Heuristic anytime approaches to stochastic decision processes |
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Authors: | Joaquín L Fernández Rafael Sanz Reid G Simmons Amador R Diéguez |
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Institution: | (1) Department of System Engineering, University of Vigo, Campus Lagoas-Marcosende, 36200 Vigo, Spain;(2) Computer Science Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15214, USA |
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Abstract: | This paper proposes a set of methods for solving stochastic decision problems modeled as partially observable Markov decision
processes (POMDPs). This approach (Real Time Heuristic Decision System, RT-HDS) is based on the use of prediction methods combined with several existing heuristic decision algorithms. The prediction process
is one of tree creation. The value function for the last step uses some of the classic heuristic decision methods. To illustrate
how this approach works, comparative results of different algorithms with a variety of simple and complex benchmark problems
are reported. The algorithm has also been tested in a mobile robot supervision architecture. |
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Keywords: | Partially observable Markov decision process POMDP Decision Systems Planning Heuristic algorithms |
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