IMO3: Interactive Multi-Objective Off-Policy Optimization

International Joint Conference on Artificial Intelligence(2022)

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摘要
Most real-world optimization problems have multiple objectives. A system designer needs to find a policy that trades off these objectives to reach a desired operating point. This problem has been studied extensively in the setting of known objective functions. We consider a more practical but challenging setting of unknown objective functions. In industry, this problem is mostly approached with online A/B testing, which is often costly and inefficient. As an alternative, we propose interactive multi-objective off-policy optimization (IMO^3). The key idea in our approach is to interact with a system designer using policies evaluated in an off-policy fashion to uncover which policy maximizes her unknown utility function. We theoretically show that IMO^3 identifies a near-optimal policy with high probability, depending on the amount of feedback from the designer and training data for off-policy estimation. We demonstrate its effectiveness empirically on multiple multi-objective optimization problems.
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关键词
Machine Learning: Experimental Methodology,Machine Learning: Optimisation,Uncertainty in AI: Decision and Utility Theory,Knowledge Representation and Reasoning: Preference Modelling and Preference-Based Reasoning,Humans and AI: Human-AI Collaboration
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