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How should we gather information to make effective deci- sions? A classical answer to this fundamental problem is given by the decision-theoretic value of information. Unfor- tunately, 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 informa- tion. 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 lat- ter property enables us to utilize efficient greedy optimiza- tion 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 man- agement, and touch-based robotic localization. On the latter application, we demonstrate DIRECT in closed-loop on an actual robotic platform.
Submodular Surrogates for Value of Information Y. Chen, S. Javdani, A. Karbasi, J. A. Bagnell, S. Srinivasa, A. KrauseIn Proc. Conference on Artificial Intelligence (AAAI), 2015
Bibtex Entry:
	author = {Yuxin Chen and Shervin Javdani and Amin Karbasi and James Andrew Bagnell and Siddhartha Srinivasa and Andreas Krause},
	booktitle = {Proc. Conference on Artificial Intelligence (AAAI)},
	month = {January},
	title = {Submodular Surrogates for Value of Information},
	year = {2015}}