Generating macro-operators by exploiting inner entanglements
Lukáš Chrpa, Mauro Vallati, Thomas Leo McCluskey, Diane E. Kitchin · Huddersfield Research Portal (University of Huddersfield) · 2013
In Automated Planning, learning and exploiting additional knowledge within a domain model, in order to improve plan generation speed-up and increase the scope of problems solved, has attracted much research. Reformulation techniques such as those based on macro-operators or entanglements are very promising because they are to some extent domain model and planning engine independent. This paper aims to exploit recent work on inner entanglements, relations between pairs of planning operators and predicates encapsulating exclusivity of predicate ‘achievements‘ or ‘requirements’, for generating macro-operators. We provide a theoretical study resulting in a set of conditions when planning operators in an inner entanglement relation can be removed from a domain model and replaced by a macro-operator without compromising solvability of a given (class of) problem(s). The effectiveness of our approach will be experimentally shown on a set of well-known benchmark domains using several highperforming planning engines.