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PLANNING WITH CONTINUOUS RESOURCES IN AGENT SYSTEMS
by
Janusz Marecki
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(COMPUTER SCIENCE)
August 2008
Copyright 2008 Janusz Marecki
Object Description
| Title | Planning with continuous resources in agent systems |
| Author | Marecki, Janusz |
| Author email | marecki@usc.edu |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Computer Science |
| School | Viterbi School of Engineering |
| Date defended/completed | 2008-05-07 |
| Date submitted | 2008 |
| Restricted until | Unrestricted |
| Date published | 2008-06-19 |
| Advisor (committee chair) | Tambe, Milind |
| Advisor (committee member) |
Lesser, Victor Gratch, Jonathan Maheswaran, Rajiv Ordonez, Fernando |
| Abstract | My research concentrates on developing reasoning techniques for intelligent, autonomous agent systems. In particular, I focus on planning techniques for both single and multi-agent systems acting in uncertain domains. In modeling these domains, I consider two types of uncertainty: (i) the outcomes of agent actions are uncertain and (ii) the amount of resources consumed by agent actions is uncertain and only characterized by continuous probability density functions. Such rich domains, that range from the Mars rover exploration to the unmanned aerial surveillance to the automated disaster rescue operations are commonly modeled as continuous resource Markov decision processes (MDPs) that can then be solved in order to construct policies for agents acting in these domains.; This thesis addresses two major unresolved problems in continuous resource MDPs. First, they are very difficult to solve and existing algorithms are either fast, but make additional restrictive assumptions about the model, or do not introduce these assumptions but are very inefficient. Second, continuous resource MDP framework is not directly applicable to multi-agent systems and current approaches all discretize resource levels or assume deterministic resource consumption which automatically invalidates the formal solution quality guarantees. The goal of my thesis is to fundamentally alter this landscape in three contributions:; I first introduce CPH, a fast analytic algorithm for solving continuous resource MDPs. CPH solves the planning problems at hand by first approximating with a desired accuracy the probability distributions over the resource consumptions with phase-type distributions, which use exponential distributions as building blocks. It then uses value iteration to solve the resulting MDPs more efficiently than its closest competitor, and allows for a systematic trade-off of solution quality for speed.; Second, to improve the anytime performance of CPH and other continuous resource MDP solvers I introduce the DPFP algorithm. Rather than using value iteration to solve the problem at hand, DPFP performs a forward search in the corresponding dual space of cumulative distribution functions. In doing so, DPFP discriminates in its policy generation effort providing only approximate policies for regions of the state-space reachable with low probability yet it bounds the error that such approximation entails.; Third, I introduce CR-DEC-MDP, a framework for planning with continuous resources in multi-agent systems and propose two algorithms for solving CR-DEC-MDPs: The first algorithm (VFP) emphasizes scalability. It performs a series of policy iterations in order to quickly find a locally optimal policy. In contrast, the second algorithm (M-DPFP) stresses optimality; it allows for a systematic trade-off of solution quality for speed by using the concept of DPFP in a multiagent setting.; My results show up to three orders of magnitude speedups in solving single agent planning problems and up to one order of magnitude speedup in solving multi-agent planning problems. Furthermore, I demonstrate the practical use of one of my algorithms in a large-scale disaster simulation where it allows for a more efficient rescue operation. |
| Keyword | agents; planning under uncertainty; continuous resources; Markov decision process; convolution |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Type | texts |
| Legacy record ID | usctheses-m1277 |
| Rights | Marecki, Janusz |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | http://www.usc.edu/isd/libraries/services/ask_a_librarian/email/ |
| Filename | etd-Marecki-20080619 |
| Archival file | uscthesesreloadpub_Volume32/etd-Marecki-20080619.pdf |
Description
| Title | Page 1 |
| Full text | PLANNING WITH CONTINUOUS RESOURCES IN AGENT SYSTEMS by Janusz Marecki A Dissertation Presented to the FACULTY OF THE GRADUATE SCHOOL UNIVERSITY OF SOUTHERN CALIFORNIA In Partial Fulfillment of the Requirements for the Degree DOCTOR OF PHILOSOPHY (COMPUTER SCIENCE) August 2008 Copyright 2008 Janusz Marecki |
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