Submodular Surrogates for Value of Information

How should we gather information to make effective decisions? A classical answer to this fundamental problem is given by the decision-theoretic value of information. Unfortunately, optimizing this objective is intractable, and myopic (greedy) approximations are known to perform poorly. In this paper, we introduce DIRECT, an efficient yet near-optimal algorithm for nonmyopically optimizing value of information. Crucially, DIRECT uses a novel surrogate objective that is (1) aligned with the value of information problem; (2) efficient to evaluate and (3) adaptive submodular. This latter property enables us to utilize efficient greedy optimization while providing strong approximation guarantees. We extensively demonstrate the utility of our approach on three diverse case-studies: active learning for interactive content search, optimizing value of information in conservation management, and touch-based robotic localization. On the latter application, we demonstrate DIRECT in closed-loop...

• Type: Journal
• Year: 2015
• Where: AAAI
• Authors: Yuxin Chen, Shervin Javdani, Amin Karbasi, J. Andrew Bagnell, Siddhartha S. Srinivasa, Andreas Krause
• Keywords: AAAI 2015, Intelligent Agents