognition while making in-game decisions. We illustrate the use of inverse Bayesian inference with synthetically generated data in a game called \textit{BoomTown} developed by Gallup. We use the proposed model to infer a player's behavior tendencies for moving decisions on a game map. Our results indicate that our model is able to infer these parameters towards uncovering not only a player's decision making but also their behavior tendencies for making such decisions.