TY - JOUR
T1 - Multi-contributor causal structures for planning
T2 - a formalization and evaluation
AU - Kambhampati, Subbarao
N1 - Funding Information:
I wouldl ike to thankA ustinT atew hor eada drafto f thisp apera ndp rovided many valuables uggestionasn d pointersM, ark Drummondw ho listenedt o someo f thesei deasi n the early stagesa nd providedu sefulf eedbackJ,o hn Bresinaw hosec ommentasb outv alidation-structure-bagseende ralizatioonf plansl ed me to look into multi-contributvoar lidations tructuresse riouslyin the first"p lace,D an Weld, David McAllester,J engchinC hen, Bulusu Gopi Kumar, and the anonymourse vieweros f AAAI-92 and AIPS-92 for helpful commentasn dcriticisms. Supportfo r this researchha sb eenp rovidedin partb y the NationalS cience Foundationre searchin itiationa wardI RI-9210997N, SF Young Investigator Award IRI-9457634a, ndthe ARPA/RomeL aboratorpy lanningin itiativeu n-der grantF 30602-93-C-003(9t o Universityo f Marylanda nd Arizona State University).
PY - 1994/9
Y1 - 1994/9
N2 - Explicit causal structure representations have been widely used in classical planning systems to guide a variety of aspects of planning, including plan generation, modification and generalization. For the most part, these representations were limited to single-contributor causal structures. Although widely used, single-contributor causal structures have several limitations in handling partially ordered and partially instantiated plans. Specifically they are (i) incapable of exploiting redundancy in the plan causal structure and (ii) force premature commitment to individual contributors thereby causing unnecessary backtracking. In this paper, we study multi-contributor causal structures as a way of overcoming these limitations. We will provide a general formulation for multi-contributor causal links, and explore the properties of several special classes of this formulation. We will then describe two planning algorithms-MP and MP-I-that use multi-contributor causal links to organize their search for plans. We will describe empirical studies demonstrating the advantages of MP and MP-I over planners that use single contributor causal structures, and argue that they strike a more favorable balance in the tradeoff between search space redundancy and premature commitment to contributors. Finally, we will present a framework for justifying plans with respect to multi-contributor causal structures and describe its applications in plan modification and generalization.
AB - Explicit causal structure representations have been widely used in classical planning systems to guide a variety of aspects of planning, including plan generation, modification and generalization. For the most part, these representations were limited to single-contributor causal structures. Although widely used, single-contributor causal structures have several limitations in handling partially ordered and partially instantiated plans. Specifically they are (i) incapable of exploiting redundancy in the plan causal structure and (ii) force premature commitment to individual contributors thereby causing unnecessary backtracking. In this paper, we study multi-contributor causal structures as a way of overcoming these limitations. We will provide a general formulation for multi-contributor causal links, and explore the properties of several special classes of this formulation. We will then describe two planning algorithms-MP and MP-I-that use multi-contributor causal links to organize their search for plans. We will describe empirical studies demonstrating the advantages of MP and MP-I over planners that use single contributor causal structures, and argue that they strike a more favorable balance in the tradeoff between search space redundancy and premature commitment to contributors. Finally, we will present a framework for justifying plans with respect to multi-contributor causal structures and describe its applications in plan modification and generalization.
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U2 - 10.1016/0004-3702(94)90083-3
DO - 10.1016/0004-3702(94)90083-3
M3 - Article
AN - SCOPUS:0028494550
SN - 0004-3702
VL - 69
SP - 235
EP - 278
JO - Artificial Intelligence
JF - Artificial Intelligence
IS - 1-2
ER -