A new representation and associated algorithms for generalized planning

Siddharth Srivastava, Neil Immerman, Shlomo Zilberstein

Research output: Contribution to journalArticlepeer-review

43 Scopus citations

Abstract

Constructing plans that can handle multiple problem instances is a longstanding open problem in AI. We present a framework for generalized planning that captures the notion of algorithm-like plans and unifies various approaches developed for addressing this problem. Using this framework, and building on the TVLA system for static analysis of programs, we develop a novel approach for computing generalizations of classical plans by identifying sequences of actions that will make measurable progress when placed in a loop. In a wide class of problems that we characterize formally in the paper, these methods allow us to find generalized plans with loops for solving problem instances of unbounded sizes and also to determine the correctness and applicability of the computed generalized plans. We demonstrate the scope and scalability of the proposed approach on a wide range of planning problems.

Original languageEnglish (US)
Pages (from-to)615-647
Number of pages33
JournalArtificial Intelligence
Volume175
Issue number2
DOIs
StatePublished - Feb 2011
Externally publishedYes

Keywords

  • Automated planning
  • Plan verification
  • Plans with loops

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language
  • Artificial Intelligence

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