TY - GEN
T1 - An online learning method for improving over-subscription planning
AU - Yoon, Sungwook
AU - Benton, J.
AU - Kambhampati, Subbarao
PY - 2008/12/1
Y1 - 2008/12/1
N2 - Despite the recent resurgence of interest in learning methods for planning, most such efforts are still focused exclusively on classical planning problems. In this work, we investigate the effectiveness of learning approaches for improving over-subscription planning, a problem that has received significant recent interest. Viewing over-subscription planning as a domain-independent optimization problem, we adapt the STAGE (Boyan and Moore 2000) approach to learn and improve the plan search. The key challenge in our study is how to automate the feature generation process. In our case, we developed and experimented with a relational feature set, based on Taxonomic syntax as well as a propositional feature set, based on ground-facts. The feature generation process and training data generation process are all automatic, making it a completely domain-independent optimization process that takes advantage of online learning. In empirical studies, our proposed approach improved upon the baseline planner for over-subscription planning on many of the benchmark problems.
AB - Despite the recent resurgence of interest in learning methods for planning, most such efforts are still focused exclusively on classical planning problems. In this work, we investigate the effectiveness of learning approaches for improving over-subscription planning, a problem that has received significant recent interest. Viewing over-subscription planning as a domain-independent optimization problem, we adapt the STAGE (Boyan and Moore 2000) approach to learn and improve the plan search. The key challenge in our study is how to automate the feature generation process. In our case, we developed and experimented with a relational feature set, based on Taxonomic syntax as well as a propositional feature set, based on ground-facts. The feature generation process and training data generation process are all automatic, making it a completely domain-independent optimization process that takes advantage of online learning. In empirical studies, our proposed approach improved upon the baseline planner for over-subscription planning on many of the benchmark problems.
UR - http://www.scopus.com/inward/record.url?scp=58849090115&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=58849090115&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:58849090115
SN - 9781577353867
T3 - ICAPS 2008 - Proceedings of the 18th International Conference on Automated Planning and Scheduling
SP - 404
EP - 411
BT - ICAPS 2008 - Proceedings of the 18th International Conference on Automated Planning and Scheduling
T2 - 18th International Conference on Automated Planning and Scheduling, ICAPS 2008
Y2 - 14 September 2008 through 18 September 2008
ER -