A unified framework for explanation-based generalization of partially ordered and partially instantiated plans

Subbarao Kambhampati, Smadar Kedar

Research output: Contribution to journalArticle

23 Citations (Scopus)

Abstract

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.

Original languageEnglish (US)
Pages (from-to)29-70
Number of pages42
JournalArtificial Intelligence
Volume67
Issue number1
DOIs
StatePublished - 1994

Fingerprint

Planning
Demonstrations
planning
Correctness

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics

Cite this

A unified framework for explanation-based generalization of partially ordered and partially instantiated plans. / Kambhampati, Subbarao; Kedar, Smadar.

In: Artificial Intelligence, Vol. 67, No. 1, 1994, p. 29-70.

Research output: Contribution to journalArticle

@article{ecd657cff7d943b89491440769049347,
title = "A unified framework for explanation-based generalization of partially ordered and partially instantiated plans",
abstract = "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.",
author = "Subbarao Kambhampati and Smadar Kedar",
year = "1994",
doi = "10.1016/0004-3702(94)90011-6",
language = "English (US)",
volume = "67",
pages = "29--70",
journal = "Artificial Intelligence",
issn = "0004-3702",
publisher = "Elsevier",
number = "1",

}

TY - JOUR

T1 - A unified framework for explanation-based generalization of partially ordered and partially instantiated plans

AU - Kambhampati, Subbarao

AU - Kedar, Smadar

PY - 1994

Y1 - 1994

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.

UR - http://www.scopus.com/inward/record.url?scp=0028430019&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=0028430019&partnerID=8YFLogxK

U2 - 10.1016/0004-3702(94)90011-6

DO - 10.1016/0004-3702(94)90011-6

M3 - Article

AN - SCOPUS:0028430019

VL - 67

SP - 29

EP - 70

JO - Artificial Intelligence

JF - Artificial Intelligence

SN - 0004-3702

IS - 1

ER -