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Active Diagnosis Through Information-Lookahead Planning

Mauricio Araya 1 Olivier Buffet 1 Vincent Thomas 1
1 MAIA - Autonomous intelligent machine
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
Abstract : We consider challenging active diagnosis problems, that is, when smart exploration is needed to acquire information about a hidden target variable. Classical approaches rely on information-greedy strategies or ad-hoc algorithms for specific classes of problems. We propose to model this problem using the generic ρPOMDP formalism, which leads to an information-lookahead planning strategy, where the objective is to gather information-based reward. We empirically evaluate this approach on the Rock Diagnosis problem, which is a variation of the well-known Rock Sample problem, showing that we obtain better performance results than information-greedy techniques.
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https://hal.inria.fr/hal-00907288
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Submitted on : Thursday, November 21, 2013 - 9:39:42 AM
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Mauricio Araya, Olivier Buffet, Vincent Thomas. Active Diagnosis Through Information-Lookahead Planning. 8èmes Journées Francophones sur la Planification, la Décision et l'Apprentissage pour la conduite de systèmes, Jul 2013, Lille, France. ⟨hal-00907288⟩

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