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From skills to symbols: Learning symbolic representations for abstract high-level planning

Publication ,  Journal Article
Konidaris, G; Kaelbling, LP; Lozano-Perez, T
Published in: Journal of Artificial Intelligence Research
January 1, 2018

We consider the problem of constructing abstract representations for planning in highdimensional, continuous environments. We assume an agent equipped with a collection of high-level actions, and construct representations provably capable of evaluating plans composed of sequences of those actions. We first consider the deterministic planning case, and show that the relevant computation involves set operations performed over sets of states. We define the specific collection of sets that is necessary and sufficient for planning, and use them to construct a grounded abstract symbolic representation that is provably suitable for deterministic planning. The resulting representation can be expressed in PDDL, a canonical high-level planning domain language; we construct such a representation for the Playroom domain and solve it in milliseconds using an off-the-shelf planner. We then consider probabilistic planning, which we show requires generalizing from sets of states to distributions over states. We identify the specific distributions required for planning, and use them to construct a grounded abstract symbolic representation that correctly estimates the expected reward and probability of success of any plan. In addition, we show that learning the relevant probability distributions corresponds to specific instances of probabilistic density estimation and probabilistic classification. We construct an agent that autonomously learns the correct abstract representation of a computer game domain, and rapidly solves it. Finally, we apply these techniques to create a physical robot system that autonomously learns its own symbolic representation of a mobile manipulation task directly from sensorimotor data|point clouds, map locations, and joint angles|and then plans using that representation. Together, these results establish a principled link between high-level actions and abstract representations, a concrete theoretical foundation for constructing abstract representations with provable properties, and a practical mechanism for autonomously learning abstract high-level representations.

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Published In

Journal of Artificial Intelligence Research

DOI

ISSN

1076-9757

Publication Date

January 1, 2018

Volume

61

Start / End Page

215 / 289

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4611 Machine learning
  • 4603 Computer vision and multimedia computation
  • 4602 Artificial intelligence
  • 1702 Cognitive Sciences
  • 0801 Artificial Intelligence and Image Processing
  • 0102 Applied Mathematics
 

Citation

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Konidaris, G., Kaelbling, L. P., & Lozano-Perez, T. (2018). From skills to symbols: Learning symbolic representations for abstract high-level planning. Journal of Artificial Intelligence Research, 61, 215–289. https://doi.org/10.1613/jair.5575
Konidaris, G., L. P. Kaelbling, and T. Lozano-Perez. “From skills to symbols: Learning symbolic representations for abstract high-level planning.” Journal of Artificial Intelligence Research 61 (January 1, 2018): 215–89. https://doi.org/10.1613/jair.5575.
Konidaris G, Kaelbling LP, Lozano-Perez T. From skills to symbols: Learning symbolic representations for abstract high-level planning. Journal of Artificial Intelligence Research. 2018 Jan 1;61:215–89.
Konidaris, G., et al. “From skills to symbols: Learning symbolic representations for abstract high-level planning.” Journal of Artificial Intelligence Research, vol. 61, Jan. 2018, pp. 215–89. Scopus, doi:10.1613/jair.5575.
Konidaris G, Kaelbling LP, Lozano-Perez T. From skills to symbols: Learning symbolic representations for abstract high-level planning. Journal of Artificial Intelligence Research. 2018 Jan 1;61:215–289.

Published In

Journal of Artificial Intelligence Research

DOI

ISSN

1076-9757

Publication Date

January 1, 2018

Volume

61

Start / End Page

215 / 289

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 4611 Machine learning
  • 4603 Computer vision and multimedia computation
  • 4602 Artificial intelligence
  • 1702 Cognitive Sciences
  • 0801 Artificial Intelligence and Image Processing
  • 0102 Applied Mathematics