The FF Planning System: Fast Plan Generation Through Heuristic Search

We describe and evaluate the algorithmic techniques that are used in the FF planning system. Like the HSP system, FF relies on forward state space search, using a heuristic that estimates goal distances by ignoring delete lists. Unlike HSP's heuristic, our method does not assume facts to be independent. We introduce a novel search strategy that combines hill-climbing with systematic search, and we show how other powerful heuristic information can be extracted and used to prune the search space. FF was the most successful automatic planner at the recent AIPS-2000 planning competition. We review the results of the competition, give data for other benchmark domains, and investigate the reasons for the runtime performance of FF compared to HSP.

[1]  S. Siegel,et al.  Nonparametric Statistics for the Behavioral Sciences , 2022, The SAGE Encyclopedia of Research Design.

[2]  Richard Fikes,et al.  STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving , 1971, IJCAI.

[3]  Jie Cheng,et al.  Subgoal Ordering and Goal Augmentation for Heuristic Problem Solving , 1987, IJCAI.

[4]  Mark Drummond,et al.  Goal Ordering in Partially Ordered Plans , 1989, IJCAI.

[5]  Edwin P. D. Pednault,et al.  ADL: Exploring the Middle Ground Between STRIPS and the Situation Calculus , 1989, KR.

[6]  Jie Cheng,et al.  Ordering Problem Subgoals , 1989, IJCAI.

[7]  John W. Roach,et al.  A Theoretical Analysis of Conjunctive-Goal Problems , 1989, Artif. Intell..

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

[9]  Hector J. Levesque,et al.  Hard and Easy Distributions of SAT Problems , 1992, AAAI.

[10]  Steven E. Hampson,et al.  Large plateaus and plateau search in Boolean Satisfiability problems: When to give up searching and start again , 1993, Cliques, Coloring, and Satisfiability.

[11]  Tom Bylander,et al.  The Computational Complexity of Propositional STRIPS Planning , 1994, Artif. Intell..

[12]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

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

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

[15]  Drew McDermott,et al.  A Heuristic Estimator for Means-Ends Analysis in Planning , 1996, AIPS.

[16]  Jeremy Frank,et al.  When Gravity Fails: Local Search Topology , 1997, J. Artif. Intell. Res..

[17]  Craig A. Knoblock,et al.  Combining the Expressivity of UCPOP with the Efficiency of Graphplan , 1997, ECP.

[18]  Bernhard Nebel,et al.  Ignoring Irrelevant Facts and Operators in Plan Generation , 1997, ECP.

[19]  Blai Bonet,et al.  A Robust and Fast Action Selection Mechanism for Planning , 1997, AAAI/IAAI.

[20]  Amedeo Cesta,et al.  Recent Advances in AI Planning , 1997, Lecture Notes in Computer Science.

[21]  Subbarao Kambhampati,et al.  Understanding and Extending Graphplan , 1997, ECP.

[22]  Bernhard Nebel,et al.  Extending Planning Graphs to an ADL Subset , 1997, ECP.

[23]  David E. Smith,et al.  Conditional Effects in Graphplan , 1998, AIPS.

[24]  Jana Koehler,et al.  Solving Complex Planning Tasks Through Extraction of Subproblems , 1998, AIPS.

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

[26]  S. CorinR.AndersonDavidE.SmithDaniel,et al.  Conditional E ects in Graphplan , 1998 .

[27]  D. Long,et al.  E cient Implementation of the Plan Graph in STAN , 1999 .

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

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

[30]  Jana Koehler.  Handling of Conditional Effects and Negative Goals in IPP , 1999 .

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

[32]  Ioannis P. Vlahavas,et al.  GRT: A Domain Independent Heuristic for STRIPS Worlds Based on Greedy Regression Tables , 1999, ECP.

[33]  Bart Selman,et al.  Unifying SAT-based and Graph-based Planning , 1999, IJCAI.

[34]  Jörg Hoffmann,et al.  On Reasonable and Forced Goal Orderings and their Use in an Agenda-Driven Planning Algorithm , 2000, J. Artif. Intell. Res..

[35]  Ioannis P. Vlahavas,et al.  Exploiting State Constraints in Heuristic State-Space Planning , 2000, AIPS.

[36]  Jörg Hoffmann,et al.  On the Instantiation of ADL Operators Involving Arbitrary First-Order Formulas , 2000, PuK.

[37]  Jörg Hoffmann.  A Heuristic for Domain Independent Planning and its Use in an Enforced Hill-climbing Algorithm , 2000, PuK.

[38]  Patrik Haslum,et al.  Towards efficient universal planning: A randomized approach , 2000, Artif. Intell..

[39]  Jana Koehler,et al.  Elevator Control as a Planning Problem , 2000, AIPS.

[40]  Maria Fox,et al.  Extracting Route-Planning: First Steps in Automatic Problem Decomposition , 2000 .

[41]  Jörg Hoffmann.  A Heuristic for Domain Independent Planning and its Use in an Enforced Hill-climbing Algorithm , 2000, Planen und Konfigurieren.

[42]  Stefan Edelkamp,et al.  Heuristic Search Planning with BDDs , 2000, PuK.

[43]  Steffen Hölldobler,et al.  Solving the Entailment Problem in the Fluent Calculus Using Binary Decision Diagrams , 2000, Computational Logic.

[44]  Jorg Homann.  A Heuristic for Domain Independent Planning and Its Use in an Enforced Hill-Climbing Algorithm , 2000 .

[45]  Bernhard Nebel,et al.  On the Compilability and Expressive Power of Propositional Planning Formalisms , 1998, J. Artif. Intell. Res..

[46]  Blai Bonet,et al.  Planning as heuristic search , 2001, Artif. Intell..

[47]  Maria Fox,et al.  Hybrid STAN: Identifying and Managing Combinatorial Optimisation Sub- problems in Planning , 2001, IJCAI.

[48]  John K. Slaney,et al.  Blocks World revisited , 2001, Artif. Intell..

[49]  Jörg Hoffmann,et al.  Local Search Topology in Planning Benchmarks: An Empirical Analysis , 2001, IJCAI.