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. Grastien, Alban; Scala, Enrico: CPCES: a planning framework to solve conformant planning problems through a counterexample guided refinement (2020)
  2. Pereira, Ramon Fraga; Oren, Nir; Meneguzzi, Felipe: Landmark-based approaches for goal recognition as planning (2020)
  3. Shekhar, Shashank; Brafman, Ronen I.: Representing and planning with interacting actions and privacy (2020)
  4. Spies, David; You, Jia-Huai; Hayward, Ryan: Domain-independent cost-optimal planning in ASP (2019)
  5. Bernardini, Sara; Fagnani, Fabio; Smith, David E.: Extracting mutual exclusion invariants from lifted temporal planning domains (2018)
  6. Gnad, Daniel; Hoffmann, Jörg: Star-topology decoupled state space search (2018)
  7. Bohy, Aaron; Bruyère, Véronique; Raskin, Jean-François; Bertrand, Nathalie: Symblicit algorithms for mean-payoff and shortest path in monotonic Markov decision processes (2017)
  8. Egly, Uwe; Kronegger, Martin; Lonsing, Florian; Pfandler, Andreas: Conformant planning as a case study of incremental QBF solving (2017)
  9. Steinmetz, Marcel; Hoffmann, Jörg: State space search nogood learning: online refinement of critical-path dead-end detectors in planning (2017)
  10. Surynek, Pavel: Time-expanded graph-based propositional encodings for makespan-optimal solving of cooperative path finding problems (2017)
  11. De Giacomo, Giuseppe; Gerevini, Alfonso Emilio; Patrizi, Fabio; Saetti, Alessandro; Sardina, Sebastian: Agent planning programs (2016)
  12. Ghosh, Kamalesh; Dasgupta, Pallab; Ramesh, S.: Automated planning as an early verification tool for distributed control (2015)
  13. Rieffel, Eleanor G.; Venturelli, Davide; O’Gorman, Bryan; Do, Minh B.; Prystay, Elicia M.; Smelyanskiy, Vadim N.: A case study in programming a quantum annealer for hard operational planning problems (2015)
  14. Grześ, Marek; Hoey, Jesse; Khan, Shehroz S.; Mihailidis, Alex; Czarnuch, Stephen; Jackson, Dan; Monk, Andrew: Relational approach to knowledge engineering for POMDP-based assistance systems as a translation of a psychological model (2014) ioport
  15. Brafman, Ronen I.; Domshlak, Carmel: On the complexity of planning for agent teams and its implications for single agent planning (2013)
  16. Cai, Dunbo; Xu, Sheng; Zhao, Tongzhou; Zhang, Yanduo: On the completeness of pruning techniques for planning with conditional effects (2013)
  17. Cooper, Martin C.; Maris, Frédéric; Régnier, Pierre: Managing temporal cycles in planning problems requiring concurrency (2013)
  18. Satzger, Benjamin; Kramer, Oliver: Goal distance estimation for automated planning using neural networks and support vector machines (2013)
  19. Cugola, Gianpaolo; Ghezzi, Carlo; Pinto, Leandro Sales: DSOL: a declarative approach to self-adaptive service orchestrations (2012)
  20. Gebser, Martin; Kaufmann, Roland; Schaub, Torsten: Gearing up for effective ASP planning (2012)

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