Name
Abstract | ||
|---|---|---|
By relaxing the hard-goal constraints from classical planning and associating them with reward values, over-subscription planning allows users to concentrate on presenting what they want and leaves the task of deciding the best goals to achieve to the planner. In this paper, we extend the over-subscription planning problem and its limited goal specification to allow numeric goals with continuous utility values and goals with mixed hard and soft constraints. Together they considerably extend the modeling power of goal specification and allow the user to express goal constraints that were not possible before. To handle these new goal constraints, we extend the Sapaps planner's planning graph based techniques to help it choose the best beneficial subset of goals that can include both hard or soft logical and numeric goals. We also provide empirical results in several benchmark domains to demonstrate that our technique helps return quality plans. |
Year | Venue | Keywords |
|---|---|---|
2005 | IJCAI | numeric goal,classical planning,new goal constraint,over-subscription planning,goal specification,goal constraint,over-subscription planning problem,planning graph,limited goal specification,best goal |
Field | DocType | Citations |
Graph,Logical conjunction,Software engineering,Computer science,Planner,Artificial intelligence,Soft goal,Management science,Machine learning | Conference | 10 |
PageRank | References | Authors |
0.82 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| J. Benton | 1 | 155 | 9.45 |
| Minh B. Do | 2 | 198 | 10.93 |
| Subbarao Kambhampati | 3 | 3453 | 450.74 |