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Game Design for Eliciting Distinguishable Behavior
Fan Yang · Liu Leqi · Yifan Wu · Zachary Lipton · Pradeep Ravikumar · Tom M Mitchell · William Cohen

Tue Dec 10 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #84

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 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.

Author Information

Fan Yang (Carnegie Mellon University)
Liu Leqi (Carnegie Mellon University)
Yifan Wu (Carnegie Mellon University)
Zachary Lipton (Carnegie Mellon University)
Pradeep Ravikumar (Carnegie Mellon University)
Tom M Mitchell (Carnegie Mellon University)
William Cohen (Google AI)

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