AAAI Publications, Twenty-Second International FLAIRS Conference

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Knowledge Representation for Intelligent and Error-Prone Execution of Robust Granular Plans. A Conceptual Study
Sebastian Ernst, Antoni Ligeza

Last modified: 2009-03-23


Techniques currently used for vehicle route planning suffer from drawbacks, especially significant when execution of the plan is interrupted, as the algorithms tend to return to the original solution as quickly as possible, instead of trying to "understand" the situation and react accordingly. This article describes a new approach aimed at solving this problem.


robust planning; granular planning; knowledge representation

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