TY - JOUR
T1 - ASAP
T2 - An Automatic Algorithm Selection Approach for Planning
AU - Vallati, Mauro
AU - Chrpa, Lukáš
AU - Kitchin, Diane
PY - 2014/12/26
Y1 - 2014/12/26
N2 - Despite the advances made in the last decade in automated planning, no planner outperforms 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 planner 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.
AB - Despite the advances made in the last decade in automated planning, no planner outperforms 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 planner 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.
KW - algorithm selection
KW - Automated planning
KW - learning for planning
UR - http://www.scopus.com/inward/record.url?scp=84930157324&partnerID=8YFLogxK
U2 - 10.1142/S021821301460032X
DO - 10.1142/S021821301460032X
M3 - Article
AN - SCOPUS:84930157324
VL - 23
JO - International Journal on Artificial Intelligence Tools
JF - International Journal on Artificial Intelligence Tools
SN - 0218-2130
IS - 6
M1 - 1460032
ER -