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Daniela P. de Farias. Ling, Lee, S.B. Massachusetts Institute of Technology Massachusetts Institute of Technology. Dept. of Mechanical Engineering. Massachusetts Institute of Technology. Department of Mechanical Engineering 2007-03-12T17:48:37Z 2007-03-12T17:48:37Z 2006 2006 http://hdl.handle.net/1721.1/36732 77564095 Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006. Includes bibliographical references (p. 39). This thesis studies the design of basis functions in approximate linear programming (ALP) as a decision-making tool. A case study on a robotic control problem shows that feature-based basis functions are very effective because they are able to capture the characteristics and cost structure of the problem. State-space partitioning, polynomials and other non-linear combinations of state parameters are also used in the ALP. However, design of these basis functions requires more trial-and-error. Simulation results show that control policy generated by the approximate linear programming algorithm matches and sometimes surpasses that of heuristics. Moreover, optimal policies are found well before value function estimates reach optimality. The ALP scales well with problem size and the number of basis functions required to find the optimal policy does not increase significantly in larger scale systems. The promising results shed light on the possibility of applying approximate linear programming to other large-scale problems that are computationally intractable using traditional dynamic programming methods. by Lee Ling. S.B. 39 p. 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 Mechanical Engineering. Developing approximation architectures for decision-making in real-time systems Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="6ee29c6e-fc5e-4ed9-8ea7-a7b67e6bdd55"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Developing approximation architectures for decision-making in real-time systems</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2006</PublicationDate> <Authors> <Author> <DisplayName>Ling, Lee, S.B. Massachusetts Institute of Technology</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Mechanical Engineering.</Keyword> <Abstract>This thesis studies the design of basis functions in approximate linear programming (ALP) as a decision-making tool. A case study on a robotic control problem shows that feature-based basis functions are very effective because they are able to capture the characteristics and cost structure of the problem. State-space partitioning, polynomials and other non-linear combinations of state parameters are also used in the ALP. However, design of these basis functions requires more trial-and-error. Simulation results show that control policy generated by the approximate linear programming algorithm matches and sometimes surpasses that of heuristics. Moreover, optimal policies are found well before value function estimates reach optimality. The ALP scales well with problem size and the number of basis functions required to find the optimal policy does not increase significantly in larger scale systems. The promising results shed light on the possibility of applying approximate linear programming to other large-scale problems that are computationally intractable using traditional dynamic programming methods.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>