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Learning Actions with Symbolic Literals and Continuous Effects for a Waypoint Navigation Simulation

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Intelligent Systems and Applications (IntelliSys 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1250))

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Abstract

We present an algorithm for learning planning actions for waypoint simulations, a crucial subtask for robotics, gaming, and transportation agents that must perform locomotion behavior. Our algorithm is capable of learning operator’s symbolic literals and continuous effects even under noisy training data. It accepts as input a set of preprocessed positive and negative simulation-generated examples. It identifies symbolic preconditions using a MAX-SAT constraint solver and learns numeric preconditions and effects as continuous functions of numeric state variables by fitting a logistic regression model. We test the correctness of the learned operators by solving test problems and running the resulting plans on the simulator.

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Notes

  1. 1.

    https://simpy.readthedocs.io/en/latest/.

  2. 2.

    https://www.json.org/.

  3. 3.

    https://github.com/jponf/wpm1py.

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Author information

Authors and Affiliations

  1. Lehigh University, Bethlehem, PA, 18015, USA

    Morgan Fine-Morris & Hector Muños-Avila

  2. Knexus Research Corporation, National Harbor, MD, 20745, USA

    Bryan Auslander & Kalyan Gupta

Authors
  1. Morgan Fine-Morris
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  2. Bryan Auslander
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  3. Hector Muños-Avila
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  4. Kalyan Gupta
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Corresponding author

Correspondence to Hector Muños-Avila .

Editor information

Editors and Affiliations

  1. Saga University, Saga, Japan

    Kohei Arai

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

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Fine-Morris, M., Auslander, B., Muños-Avila, H., Gupta, K. (2021). Learning Actions with Symbolic Literals and Continuous Effects for a Waypoint Navigation Simulation. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, vol 1250. Springer, Cham. https://doi.org/10.1007/978-3-030-55180-3_7

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