2026-09-18T19:46:32Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1006042026-06-06T00:49:03Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Leslie Pack Kaelbling.
Kamahele-Sanfratello, Ciara L. (Ciara Lei)
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2016-01-04T19:57:53Z
2016-01-04T19:57:53Z
2015
2015
http://hdl.handle.net/1721.1/100604
932221663
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
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 (page 32).
SASY (Scalable and Adjustable SYmbolic) Planner is a flexible symbolic planner which searches for a satisfying plan to a partially observable Markov decision process, or a POMDP, while benefiting from advantages of classical symbolic planning such as compact belief state expression, domain-independent heuristics, and structural simplicity. Belief space symbolic formalism, an extension of classical symbolic formalism, can be used to transform probabilistic problems into a discretized and deterministic representation such that domain-independent heuristics originally created for classical symbolic planning systems can be applied to them. SASY is optimized to solve POMDPs encoded in belief space symbolic formalism, but can also be used to find a solution to general symbolic planning problems. We compare SASY to two other POMDP solvers, SARSOP and POMDPX_NUS, and define a new benchmark domain called Elevator.
by Ciara L. Kamahele-Sanfratello.
M.Eng.
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.
Symbolic planning in belief space
Thesis
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<Abstract>SASY (Scalable and Adjustable SYmbolic) Planner is a flexible symbolic planner which searches for a satisfying plan to a partially observable Markov decision process, or a POMDP, while benefiting from advantages of classical symbolic planning such as compact belief state expression, domain-independent heuristics, and structural simplicity. Belief space symbolic formalism, an extension of classical symbolic formalism, can be used to transform probabilistic problems into a discretized and deterministic representation such that domain-independent heuristics originally created for classical symbolic planning systems can be applied to them. SASY is optimized to solve POMDPs encoded in belief space symbolic formalism, but can also be used to find a solution to general symbolic planning problems. We compare SASY to two other POMDP solvers, SARSOP and POMDPX_NUS, and define a new benchmark domain called Elevator.</Abstract>
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