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 161 articles )

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  1. De Giacomo, Giuseppe; Gerevini, Alfonso Emilio; Patrizi, Fabio; Saetti, Alessandro; Sardina, Sebastian: Agent planning programs (2016)
  2. Ghosh, Kamalesh; Dasgupta, Pallab; Ramesh, S.: Automated planning as an early verification tool for distributed control (2015)
  3. 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)
  4. 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)
  5. Cai, Dunbo; Xu, Sheng; Zhao, Tongzhou; Zhang, Yanduo: On the completeness of pruning techniques for planning with conditional effects (2013)
  6. Satzger, Benjamin; Kramer, Oliver: Goal distance estimation for automated planning using neural networks and support vector machines (2013)
  7. Cugola, Gianpaolo; Ghezzi, Carlo; Pinto, Leandro Sales: DSOL: a declarative approach to self-adaptive service orchestrations (2012)
  8. Kolobov, Andrey; Mausam; Weld, Daniel S.: Discovering hidden structure in factored MDPs (2012)
  9. Lin, Szu-Yin; Lin, Guan-Ting; Chao, Kuo-Ming; Lo, Chi-Chun: A cost-effective planning graph approach for large-scale Web Service Composition (2012)
  10. Nguyen, Tuan Anh; Do, Minh; Gerevini, Alfonso Emilio; Serina, Ivan; Srivastava, Biplav; Kambhampati, Subbarao: Generating diverse plans to handle unknown and partially known user preferences (2012)
  11. Rintanen, Jussi: Planning as satisfiability: heuristics (2012)
  12. Baioletti, Marco; Milani, Alfredo; Poggioni, Valentina; Rossi, Fabio: Experimental evaluation of pheromone models in ACOPlan (2011)
  13. Bryce, Daniel; Cushing, William; Kambhampati, Subbarao: State agnostic planning graphs: deterministic, non-deterministic, and probabilistic planning (2011)
  14. Gerevini, Alfonso E.; Saetti, Alessandro; Serina, Ivan: Planning in domains with derived predicates through rule-action graphs and local search (2011)
  15. Hoffmann, J.: Analyzing search topology without running any search: on the connection between causal graphs and $h^+$ (2011)
  16. Joshi, S.; Khardon, R.: Probabilistic relational planning with first order decision diagrams (2011)
  17. Koller, Alexander; Petrick, Ronald P.A.: Experiences with planning for natural language generation (2011)
  18. Tu, Phan Huy; Son, Tran Cao; Gelfond, Michael; Morales, A.Ricardo: Approximation of action theories and its application to conformant planning (2011)
  19. Lang, Tobias; Toussaint, Marc: Planning with noisy probabilistic relational rules (2010)
  20. Rodríguez-Mier, Pablo; Mucientes, Manuel; Lama, Manuel; Couto, Miguel I.: Composition of web services through genetic programming (2010)

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