TY - GEN
T1 - Scaling up planning by teasing out resource scheduling
AU - Srivastava, Biplav
AU - Kambhampati, Subbarao
N1 - Publisher Copyright:
© 2000 Springer-Verlag Berlin Heidelberg.
PY - 2000
Y1 - 2000
N2 - Planning consists of an action selection phase where actions are selected and ordered to reach the desired goals, and a resource alloca-tion phase where enough resources are assigned to ensure the successful execution of the chosen actions. In most real-world problems, these two phases are loosely coupled. Most existing planners do not exploit this loose-coupling, and perform both action selection and resource assign-ment employing the same algorithm. We shall show that this strategy severely curtails the scale-up potential of existing planners, including such recent ones as Graphplan and Blackbox. In response, we propose a novel planning framework in which resource allocation is teased apart from planning, and is handled in a separate\scheduling" phase. We ig-nore resource constraints during planning and produce an abstract plan that can correctly achieve the goals but for the resource constraints. Next, based on the actual resource availability, the abstract plan will be allocated resources to produce an executable plan. Our approach not only preserves both the correctness as well as the quality (measured in length) of the plan but also improves eficiency. We describe a prototype implementation of our approach on top of Graphplan and show impres-sive empirical results.
AB - Planning consists of an action selection phase where actions are selected and ordered to reach the desired goals, and a resource alloca-tion phase where enough resources are assigned to ensure the successful execution of the chosen actions. In most real-world problems, these two phases are loosely coupled. Most existing planners do not exploit this loose-coupling, and perform both action selection and resource assign-ment employing the same algorithm. We shall show that this strategy severely curtails the scale-up potential of existing planners, including such recent ones as Graphplan and Blackbox. In response, we propose a novel planning framework in which resource allocation is teased apart from planning, and is handled in a separate\scheduling" phase. We ig-nore resource constraints during planning and produce an abstract plan that can correctly achieve the goals but for the resource constraints. Next, based on the actual resource availability, the abstract plan will be allocated resources to produce an executable plan. Our approach not only preserves both the correctness as well as the quality (measured in length) of the plan but also improves eficiency. We describe a prototype implementation of our approach on top of Graphplan and show impres-sive empirical results.
UR - http://www.scopus.com/inward/record.url?scp=84943268939&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84943268939&partnerID=8YFLogxK
U2 - 10.1007/10720246_14
DO - 10.1007/10720246_14
M3 - Conference contribution
AN - SCOPUS:84943268939
SN - 3540678662
SN - 9783540678663
T3 - Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
SP - 172
EP - 186
BT - Recent Advances in AI Planning - 5th European Conference on Planning, ECP 1999, Proceedings
A2 - Biundo, Susanne
A2 - Fox, Maria
PB - Springer Verlag
T2 - 5th European Conference on Planning, ECP 1999
Y2 - 8 September 1999 through 10 September 1999
ER -