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Operator Mutexes and Symmetries for Simplifying Planning Tasks
2019
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
Simplifying classical planning tasks by removing operators while preserving at least one optimal solution can significantly enhance the performance of planners. In this paper, we introduce the notion of operator mutex, which is a set of operators that cannot all be part of the same (strongly) optimal plan. We propose four different methods for inference of operator mutexes and experimentally verify that they can be found in a sizable number of planning tasks. We show how operator mutexes can be
doi:10.1609/aaai.v33i01.33017586
fatcat:rugxfzy7zzeh5cxbezzfjflfei