Multi-Robot Coordination and Competition Using Mixed Integer and Linear Programs
PhD Thesis, Tech. Report, CMU-RI-TR-04-65, Robotics Institute, Carnegie Mellon University, August, 2004
Abstract
Markov decision processes (MDPs) and partially observable Markov decision processes (POMDPs) are preferred methods representing complex uncertain dynamic systems and determining an optimal control policy to manipulate the system in the desired manner. Until recently, controlling a system composed of multiple agents using the MDP methodology was impossible due to an exponential increase in the size of the MDP problem representation. In this thesis, a novel method for solving large multi-agent MDP systems is presented which avoids this exponential size increase while still providing optimal policies for a large class of useful problems. This thesis provides the following main contributions:
BibTeX
@phdthesis{Bererton-2004-8996,author = {Curt Bererton},
title = {Multi-Robot Coordination and Competition Using Mixed Integer and Linear Programs},
year = {2004},
month = {August},
school = {Carnegie Mellon University},
address = {Pittsburgh, PA},
number = {CMU-RI-TR-04-65},
}
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