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Probabilistic reasoning for plan robustness

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dc.contributor.author Schaffer, Steve R.
dc.contributor.author Clement, Bradley J.
dc.contributor.author Chien, Steve A.
dc.date.accessioned 2008-02-06T23:14:18Z
dc.date.available 2008-02-06T23:14:18Z
dc.date.issued 2005-06-30
dc.identifier.citation International Joint Conference on Artificial Intelligence (IJCAI), Edinburgh, Scotland, June 30, 2005. en
dc.identifier.clearanceno 05-1025
dc.identifier.uri http://hdl.handle.net/2014/40639
dc.description.abstract A planning system must reason about the uncertainty of continuous variables in order to accurately project the possible system state over time. A method is devised for directly reasoning about the uncertainty in continuous activity duration and resource usage for planning problems. By representing random variables as parametric distributions, computing projected system state can be simplified in some cases. Common approximation and novel methods are compared for over-constrained and lightly constrained domains. The system compares a few common approximation methods for an iterative repair planner. Results show improvements in robustness over the conventional non-probabilistic representation by reducing the number of constraint violations witnessed by execution. The improvement is more significant for larger problems and problems with higher resource subscription levels but diminishes as the system is allowed to accept higher risk levels. en
dc.description.sponsorship NASA/JPL en
dc.format.extent 759365 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US en
dc.publisher Pasadena, CA : Jet Propulsion Laboratory, National Aeronautics and Space Administration, 2005. en
dc.subject autonomous planning en
dc.subject probabilistic reasoning en
dc.title Probabilistic reasoning for plan robustness en
dc.type Preprint en


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