Learning How to Ground a Plan - Partial Grounding in Classical Planning

D. Gnad, A. Torralba, M. Dom\'inguez, C. Areces, and F. Bustos. Learning How to Ground a Plan - Partial Grounding in Classical Planning. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019, pp. 7602–7609, 2019.

Download

[pdf] 

Abstract

Current classical planners are very successful in finding (non-optimal) plans, even for large planning instances. To do so, most planners rely on a preprocessing stage that computes a grounded representation of the task. Whenever the grounded task is too big to be generated (i.e., whenever this preprocess fails) the instance cannot even be tackled by the actual planner. To address this issue, we introduce a partial grounding approach that grounds only a projection of the task, when complete grounding is not feasible. We propose a guiding mechanism that, for a given domain, identifies the parts of a task that are relevant to find a plan by using off-the-shelf machine learning methods. Our empirical evaluation attests that the approach is capable of solving planning instances that are too big to be fully grounded.

BibTeX

@InCollection{Gnad2019,
  author =       "D. Gnad and A. Torralba and M. Dom{\'i}nguez and C.
                 Areces and F. Bustos",
  booktitle =    "The Thirty-Third {AAAI} Conference on Artificial
                 Intelligence, {AAAI} 2019, The Thirty-First Innovative
                 Applications of Artificial Intelligence Conference,
                 {IAAI} 2019, The Ninth {AAAI} Symposium on Educational
                 Advances in Artificial Intelligence, {EAAI} 2019,
                 Honolulu, Hawaii, USA, January 27 - February 1, 2019",
  title =        "Learning How to Ground a Plan - Partial Grounding in
                 Classical Planning",
  year =         "2019",
  pages =        "7602--7609",
  abstract =     "Current classical planners are very successful in
                 finding (non-optimal) plans, even for large planning
                 instances. To do so, most planners rely on a
                 preprocessing stage that computes a grounded
                 representation of the task. Whenever the grounded task
                 is too big to be generated (i.e., whenever this
                 preprocess fails) the instance cannot even be tackled
                 by the actual planner. To address this issue, we
                 introduce a partial grounding approach that grounds
                 only a projection of the task, when complete grounding
                 is not feasible. We propose a guiding mechanism that,
                 for a given domain, identifies the parts of a task that
                 are relevant to find a plan by using off-the-shelf
                 machine learning methods. Our empirical evaluation
                 attests that the approach is capable of solving
                 planning instances that are too big to be fully
                 grounded.",
  bibsource =    "dblp computer science bibliography, https://dblp.org",
  biburl =       "https://dblp.org/rec/conf/aaai/GnadTDAB19.bib",
  doi =          "10.1609/aaai.v33i01.33017602",
  timestamp =    "Wed, 25 Sep 2019 11:05:09 +0200",
  URL =          "https://doi.org/10.1609/aaai.v33i01.33017602",
  ISBN =         "978-1-57735-809-1",
}