Romeo Sanchez Nigenda

Planning graph as the basis for deriving heuristics for plan synthesis by state space and CSP search

Most recent strides in scaling up planning have centered around two competing themesdisjunctive planners, exemplified by Graphplan, and heuristic state search planners, exemplified by UNPOP, HSP and HSP-r. In this paper, we present a novel approach for successfully harnessing the advantages of the two competing paradigms to develop planners that are significantly more powerful than either of the approaches. Specifically, we show that the polynomial-time planning graph structure that the Graphplan builds provides a rich substrate for deriving a family of highly effective heuristics for guiding state space search as well as CSP style search. The main leverage provided by the planning graph structure is a systematic and graded way to take subgoal interactions into account in designing state space heuristics. For state space search, we develop several families of heuristics, some aimed at search speed and others at optimality of solutions, and analyze many approaches for improving the cost-quality tradeoffs offered by these heuristics. Our normalized empirical comparisons show that our heuristics handily outperform the existing state space heuristics. For CSP style search, we describe a novel way of using the planning graph structure to derive highly effective variable and value ordering heuristics. We show that these heuristics can be used to improve Graphplans own backward search significantly. To demonstrate the effectiveness of our approach vis a vis the state-of-the-art in plan synthesis, we present AltAlt, a planner literally cobbled together from the implementations of Graphplan and state search style planners using our theory. We evaluate AltAlt on the suite of problems used in the recent AIPS-2000 planning competition. The results place AltAlt in the top tier of the competition plannersoutperforming both Graphplan based and heuristic search based planners.

AltAlt p : online parallelization of plans with heuristic state search

Despite their near dominance, heuristic state search planners still lag behind disjunctive planners in the generation of parallel plans in classical planning. The reason is that directly searching for parallel solutions in state space planners would require the planners to branch on all possible subsets of parallel actions, thus increasing the branching factor exponentially. We present a variant of our heuristic state search planner AltAlt called AltAltp which generates parallel plans by using greedy online parallelization of partial plans. The greedy approach is significantly informed by the use of novel distance heuristics that AltAltp derives from a graphplan-style planning graph for the problem. While this approach is not guaranteed to provide optimal parallel plans, empirical results show that AltAltp is capable of generating good quality parallel plans at a fraction of the cost incurred by the disjunctive planners.

AltAlt: Combining Graphplan and Heuristic State Search

We briefly describe the implementation and evaluation of a novel plan synthesis system, called AltAlt. AltAlt is designed to exploit the complementary strengths of two of the currently popular competing approaches for plan generation: (1) graphplan and (2) heuristic state search. It uses the planning graph to derive effective heuristics that are then used to guide heuristic state search. The heuristics derived from the planning graph do a better job of taking the subgoal interactions into account and, as such, are significantly more effective than existing heuristics. AltAlt was implemented on top of two state-of-the-art planning systems: (1) stan3.0, a graphplan-style planner, and (2) hsp-r, a heuristic search planner.