2026-09-18T18:40:37Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1060282026-06-06T01:03:15Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Tomás Lozano-Pérez and Leslie Pack Kaelbling.
Cruz, Gabriel, M. Eng. Massachusetts Institute of Technology
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2016-12-22T15:19:09Z
2016-12-22T15:19:09Z
2016
2016
http://hdl.handle.net/1721.1/106028
965830909
Thesis: M. Eng. in Computer Science and Engineering, Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 31-32).
In this thesis, we designed and implemented an algorithm to find approximate solutions to multi-agent systems. We model the problems with a Decentralized Markov Decision Process, and we make use of options and intention recognition to solve the problem. Rather than directly solving the Dec-MDP, which is NEXP-Complete, we instead solve a set of single-agent MDPs, that we can solve in P-Complete, and combine these solutions during execution time. We tested our algorithm on several instances of the Bribed Package Retrieval Problem and we were able to handle problems as large as our MDP solver would allow, which is a big improvement over what optimal Dec-MDP solvers can handle.
by Gabriel Cruz.
M.Eng. in Computer Science and Engineering
32 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Solving Dec-MDPs with options and intention recognition
Solving decentralized Markov decision processes with options and intention recognition
Thesis
application/pdf
Publication
unknown
unknown
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<Abstract>In this thesis, we designed and implemented an algorithm to find approximate solutions to multi-agent systems. We model the problems with a Decentralized Markov Decision Process, and we make use of options and intention recognition to solve the problem. Rather than directly solving the Dec-MDP, which is NEXP-Complete, we instead solve a set of single-agent MDPs, that we can solve in P-Complete, and combine these solutions during execution time. We tested our algorithm on several instances of the Bribed Package Retrieval Problem and we were able to handle problems as large as our MDP solver would allow, which is a big improvement over what optimal Dec-MDP solvers can handle.</Abstract>
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