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AltAlt: Combining the Advantages of Graphplan and Heuristic State Search

Most recent strides in scaling up planning have centered around two competing themes–disjunctive planners, exemplified by Graphplan, and heuristic state search planners, exemplified by HSP and HSP-R. In this paper, we describe a planner called AltAlt , which successfully combines the advantages of the two competing paradigms to develop a planner that is significantly more powerful than either of the approaches. AltAlt uses Graphplan’s planning graph in a novel manner to derive very effective search heuristics which are then used to drive a heuristic state search planner. AltAlt is implemented by splicing together implementations of STAN, a state-of-the-art Graphplan implementation, and HSP-r, a heuristic search planner. We present empirical results in a variety of domains that show the significant scale-up power of our combined approach. We will also present a variety of possible optimizations for our approach, and discuss the rich connections between our work and the literature on state-space search heuristics.