Exploitation of User's Preferences in Reinforcement Learning Decision Support Systems

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Authors
  1. Bélanger, M.
  2. Berger, J.
  3. Perron, J.
  4. Hogan, J.
  5. Moulin, B.
Corporate Authors
Defence R&D Canada - Valcartier, Valcartier QUE (CAN)
Abstract
A system called COLMAS (COordination Learning in Multi-Agent Systems) has been developed to investigate how the integration of realistic geosimulation and reinforcement learning might support a decision-maker in the context of cooperative patrolling. COLMAS is a model-driven automated decision support system combining geosimulation and reinforcement learning to compute near optional solutions. Building upon this hybrid approach, this paper proposes an extended framework to constructively incorporate user preferences providing mixed-initiative generation of further trusted and validated solutions. The proposed approach integrated the user's preferences in COLMAS by automatically extracting user's preferred solution.
Report Number
DRDC-VALCARTIER-SL-2009-169 — Scientific Literature
Date of publication
01 Jul 2008
Number of Pages
10
DSTKIM No
CA032814
CANDIS No
532044
Format(s):
Document Image stored on Optical Disk

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