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dc.contributor.advisorBrian C. Williams.en_US
dc.contributor.authorWang, Andrew Jen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2014-03-06T15:47:39Z
dc.date.available2014-03-06T15:47:39Z
dc.date.copyright2013en_US
dc.date.issued2013en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/85516
dc.descriptionThesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2013.en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 63-64).en_US
dc.description.abstractTemporal uncertainty arises when performing any activity in the natural world. When activities are composed into temporal plans, then, there is a risk of not meeting the plan requirements. Currently, we do not have quantitatively precise methods for assessing temporal risk of a plan. Existing methods that deal with temporal uncertainty either forgo probabilistic models or try to optimize a single objective, rather than satisfy multiple objectives. This thesis offers a method for evaluating whether a schedule exists that meets a set of temporal constraints, with acceptable risk of failure. Our key insight is to assume a form of risk allocation to each source of temporal uncertainty in our plan, such that we may reformulate the probabilistic plan into an STNU parameterized on the risk allocation. We show that the problem becomes a deterministic one of finding a risk allocation which implies a schedulable STNU within acceptable risk. By leveraging the principles behind STNU analysis, we derive conditions which encode this problem as a convex feasibility program over risk allocations. Furthermore, these conditions may be learned incrementally as temporal conflicts. Thus, to boost computational efficiency, we employ a generate-and-test approach to determine whether a schedule may be found.en_US
dc.description.statementofresponsibilityby Andrew J. Wang.en_US
dc.format.extent64 pagesen_US
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/1721.1/7582en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleRisk allocation for temporal risk assessmenten_US
dc.typeThesisen_US
dc.description.degreeM. Eng.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.identifier.oclc871037953en_US


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