2026-09-18T22:16:32Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/985662022-01-13T07:54:48Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Michael P. Owen and Dimitri P. Bertsekas.
Lepird, John R
Massachusetts Institute of Technology. Operations Research Center.
Massachusetts Institute of Technology. Operations Research Center
Sloan School of Management
2015-09-17T17:42:55Z
2015-09-17T17:42:55Z
2015
http://hdl.handle.net/1721.1/98566
920857584
Thesis: S.M., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2015.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
"June 2015." Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 85-90).
Developed in the 1970's and 1980's, the Traffic Alert and Collision Avoidance System (TCAS) is the last safety net to prevent an aircraft mid-air collision. Although TCAS has been historically very effective, TCAS logic must adapt to meet the new challenges of our increasingly busy modern airspace. Numerous studies have shown that formulating collision avoidance as a partially-observable Markov decision process (POMDP) can dramatically increase system performance. However, the POMDP formulation relies on a number of design parameters modifying these parameters can dramatically alter system behavior. Prior work tunes these design parameters with respect to a single performance metric. This thesis extends existing work to handle more than one performance metric. We introduce an algorithm for preference elicitation that allows the designer to meaningfully define a utility function. We also discuss and implement a genetic algorithm that can perform multi-objective optimization directly. By appropriately applying these two methods, we show that we are able to tune the POMDP design parameters more effectively than existing work.
by John R. Lepird.
S.M.
90 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
Operations Research Center.
Multi-objective optimization of next-generation aircraft collision avoidance software
Thesis
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<Abstract>Developed in the 1970's and 1980's, the Traffic Alert and Collision Avoidance System (TCAS) is the last safety net to prevent an aircraft mid-air collision. Although TCAS has been historically very effective, TCAS logic must adapt to meet the new challenges of our increasingly busy modern airspace. Numerous studies have shown that formulating collision avoidance as a partially-observable Markov decision process (POMDP) can dramatically increase system performance. However, the POMDP formulation relies on a number of design parameters modifying these parameters can dramatically alter system behavior. Prior work tunes these design parameters with respect to a single performance metric. This thesis extends existing work to handle more than one performance metric. We introduce an algorithm for preference elicitation that allows the designer to meaningfully define a utility function. We also discuss and implement a genetic algorithm that can perform multi-objective optimization directly. By appropriately applying these two methods, we show that we are able to tune the POMDP design parameters more effectively than existing work.</Abstract>
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