Planning Domain Definition Language (PDDL). PDDL2.1: An extension to PDDL for expressing temporal planning domains. In recent years research in the planning community has moved increasingly toward s application of planners to realistic problems involving both time and many typ es of resources. For example, interest in planning demonstrated by the space res earch community has inspired work in observation scheduling, planetary rover exploration and spacecraft control domains. Other temporal and resource-intensive domains including logistics planning, plant control and manufacturing have also helped to focus the community on the modelling and reasoning issues that must be confronted to make planning technology meet the challenges of application. The International Planning Competitions have acted as an important motivating force behind the progress that has been made in planning since 1998. The third com petition (held in 2002) set the planning community the challenge of handling time and numeric resources. This necessitated the development of a modelling language capable of expressing temporal and numeric properties of planning domains. In this paper we describe the language, PDDL2.1, that was used in the competition. We describe the syntax of the language, its formal semantics and the validation of concurrent plans. We observe that PDDL2.1 has considerable modelling power -- exceeding the capabilities of current planning technology -- and presents a number of important challenges to the research community.

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  1. Gerevini, Alfonso Emilio: Book review of: P. Haslum et. al., An introduction to the planning domain definition language (2020)
  2. Gerevini, Alfonso Emilio; Schubert, Lenhart: Discovering state constraints for planning with conditional effects in \textscDiscoplan. I (2020)
  3. Grastien, Alban; Scala, Enrico: CPCES: a planning framework to solve conformant planning problems through a counterexample guided refinement (2020)
  4. Krivic, Senka; Cashmore, Michael; Magazzeni, Daniele; Szedmak, Sandor; Piater, Justus: Using machine learning for decreasing state uncertainty in planning (2020)
  5. Kuchuganov, M. V.: Recursive definitions of tabular transformations (2020)
  6. Occhipinti Liberman, Andrés; Achen, Andreas; Rendsvig, Rasmus Kræmmer: Dynamic term-modal logics for first-order epistemic planning (2020)
  7. Pereira, Ramon Fraga; Oren, Nir; Meneguzzi, Felipe: Landmark-based approaches for goal recognition as planning (2020)
  8. Aineto, Diego; Jiménez Celorrio, Sergio; Onaindia, Eva: Learning action models with minimal observability (2019)
  9. Bartholomew, Michael; Lee, Joohyung: First-order stable model semantics with intensional functions (2019)
  10. Cimatti, Alessandro; Do, Minh; Micheli, Andrea; Roveri, Marco; Smith, David E.: Strong temporal planning with uncontrollable durations (2018)
  11. Fangkai Yang, Daoming Lyu, Bo Liu, Steven Gustafson: PEORL: Integrating Symbolic Planning and Hierarchical Reinforcement Learning for Robust Decision-Making (2018) arXiv
  12. Fuentetaja, Raquel; Borrajo, Daniel; de la Rosa, Tomás: Anticipation of goals in automated planning (2018)
  13. Pozanco, A.; Fernández, S.; Borrajo, D.: Learning-driven goal generation (2018)
  14. de la Rosa, Tomás; Fuentetaja, Raquel: Bagging strategies for learning planning policies (2017)
  15. Štolba, Michal; Komenda, Antonín: The MADLA planner: multi-agent planning by combination of distributed and local heuristic search (2017)
  16. Tenorth, Moritz; Beetz, Michael: Representations for robot knowledge in the \textscKnowRobframework (2017)
  17. Triska, Jan; Vychodil, Vilem: Logic of temporal attribute implications (2017)
  18. Calimeri, Francesco; Gebser, Martin; Maratea, Marco; Ricca, Francesco: Design and results of the Fifth Answer Set Programming Competition (2016)
  19. De Giacomo, Giuseppe; Lespérance, Yves; Patrizi, Fabio: Bounded situation calculus action theories (2016)
  20. Hernández-Orallo, José; Martínez-Plumed, Fernando; Schmid, Ute; Siebers, Michael; Dowe, David L.: Computer models solving intelligence test problems: progress and implications (2016) ioport

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