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A Deductive Database Approach to A.I. Planning

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Abstract

In this paper, we show that the classical A.I. planning problem can be modelled using simple database constructs with logic-based semantics. The approach is similar to that used to model updates and nondeterminism in active database rules. We begin by showing that planning problems can be automatically converted to Datalog1S programs with nondeterministic choice constructs, for which we provide a formal semantics using the concept of stable models. The resulting programs are characterized by a syntactic structure (XY-stratification) that makes them amenable to efficient implementation using compilation and fixpoint computation techniques developed for deductive database systems. We first develop the approach for sequential plans, and then we illustrate its flexibility and expressiveness by formalizing a model for parallel plans, where several actions can be executed simultaneously. The characterization of parallel plans as partially ordered plans allows us to develop (parallel) versions of partially ordered plans that can often be executed faster than the original partially ordered plans.

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Authors and Affiliations

  1. Dipartimento di Informatica, Università di Pisa Corso Italia 40, 56125, Pisa, Italy

    Antonio Brogi

  2. Computer Science Department, University of Maryland College Park, MD, 20742, USA

    V.S. Subrahmanian

  3. Computer Science Department, University of California, Los Angeles, CA, 90025, USA

    Carlo Zaniolo

Authors
  1. Antonio Brogi
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  2. V.S. Subrahmanian
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  3. Carlo Zaniolo
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Correspondence to Carlo Zaniolo.

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Brogi, A., Subrahmanian, V. & Zaniolo, C. A Deductive Database Approach to A.I. Planning. Journal of Intelligent Information Systems 20, 215–253 (2003). https://doi.org/10.1023/A:1022808724136

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