TY - JOUR
T1 - State agnostic planning graphs
T2 - Deterministic, non-deterministic, and probabilistic planning
AU - Bryce, Daniel
AU - Cushing, William
AU - Kambhampati, Subbarao
N1 - Funding Information:
This research is supported in part by the ONR grants N00014-09-1-0017 and N00014-07-1-1049, the NSF grant IIS-0905672 for Subbarao Kambhampati, and DARPA/CMO under contract #HR0011-07-C-0060 for Daniel Bryce. We thank David Smith for his contributions to the foundations of our work, in addition, we thank the members of Yochan for many helpful suggestions. We also thank Nathaniel Hyafil and Fahiem Bacchus for their support in CPplan comparisons, Carmel Domshlak and Joerg Hoffmann for their support in PFF comparisons, Jussi Rintanen for help with BBSP, and Piergiorgio Bertoli for help with MBP.
PY - 2011/3
Y1 - 2011/3
N2 - Planning graphs have been shown to be a rich source of heuristic information for many kinds of planners. In many cases, planners must compute a planning graph for each element of a set of states, and the naive technique enumerates the graphs individually. This is equivalent to solving a multiple-source shortest path problem by iterating a single-source algorithm over each source. We introduce a data-structure, the state agnostic planning graph, that directly solves the multiple-source problem for the relaxation introduced by planning graphs. The technique can also be characterized as exploiting the overlap present in sets of planning graphs. For the purpose of exposition, we first present the technique in deterministic (classical) planning to capture a set of planning graphs used in forward chaining search. A more prominent application of this technique is in conformant and conditional planning (i.e., search in belief state space), where each search node utilizes a set of planning graphs; an optimization to exploit state overlap between belief states collapses the set of sets of planning graphs to a single set. We describe another extension in conformant probabilistic planning that reuses planning graph samples of probabilistic action outcomes across search nodes to otherwise curb the inherent prediction cost associated with handling probabilistic actions. Finally, we show how to extract a state agnostic relaxed plan that implicitly solves the relaxed planning problem in each of the planning graphs represented by the state agnostic planning graph and reduces each heuristic evaluation to counting the relevant actions in the state agnostic relaxed plan. Our experimental evaluation (using many existing International Planning Competition problems from classical and non-deterministic conformant tracks) quantifies each of these performance boosts, and demonstrates that heuristic belief state space progression planning using our technique is competitive with the state of the art.
AB - Planning graphs have been shown to be a rich source of heuristic information for many kinds of planners. In many cases, planners must compute a planning graph for each element of a set of states, and the naive technique enumerates the graphs individually. This is equivalent to solving a multiple-source shortest path problem by iterating a single-source algorithm over each source. We introduce a data-structure, the state agnostic planning graph, that directly solves the multiple-source problem for the relaxation introduced by planning graphs. The technique can also be characterized as exploiting the overlap present in sets of planning graphs. For the purpose of exposition, we first present the technique in deterministic (classical) planning to capture a set of planning graphs used in forward chaining search. A more prominent application of this technique is in conformant and conditional planning (i.e., search in belief state space), where each search node utilizes a set of planning graphs; an optimization to exploit state overlap between belief states collapses the set of sets of planning graphs to a single set. We describe another extension in conformant probabilistic planning that reuses planning graph samples of probabilistic action outcomes across search nodes to otherwise curb the inherent prediction cost associated with handling probabilistic actions. Finally, we show how to extract a state agnostic relaxed plan that implicitly solves the relaxed planning problem in each of the planning graphs represented by the state agnostic planning graph and reduces each heuristic evaluation to counting the relevant actions in the state agnostic relaxed plan. Our experimental evaluation (using many existing International Planning Competition problems from classical and non-deterministic conformant tracks) quantifies each of these performance boosts, and demonstrates that heuristic belief state space progression planning using our technique is competitive with the state of the art.
KW - Heuristics
KW - Planning
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U2 - 10.1016/j.artint.2010.12.002
DO - 10.1016/j.artint.2010.12.002
M3 - Article
AN - SCOPUS:78650792294
SN - 0004-3702
VL - 175
SP - 848
EP - 889
JO - Artificial Intelligence
JF - Artificial Intelligence
IS - 3-4
ER -