Show simple item record

dc.contributor.advisorBrian C. Williams.en_US
dc.contributor.authorLi, Hui (Hui Xylo)en_US
dc.contributor.otherMassachusetts Institute of Technology. Dept. of Aeronautics and Astronautics.en_US
dc.date.accessioned2006-03-29T18:47:01Z
dc.date.available2006-03-29T18:47:01Z
dc.date.copyright2005en_US
dc.date.issued2005en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/32466
dc.descriptionThesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2005.en_US
dc.descriptionIncludes bibliographical references (p. 73-76).en_US
dc.description.abstractConflict-directed search algorithms have formed the core of practical, model-based reasoning systems for the last three decades. In many of these applications there is a series of discrete constraint optimization problems and a conflict-directed search algorithm, which uses conflicts in the forward search step to focus search away from known infeasibilities and towards the optimal solution. In the arena of model-based autonomy, discrete systems, like deep space probes, have given way to more agile systems, such as coordinated vehicle control, which must robustly control their continuous dynamics. Controlling these systems requires optimizing over continuous, as well as discrete variables, using linear and non-linear as well as logical constraints. This paper explores the development of algorithms for solving hybrid discrete/linear optimization problems that use conflicts in the forward search direction, generalizing from the conflict-directed search algorithms of model-based reasoning. We introduce a novel algorithm called Generalized Conflict-directed Branch and Bound (GCD-BB). GCD-BB extends traditional Branch and Bound (B&B), by first constructing conflicts from nodes of the search tree that are found to be infeasible or sub-optimal, and then by using these conflicts to guide the forward search away from known infeasible and sub-optimal states. We evaluate GCD-BB empirically on a range of test problems of coordinated air vehicle control. GCD-BB demonstrates a substantial improvement in performance compared to a traditional B&B algorithm, applied to either disjunctive linear programs or an equivalent binary integer program encoding.en_US
dc.description.statementofresponsibilityby Hui Li.en_US
dc.format.extent76 p.en_US
dc.format.extent3569088 bytes
dc.format.extent3572175 bytes
dc.format.mimetypeapplication/pdf
dc.format.mimetypeapplication/pdf
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582
dc.subjectAeronautics and Astronautics.en_US
dc.titleGeneralized conflict learning for hybrid discrete/linear optimizationen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Dept. of Aeronautics and Astronautics.en_US
dc.identifier.oclc61763107en_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record