Fragment-Based Planning Using Column Generation

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 infinding 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.