Optimal sensing via multi-armed bandit relaxations in mixed observability domains

Mikko Lauri, Risto Ritala

    Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

    5 Citations (Scopus)
    32 Downloads (Pure)

    Abstract

    Sequential decision making under uncertainty is studied in a mixed observability domain. The goal is to maximize the amount of information obtained on a partially observable stochastic process under constraints imposed by a fully observable internal state. An upper bound for the optimal value function is derived by relaxing constraints. We identify conditions under which the relaxed problem is a multi-armed bandit whose optimal policy is easily computable. The upper bound is applied to prune the search space in the original problem, and the effect on solution quality is assessed via simulation experiments. Empirical results show effective pruning of the search space in a target monitoring domain.

    Original languageEnglish
    Title of host publication2015 IEEE International Conference on Robotics and Automation (ICRA), 26-30 May 2015, Seattle, WA
    Pages4807-4812
    Number of pages6
    Volume2015-June
    DOIs
    Publication statusPublished - 29 Jun 2015
    Publication typeA4 Article in a conference publication
    EventIEEE International Conference on Robotics and Automation -
    Duration: 1 Jan 19001 Jan 2000

    Conference

    ConferenceIEEE International Conference on Robotics and Automation
    Period1/01/001/01/00

    Publication forum classification

    • Publication forum level 1

    ASJC Scopus subject areas

    • Software
    • Artificial Intelligence
    • Control and Systems Engineering
    • Electrical and Electronic Engineering

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