PDDL

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.


References in zbMATH (referenced in 80 articles , 1 standard article )

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  1. Cimatti, Alessandro; Do, Minh; Micheli, Andrea; Roveri, Marco; Smith, David E.: Strong temporal planning with uncontrollable durations (2018)
  2. de la Rosa, Tomás; Fuentetaja, Raquel: Bagging strategies for learning planning policies (2017)
  3. Štolba, Michal; Komenda, Antonín: The MADLA planner: multi-agent planning by combination of distributed and local heuristic search (2017)
  4. Tenorth, Moritz; Beetz, Michael: Representations for robot knowledge in the KnowRob framework (2017)
  5. Triska, Jan; Vychodil, Vilem: Logic of temporal attribute implications (2017)
  6. Calimeri, Francesco; Gebser, Martin; Maratea, Marco; Ricca, Francesco: Design and results of the Fifth Answer Set Programming Competition (2016)
  7. De Giacomo, Giuseppe; Lespérance, Yves; Patrizi, Fabio: Bounded situation calculus action theories (2016)
  8. Hernández-Orallo, José; Martínez-Plumed, Fernando; Schmid, Ute; Siebers, Michael; Dowe, David L.: Computer models solving intelligence test problems: progress and implications (2016) ioport
  9. Kaldeli, Eirini; Lazovik, Alexander; Aiello, Marco: Domain-independent planning for services in uncertain and dynamic environments (2016)
  10. Eppe, Manfred; Bhatt, Mehul: A history based approximate epistemic action theory for efficient postdictive reasoning (2015)
  11. Ghosh, Kamalesh; Dasgupta, Pallab; Ramesh, S.: Automated planning as an early verification tool for distributed control (2015)
  12. Ji, Jianmin; Lin, Fangzhen: Position systems in dynamic domains (2015)
  13. Kuchuganov, M. V.: Systems of relational transformations: rules and realizability criterion (2015)
  14. Linares López, Carlos; Jiménez Celorrio, Sergio; García Olaya, Ángel: The deterministic part of the seventh international planning competition (2015) ioport
  15. Nyolt, Martin; Krüger, Frank; Yordanova, Kristina; Hein, Albert; Kirste, Thomas: Marginal filtering in large state spaces (2015) ioport
  16. Piacentini, Chiara; Alimisis, Varvara; Fox, Maria; Long, Derek: An extension of metric temporal planning with application to AC voltage control (2015)
  17. Son, Tran Cao; Pontelli, Enrico; Baral, Chitta: A non-monotonic goal specification language for planning with preferences (2015)
  18. Thielscher, Michael: Simulation of action theories and an application to general game-playing robots (2015)
  19. Ghallab, Malik; Nau, Dana; Traverso, Paolo: The actor’s view of automated planning and acting: a position paper (2014) ioport
  20. Helmert, Malte; Haslum, Patrik; Hoffmann, Jörg; Nissim, Raz: Merge-and-shrink abstraction, A method for generating lower bounds in factored state spaces (2014)

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