Efficient Plan Adaptation through Replanning Windows and Heuristic Goals

  • Authors:

  • Alfonso E. Gerevini;Ivan Serina

  • Affiliations:

  • (Correspd.) Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Brescia, via Branze 38, 25123 Brescia, Italy. gerevini@ing.unibs.it;Free University of Bozen - Bolzano, Viale Ratisbona 16, 39042 Bressanone, Italy. ivan.serina@unibz.it

  • Venue:
  • Fundamenta Informaticae - RCRA 2008 Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion
  • Year:
  • 2010

  • Preface

    Fundamenta Informaticae - RCRA 2008 Experimental Evaluation of Algorithms for Solving Problems with Combinatorial Explosion

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Abstract

Fast plan adaptation is important in many AI applications. From a theoretical point of view, in the worst case adapting an existing plan to solve a new problem is no more efficient than a complete regeneration of the plan. However, in practice plan adaptation can be much more efficient than plan generation, especially when the adapted plan can be obtained by performing a limited amount of changes to the original plan. In this paper, we investigate a domain-independent method for plan adaptation that modifies the original plan by replanning within limited temporal windows containing portions of the plan that need to be revised. Each window is associated with a particular replanning subproblem that contains some “heuristic goals” facilitating the plan adaptation, and that can be solved using different planning methods. An experimental analysis shows that, in practice, adapting a given plan for solving a new problem using our techniques can be much more efficient than replanning from scratch.