ASAP: An Automatic Algorithm Selection Approach for Planning

Despite the advances made in the last decade in automated planning, no planner out-
performs all the others in every known benchmark domain. This observation motivates
the idea of selecting different planning algorithms for different domains. Moreover, the
planners’ performances are affected by the structure of the search space, which depends
on the encoding of the considered domain. In many domains, the performance of a plan-
ner can be improved by exploiting additional knowledge, for instance, in the form of
macro-operators or entanglements.
In this paper we propose ASAP, an automatic Algorithm Selection Approach for
Planning that: (i) for a given domain initially learns additional knowledge, in the form
of macro-operators and entanglements, which is used for creating different encodings
of the given planning domain and problems, and (ii) explores the 2 dimensional space
of available algorithms, defined as encodings–planners couples, and then (iii) selects the
most promising algorithm for optimising either the runtimes or the quality of the solution
plans.