TY - JOUR
T1 - A unified framework for explanation-based generalization of partially ordered and partially instantiated plans
AU - Kambhampati, Subbarao
AU - Kedar, Smadar
N1 - Funding Information:
The first authorw asp artiallys upportebdy the Office of Naval Researchu n-der contracNt 00014-88-K-062(0to StanfordU niversity)b, y NSF underg rant IRI-9210997( to Arizona StateU niversity)a, nd by ARPA/ROME Laboratory PlanningI nitiativeu ndergrant F30602-93-C-0039.
PY - 1994/5
Y1 - 1994/5
N2 - Most previous work in explanation-based generalization (EBG) of plans dealt with totally ordered plans. These methods cannot be directly applied to generalizing partially ordered partially instantiated plans, a class of plans that have received significant attention in planning. In this paper we present a natural way of extending the explanation-based generalization methods to partially ordered partially instantiated (POPI) plans. Our development is based on modal truth criteria for POPI plans [3]. We develop explanation structures from these truth criteria, and use them as a common basis to derive a variety of generalization algorithms. Specifically we present algorithms for precondition generalization, order generalization, and possible correctness generalization of POPI plans. The systematic derivation of the generalization algorithms from the modal truth criterion obviates the need for carrying out a separate formal proof of correctness of the EBG algorithms. Our development also systematically explicates the tradeoffs among the spectrum of possible generalizations for POPI plans, and provides an empirical demonstration of the relative utility of EBG in partial ordering, as opposed to total ordering, planning frameworks.
AB - Most previous work in explanation-based generalization (EBG) of plans dealt with totally ordered plans. These methods cannot be directly applied to generalizing partially ordered partially instantiated plans, a class of plans that have received significant attention in planning. In this paper we present a natural way of extending the explanation-based generalization methods to partially ordered partially instantiated (POPI) plans. Our development is based on modal truth criteria for POPI plans [3]. We develop explanation structures from these truth criteria, and use them as a common basis to derive a variety of generalization algorithms. Specifically we present algorithms for precondition generalization, order generalization, and possible correctness generalization of POPI plans. The systematic derivation of the generalization algorithms from the modal truth criterion obviates the need for carrying out a separate formal proof of correctness of the EBG algorithms. Our development also systematically explicates the tradeoffs among the spectrum of possible generalizations for POPI plans, and provides an empirical demonstration of the relative utility of EBG in partial ordering, as opposed to total ordering, planning frameworks.
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U2 - 10.1016/0004-3702(94)90011-6
DO - 10.1016/0004-3702(94)90011-6
M3 - Article
AN - SCOPUS:0028430019
SN - 0004-3702
VL - 67
SP - 29
EP - 70
JO - Artificial Intelligence
JF - Artificial Intelligence
IS - 1
ER -