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RI | Publications | Multi-Robot Coordination and Competition Using Mixed Integer and Linear Programs
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Text only version of this site
Multi-Robot Coordination and Competition Using Mixed Integer and Linear Programs
C. Bererton
doctoral dissertation, tech. report CMU-RI-TR-04-65, Robotics Institute, Carnegie Mellon University, August, 2004.
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| 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:
| Notes |
Associated center: VASC
Number of pages: 183
| Text Reference |
C. Bererton, Multi-Robot Coordination and Competition Using Mixed Integer and Linear Programs, doctoral dissertation, tech. report CMU-RI-TR-04-65, Robotics Institute, Carnegie Mellon University, August, 2004.
| BibTeX Reference |
@phdthesis{Bererton_2004_4871,
author = "Curt Bererton",
title = "Multi-Robot Coordination and Competition Using Mixed Integer and Linear Programs",
school = "Robotics Institute, Carnegie Mellon University",
month = "August",
year = "2004",
address = "Pittsburgh, PA"
}