Developing approximation architectures for decision-making in real-time systems
Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006.
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| Formato: | Tesis |
| Lenguaje: | eng |
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Massachusetts Institute of Technology
2007
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| Acceso en línea: | http://hdl.handle.net/1721.1/36732 |
| _version_ | 1826201228697141248 |
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| author | Ling, Lee, S.B. Massachusetts Institute of Technology |
| author2 | Daniela P. de Farias. |
| author_facet | Daniela P. de Farias. Ling, Lee, S.B. Massachusetts Institute of Technology |
| author_sort | Ling, Lee, S.B. Massachusetts Institute of Technology |
| collection | MIT |
| description | Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2006. |
| first_indexed | 2024-09-23T11:48:34Z |
| format | Thesis |
| id | mit-1721.1/36732 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T11:48:34Z |
| publishDate | 2007 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/367322019-04-11T10:29:47Z Developing approximation architectures for decision-making in real-time systems Ling, Lee, S.B. Massachusetts Institute of Technology Daniela P. de Farias. Massachusetts Institute of Technology. Dept. of Mechanical Engineering. Massachusetts Institute of Technology. Dept. of Mechanical Engineering. Mechanical Engineering. 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. 2007-03-12T17:48:37Z 2007-03-12T17:48:37Z 2006 2006 Thesis http://hdl.handle.net/1721.1/36732 77564095 eng 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 39 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Mechanical Engineering. Ling, Lee, S.B. Massachusetts Institute of Technology Developing approximation architectures for decision-making in real-time systems |
| title | Developing approximation architectures for decision-making in real-time systems |
| title_full | Developing approximation architectures for decision-making in real-time systems |
| title_fullStr | Developing approximation architectures for decision-making in real-time systems |
| title_full_unstemmed | Developing approximation architectures for decision-making in real-time systems |
| title_short | Developing approximation architectures for decision-making in real-time systems |
| title_sort | developing approximation architectures for decision making in real time systems |
| topic | Mechanical Engineering. |
| url | http://hdl.handle.net/1721.1/36732 |
| work_keys_str_mv | AT lingleesbmassachusettsinstituteoftechnology developingapproximationarchitecturesfordecisionmakinginrealtimesystems |