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Description
  • The International Planning Competitions have led to development of a standard modeling framework for describing planning domains and problems - Planning Domain Description Language (PDDL). The majority of planning research is done around problems modeled in PDDL though there are only a few applications adopting PDDL. The planning model of independent actions connected only via causal relations is very flexible, but it also makes plans less predictable (plans look different than expected by the users) and it is probably also one of the reasons of bad practical efficiency of current planners (%22visibly%22 wrong plans are blindly explored by the planners). In this paper we argue that grouping actions into flexible sub-plans is a way to overcome the efficiency problems. The idea is that instead of seeing actions as independent entities that are causally connected via preconditions and effects, we suggest using a form of finite state automaton (FSA) to describe the expected sequences of actions. Arcs in FSA are annotated by conditions guiding the planner to explore only %22proper%22 paths in the automaton. The second idea is composing primitive actions into meta-actions, which decreases the size of a FSA and makes planning much faster. The main motivation is to give users more control over the action sequencing with two primary goals: obtaining more predictable plans and improving efficiency of planning. The presented ideas originate from solving the Petrobras logistic problem, where this technique outperformed classical planning models.
  • The International Planning Competitions have led to development of a standard modeling framework for describing planning domains and problems - Planning Domain Description Language (PDDL). The majority of planning research is done around problems modeled in PDDL though there are only a few applications adopting PDDL. The planning model of independent actions connected only via causal relations is very flexible, but it also makes plans less predictable (plans look different than expected by the users) and it is probably also one of the reasons of bad practical efficiency of current planners (%22visibly%22 wrong plans are blindly explored by the planners). In this paper we argue that grouping actions into flexible sub-plans is a way to overcome the efficiency problems. The idea is that instead of seeing actions as independent entities that are causally connected via preconditions and effects, we suggest using a form of finite state automaton (FSA) to describe the expected sequences of actions. Arcs in FSA are annotated by conditions guiding the planner to explore only %22proper%22 paths in the automaton. The second idea is composing primitive actions into meta-actions, which decreases the size of a FSA and makes planning much faster. The main motivation is to give users more control over the action sequencing with two primary goals: obtaining more predictable plans and improving efficiency of planning. The presented ideas originate from solving the Petrobras logistic problem, where this technique outperformed classical planning models. (en)
Title
  • On Modeling Planning Problems: Experience From The Petrobras Challenge
  • On Modeling Planning Problems: Experience From The Petrobras Challenge (en)
skos:prefLabel
  • On Modeling Planning Problems: Experience From The Petrobras Challenge
  • On Modeling Planning Problems: Experience From The Petrobras Challenge (en)
skos:notation
  • RIV/00216208:11320/13:10195090!RIV14-GA0-11320___
http://linked.open...avai/predkladatel
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • I, P(GAP202/10/1188)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 93752
http://linked.open...ai/riv/idVysledku
  • RIV/00216208:11320/13:10195090
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Petrobras Challenge; Planning Problems; Modeling (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [0E4D21F3786B]
http://linked.open...v/mistoKonaniAkce
  • Mexico City, Mexico
http://linked.open...i/riv/mistoVydani
  • Heidelberg, Germany
http://linked.open...i/riv/nazevZdroje
  • Proceedings of the 12th Mexican International Conference on Artificial Intelligence (MICAI 2013), Part II
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Barták, Roman
  • Zhou, Neng-Fa
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
issn
  • 0302-9743
number of pages
http://purl.org/ne...btex#hasPublisher
  • Springer-Verlag
https://schema.org/isbn
  • 978-3-642-45110-2
http://localhost/t...ganizacniJednotka
  • 11320
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