Heuristic anytime approaches to stochastic decision processes

Authors:
Joaquín L. Fernández;Rafael Sanz;Reid G. Simmons;Amador R. Diéguez
Affiliations:
Department of System Engineering, University of Vigo, Vigo, Spain 36200;Department of System Engineering, University of Vigo, Vigo, Spain 36200;Computer Science Department, Carnegie Mellon University, Pittsburgh, USA 15214;Department of System Engineering, University of Vigo, Vigo, Spain 36200
Venue:
Journal of Heuristics
Year:
2006

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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.