Admissible Pruning Strategies based on plan minimality for Plan-Space Planning

Although plan space planners have been shown to be flexible and efficient in plan generation, they do suffer from the problem of "looping" - that is they may spend an inordinate amount of time. doing locally seemingly useful but globally useless refinements In this paper I review the anatomy of looping and argue that looping is intimately tied to the production of non minimal solutions I then propose two classes of admissible pruning techniques based on the notion of plan minimality I show that the first one is admissible for planners which do not protect their establishments but allow a precondition to be reestablished any number of times. The second one is admissible for planners which protect their establishments through causal links I also discuss the complexity of the proposed pruning strategies and then potential applications.

[1]  David A. McAllester,et al.  Systematic Nonlinear Planning , 1991, AAAI.

[2]  Subbarao Kambhampati,et al.  Planning as Refinement Search: A Unified Framework for Evaluating Design Tradeoffs in Partial-Order Planning , 1995, Artif. Intell..

[3]  Qiang Yang,et al.  AN EVALUATION OF THE TEMPORAL COHERENCE HEURISTIC IN PARTIAL‐ORDER PLANNING , 1994, Comput. Intell..

[4]  Eugene Fink,et al.  A Spectrum of Plan Justifications , 1993, AAAI 1993.

[5]  Drew McDermott,et al.  Regression planning , 1991, Int. J. Intell. Syst..

[6]  Mark A. Peot,et al.  Suspending Recursion in Causal-Link Planning , 1996, AIPS.

[7]  Daniel S. Weld,et al.  UCPOP: A Sound, Complete, Partial Order Planner for ADL , 1992, KR.

[8]  Mark Drummond,et al.  Exploiting temporal coherence in nonlinear plan construction , 1988, Comput. Intell..

[9]  S. Kambhampati,et al.  Universal classical planner: an algorithm for unifying state-space and plan-space planning , 1996 .

[10]  Mark A. Peot,et al.  Postponing Threats in Partial-Order Planning , 1993, AAAI.

[11]  Richard Fikes,et al.  Learning and Executing Generalized Robot Plans , 1993, Artif. Intell..

[12]  Manuela Veloso,et al.  An analysis of search techniques for a totally-ordered nonlinear planner , 1992 .

[13]  Subbarao Kambhampati,et al.  Failure Driven Dynamic Search Control for Partial Order Planners: An Explanation Based Approach , 1996, Artif. Intell..

[14]  S. Kambhampati,et al.  Learning Explanation-Based Search Control Rules for Partial Order Planning , 1994, AAAI.

[15]  David Chapman,et al.  Planning for Conjunctive Goals , 1987, Artif. Intell..

[16]  Steven Minton,et al.  Total-Order and Partial-Order Planning: A Comparative Analysis , 1994, J. Artif. Intell. Res..

[17]  Paul Morris,et al.  Admissible Criteria for Loop Control in Planning , 1990, AAAI.

[18]  Anthony Barrett,et al.  Partial-Order Planning: Evaluating Possible Efficiency Gains , 1994, Artificial Intelligence.

[19]  Subbarao Kambhampati,et al.  Learning search control rules for plan-space planners: factors affecting the performance , 1996 .