Personalized Decision Supports based on Theory of Mind Modeling and Explainable Reinforcement Learning
2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)(2023)
摘要
In this paper, we propose a novel personalized decision support system that
combines Theory of Mind (ToM) modeling and explainable Reinforcement Learning
(XRL) to provide effective and interpretable interventions. Our method
leverages DRL to provide expert action recommendations while incorporating ToM
modeling to understand users' mental states and predict their future actions,
enabling appropriate timing for intervention. To explain interventions, we use
counterfactual explanations based on RL's feature importance and users' ToM
model structure. Our proposed system generates accurate and personalized
interventions that are easily interpretable by end-users. We demonstrate the
effectiveness of our approach through a series of crowd-sourcing experiments in
a simulated team decision-making task, where our system outperforms control
baselines in terms of task performance. Our proposed approach is agnostic to
task environment and RL model structure, therefore has the potential to be
generalized to a wide range of applications.
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关键词
Decision Support,Theory Model,Theory Of Mind,Reinforcement Learning Theory,Effective Interventions,Mental State,Task Performance,Important Characteristics,Decision Support System,Deep Reinforcement Learning,Decision-making Task,Expert Recommendations,Recommendations For Action,Deep Learning,Bayesian Model,Maximum Likelihood Estimation,Human Behavior,Human Participants,Team Sports,Intelligence Agencies,Explanations For Decisions,Calling For Help,Task Scenarios,Markov Decision Process,Bayesian Network Structure,Long-term Compliance,Specific Decisions,State-action Pair,Round Of The Game,Online Update
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