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Published in:
2019 | OriginalPaper | Chapter
Improving Planning Performance in PDDL+ Domains via Automated Predicate Reformulation
Authors : Santiago Franco, Mauro Vallati, Alan Lindsay, Thomas Lee McCluskey
Published in: Computational Science – ICCS 2019
Publisher: Springer International Publishing
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Abstract
In the last decade, planning with domains modelled in the hybrid PDDL+ formalism has been gaining significant research interest. A number of approaches have been proposed that can handle PDDL+, and their exploitation fostered the use of planning in complex scenarios. In this paper we introduce a PDDL+ reformulation method that reduces the size of the grounded problem, by reducing the arity of sparse predicates, i.e. predicates with a very large number of possible groundings, out of which very few are actually exploited in the planning problems. We include an empirical evaluation which demonstrates that these methods can substantially improve performance of domain-independent planners on PDDL+ domains.