Control Knowledge in Planning: Benefits and Tradeoffs

Proceedings of the AAAI Conference on Artificial Intelligence, 16

Volume

Proceedings of the AAAI Conference on Artificial Intelligence, 16

Planning

Recent new planning paradigms, such as Graphplan and Satplan, have been shown to outperform more traditional domain-independent planners. An interesting aspect of these planners is that they do not incorporate domain specific control knowledge, but instead rely on efficient graph-based or propositional representations and advanced search techniques. An alternative approach has been proposed in the TLPlan system. TLPlan is an example of a powerful planner incorporating declarative control specified in temporal logic formulas. We show how these control rules can be parsed into Satplan. Our empirical results show more than an order of magnitude speed up. We also provide a detailed comparison with TLPlan, and show how the search strategies in TLPlan lead to efficient plans in terms of the number of actions but with little or no parallelism. The Satplan and Graphplan formalisms on the other hand do find highly parallel plans, but are less effective in sequential domains. We believe our results enhance our understanding of the various tradeoffs in planning technology.