Game Design for Eliciting Distinguishable Behavior
This addresses the need for personalized human-interacting machine learning systems by improving trait inference, though it is incremental as it builds on existing methods like prospect theory and MDPs.
The paper tackles the problem of designing games that can infer latent psychological traits from human behavior by formulating it as a mutual information maximization problem, and shows that their designed games outperform manually-designed ones by a large margin in distinguishing players with different traits.
The ability to inferring latent psychological traits from human behavior is key to developing personalized human-interacting machine learning systems. Approaches to infer such traits range from surveys to manually-constructed experiments and games. However, these traditional games are limited because they are typically designed based on heuristics. In this paper, we formulate the task of designing \emph{behavior diagnostic games} that elicit distinguishable behavior as a mutual information maximization problem, which can be solved by optimizing a variational lower bound. Our framework is instantiated by using prospect theory to model varying player traits, and Markov Decision Processes to parameterize the games. We validate our approach empirically, showing that our designed games can successfully distinguish among players with different traits, outperforming manually-designed ones by a large margin.