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Some recent works in conditional planning have proposed reachability heuristics to improve
planner scalability, but many lack a formal description of the properties of their distance estimates.
To place previous work in context and extend work on heuristics for conditional planning, we
provide a formal basis for distance estimates between belief states. We give a definition for the
distance between belief states that relies on aggregating underlying state distance measures. We
give several techniques to aggregate state distances and their associated properties. Many existing
heuristics exhibit a subset of the properties, but in order to provide a standardized comparison we
present several generalizations of planning graph heuristics that are used in a single planner. We
compliment our belief state distance estimate framework by also investigating efficient planning
graph data structures that incorporate BDDs to compute the most effective heuristics.
We developed two planners to serve as test-beds for our investigation. The first, CAltAlt,
is a conformant regression planner that uses A* search. The second, POND, is a conditional
progression planner that uses AO* search. We show the relative effectiveness of our heuristic
techniques within these planners. We also compare the performance of these planners with several
state of the art approaches in conditional planning.
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