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LEARNING PRECONDITIONS FOR PLANNING FROM PLAN TRACES AND HTN STRUCTURE

  • Published:2005-11 Issue:4 Volume:21 Page:388-413
  • ISSN:0824-7935
  • Container-title:Computational Intelligence
  • language:en
  • Short-container-title:Computational Intell

Author:

Ilghami Okhtay,Nau Dana S.,Munoz-Avila Hector,Aha David W.

Publisher

Wiley

Subject

Artificial Intelligence,Computational Mathematics

Reference67 articles.

1. F.Bacchus, 2000 . Aips-00 planning competition . http://www.cs.toronto.edu/aips2000.

2. O-Plan: The open planning architecture

3. S.Edelkamp, and J.Hoffmann., 2004 . International planning competition . http://ls5-www.cs.uni-dortmund.de/~edelkamp/ipc-4/.

4. Complexity results for HTN planning

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1. Reconfigurable Behavior Trees: Towards an Executive Framework Meeting High-level Decision Making and Control Layer Features;2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC);2020-10-11

2. Planning meets activity recognition: Service coordination for intelligent buildings;Pervasive and Mobile Computing;2017-07

3. HTN planning: Overview, comparison, and beyond;Artificial Intelligence;2015-05

4. Learning and Planning (Intelligent Systems);Encyclopedia of Complexity and Systems Science;2015

5. Dynamic Web Service Composition Based on Service Integration and HTN Planning;2013 Seventh International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing;2013-07
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