Integrating Acting, Planning, and Learning in Hierarchical Operational Models

Authors

  • Sunandita Patra University of Maryland, College Park
  • James Mason University of Maryland, College Park
  • Amit Kumar University of Maryland, College Park
  • Malik Ghallab LAAS-CNRS
  • Paolo Traverso Fondazione Bruno Kessler
  • Dana Nau University of Maryland, College Park

DOI:

https://doi.org/10.1609/icaps.v30i1.6743

Abstract

We present new planning and learning algorithms for RAE, the Refinement Acting Engine (Ghallab, Nau, and Traverso 2016). RAE uses hierarchical operational models to perform tasks in dynamically changing environments. Our planning procedure, UPOM, does a UCT-like search in the space of operational models in order to find a near optimal method to use for the task and context at hand. Our learning strategies acquire, from online acting experiences and/or simulated planning results, a mapping from decision contexts to method instances as well as a heuristic function to guide UPOM. Our experimental results show that UPOM and our learning strategies significantly improve RAE's performance in four test domains using two different metrics: efficiency and success ratio.

ICAPS 2020 Proceedings Cover

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Published

2020-06-01

How to Cite

Patra, S., Mason, J., Kumar, A., Ghallab, M., Traverso, P., & Nau, D. (2020). Integrating Acting, Planning, and Learning in Hierarchical Operational Models. Proceedings of the International Conference on Automated Planning and Scheduling, 30(1), 478-487. https://doi.org/10.1609/icaps.v30i1.6743

Issue

Vol. 30 (2020): Proceedings of the Thirtieth International Conference on Automated Planning and Scheduling

Section

Knowledge Engineering