Reviving Partial Order Planning

This paper challenges the prevailing pessimism about the scalability of partial order planning (POP) algorithms by presenting several novel heuristic control techniques that make them competitive with the state of the art plan synthesis algorithms. Our key insight is that the techniques responsible for the efficiency of the currently successful planners–viz., distance based heuristics, reachability analysis and disjunctive constraint handling–can also be adapted to dramatically improve the efficiency of the POP algorithm. We implement our ideas in a variant of UCPOP called REPOP. Our empirical results show that in addition to dominating UCPOP, REPOP also convincingly outperforms Graphplan in several “parallel” domains. The plans generated by REPOP also tend to be better than those generated by Graphplan and state search planners in terms of execution flexibility.

[1]  Subbarao Kambhampati,et al.  Planning graph as the basis for deriving heuristics for plan synthesis by state space and CSP search , 2002, Artif. Intell..

[2]  Mark A. Peot,et al.  Threat-Removal Strategies for Partial-Order Planning , 1993, AAAI.

[3]  Maria Fox,et al.  The Automatic Inference of State Invariants in TIM , 1998, J. Artif. Intell. Res..

[4]  Patrik Haslum,et al.  Admissible Heuristics for Optimal Planning , 2000, AIPS.

[5]  Lenhart K. Schubert,et al.  Inferring State Constraints for Domain-Independent Planning , 1998, AAAI/IAAI.

[6]  Malik Ghallab,et al.  Representation and Control in IxTeT, a Temporal Planner , 1994, AIPS.

[7]  David E. Smith,et al.  Temporal Planning with Mutual Exclusion Reasoning , 1999, IJCAI.

[8]  M. Fox,et al.  Efficient Implementation of the Plan Graph in STAN , 2011, J. Artif. Intell. Res..

[9]  Martha E. Pollack,et al.  Least-Cost Flaw Repair: A Plan Refinement Strategy for Partial-Order Planning , 1994, AAAI.

[10]  Subbarao Kambhampati,et al.  Extracting Effective and Admissible State Space Heuristics from the Planning Graph , 2000, AAAI/IAAI.

[11]  Tom Bylander.  A Linear Programming Heuristic for Optimal Planning , 1997, AAAI/IAAI.

[12]  Subbarao Kambhampati,et al.  Planning Graph as a (Dynamic) CSP: Exploiting EBL, DDB and other CSP Search Techniques in Graphplan , 2000, J. Artif. Intell. Res..

[13]  Bart Selman,et al.  Pushing the Envelope: Planning, Propositional Logic and Stochastic Search , 1996, AAAI/IAAI, Vol. 2.

[14]  Subbarao Kambhampati,et al.  On the Role of Disjunctive Representations and Constraint Propagation in Refinement Planning , 1996, KR.

[15]  Blai Bonet,et al.  Planning as Heuristic Search: New Results , 1999, ECP.

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

[17]  Lenhart K. Schubert,et al.  Accelerating Partial-Order Planners: Some Techniques for Effective Search Control and Pruning , 1996, J. Artif. Intell. Res..

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

[19]  Martha E. Pollack,et al.  Passive and active decision postponement in plan generation , 1996 .

[20]  Avrim Blum,et al.  Fast Planning Through Planning Graph Analysis , 1995, IJCAI.

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

[22]  Nicola Muscettola,et al.  Planning in Interplanetary Space: Theory and Practice , 2000, AIPS.

[23]  Daniel S. Weld.  An Introduction to Least Commitment Planning , 1994, AI Mag..

[24]  Drew McDermott,et al.  Using Regression-Match Graphs to Control Search in Planning , 1999, Artif. Intell..

[25]  Bernhard Nebel,et al.  The FF Planning System: Fast Plan Generation Through Heuristic Search , 2011, J. Artif. Intell. Res..

[26]  Christer Bäckström.  Computational Aspects of Reordering Plans , 1998, J. Artif. Intell. Res..

[27]  David Jo n,et al.  Least-Cost Flaw Repair: A Plan Refinement Strategy for Partial-Order Planning , 1994 .