Name
Abstract | ||
|---|---|---|
We introduce a novel algorithm for temporal planning in Golog using shared resources, and describe the Bulk Freight Rail Scheduling Problem, a motivating example of such a temporal domain. We use the framework of column generation to tackle complex resource constrained temporal planning problems that are beyond the scope of current planning technology by combining: the global view of a linear programming relaxation of the problem; the strength of search in finding action sequences; and the domain knowledge that can be encoded in a Golog program. We show that our approach significantly outperforms state-of-the-art temporal planning and constraint programming approaches in this domain, in addition to existing temporal Golog implementations. We also apply our algorithm to a temporal variant of blocks-world where our decomposition speeds proof of optimality significantly compared to other anytime algorithms. We discuss the potential of the underlying algorithm being applicable to STRIPS planning, with further work. |
Year | Venue | Field |
|---|---|---|
2014 | Proceedings of the International Conference on Automated Planning and Scheduling | Mathematical optimization,Column generation,Job shop scheduling,Domain knowledge,Computer science,Constraint programming,Implementation,STRIPS,Artificial intelligence,Linear programming relaxation,Machine learning |
DocType | ISSN | Citations |
Conference | 2334-0835 | 2 |
PageRank | References | Authors |
0.37 | 17 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Toby O. Davies | 1 | 3 | 1.41 |
| Adrian R. Pearce | 2 | 301 | 31.88 |
| Peter J. Stuckey | 3 | 4368 | 457.58 |
| Harald Søndergaard | 4 | 858 | 79.52 |