Graphplan

Fast planning through planning graph analysis. We introduce a new approach to planning in STRIPS-like domains based on constructing and analyzing a compact structure we call a planning graph. We describe a new planner, Graphplan, that uses this paradigm. Graphplan always returns a shortest possible partial-order plan, or states that no valid plan exists.par We provide empirical evidence in favor of this approach, showing that Graphplan outperforms the total-order planner, Prodigy, and the partial-order planner, UCPOP, on a variety of interesting natural and artificial planning problems. We also give empirical evidence that the plans produced by Graphplan are quite sensible. Since searches made by this approach are fundamentally different from the searches of other common planning methods, they provide a new perspective on the planning problem.


References in zbMATH (referenced in 181 articles , 1 standard article )

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  1. Hölldobler, Steffen; Störr, Hans-Peter: Solving the entailment problem in the fluent calculus using binary decision diagrams (2000)
  2. Jonsson, P.; Haslum, P.; Bäckström, C.: Towards efficient universal planning: A randomized approach (2000)
  3. Kambhampati, S.: Planning graph as a (dynamic) CSP: Exploiting EBL, DDB and other CSP search techniques in graphplan (2000)
  4. Nareyek, Alexander: AI planning in a constraint programming framework (2000)
  5. Nebel, B.: On the compilability and expressive power of propositional planning formalisms (2000)
  6. Baum, Eric B.: Manifesto for an evolutionary economics of intelligence (1999)
  7. Boutilier, C.; Dean, T.; Hanks, S.: Decision-theoretic planning: Structural assumptions and computational leverage (1999)
  8. Long, Derek; Fox, Maria: Efficient implementation of the plan graph in STAN (1999)
  9. McDermott, Drew: Using regression-match graphs to control search in planning (1999)
  10. Niemelä, Ilkka: Logic programs with stable model semantics as a constraint programming paradigm (1999)
  11. Rintanen, J.: Constructing conditional plans by a theorem-prover (1999)
  12. Bibel, W.: Let’s plan it deductively! (1998)
  13. Fox, M.; Long, D.: The automatic inference of state invariants in TIM (1998)
  14. Jonsson, Peter; Bäckström, Christer: State-variable planning under structural restrictions: algorithms and complexity (1998)
  15. Kaelbling, Leslie Pack; Littman, Michael L.; Cassandra, Anthony R.: Planning and acting in partially observable stochastic domains (1998)
  16. Kambhampati, Subbarao: On the relations between intelligent backtracking and failure-driven explanation-based learning in constraint satisfaction and planning (1998)
  17. Littman, M. L.; Goldsmith, J.; Mundhenk, M.: The computational complexity of probabilistic planning (1998)
  18. Muscettola, Nicola; Nayak, P. Pandurang; Pell, Barney; Williams, Brian C.: Remote Agent: to boldly go where no AI system has gone before (1998)
  19. Pollock, John L.: The logical foundations of goal-regression planning in autonomous agents (1998)
  20. Blum, Avrim L.; Furst, Merrick L.: Fast planning through planning graph analysis (1997)

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