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Using Iterative Repair to Improve the Responsiveness of Planning and Scheduling

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dc.contributor.author Chien, S. en_US
dc.contributor.author Knight, R. en_US
dc.contributor.author Stechert, A. en_US
dc.contributor.author Sherwood, R. en_US
dc.contributor.author Rabideau, G. en_US
dc.date.accessioned 2004-09-23T17:31:41Z
dc.date.available 2004-09-23T17:31:41Z
dc.date.issued 2000-04-14 en_US
dc.identifier.citation Artificial Intelligence Planning and Scheduling en_US
dc.identifier.citation Breckenridge, CO, USA en_US
dc.identifier.clearanceno 00-0277 en_US
dc.identifier.uri http://hdl.handle.net/2014/13902
dc.description.abstract The majority of planning and scheduling research has focused on batch-oriented models of planning. en_US
dc.format.extent 988883 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.subject.other Artificial Intelligence planning scheduling execution en_US
dc.title Using Iterative Repair to Improve the Responsiveness of Planning and Scheduling en_US


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