Solving Dec-MDPs with options and intention recognition

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2016

Author dc:creator

• Cruz, Gabriel, M. Eng. Massachusetts Institute of Technology

Advisor dc:contributor.advisor

• Tomás Lozano-Pérez and Leslie Pack Kaelbling.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/106028

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/106028