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Cited by 206AI-based Problem Solving and PlanningLogic, Reasoning, and KnowledgeReinforcement Learning in Robotics

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Learning action models from plan examples using weighted MAX-SAT

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Yang, Qiang, Wu, Kangheng, Jiang, Yunfei·2007·Artificial Intelligence
DOI 10.1016/j.artint.2006.11.005
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Crossref · 10.1016/j.artint.2006.11.005

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  • The symbolic analogue of learning an action model inside a planning loop is classical action-model learning.

    “The generate-test-repair loop is Reflexion-style iteration [Shinn et al., 2023, Madaan et al., 2023, Chen et al., 2024], and the symbolic analogue of learning an action model inside a planning loop is classical action-model learning [Lamanna et al., 2021, Yang et al., 2007, Aineto et al., 2019].”

    From OPINE-World: Programmatic World Modeling with Ontology-error-Prioritized Interactive Exploration for ARC-AGI-3 (2026) · cites this paper as Yang et al., 2007

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Research works printed but not linked: 34

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  12. Ilghami, 2003, Learning preconditions for planning from plan traces and HTN structure, Journal of Artificial Intelligence Research, 20, 379
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  14. O. Ilghami, D.S. Nau, H. Munoz-Avila, Camel: Learning method preconditions for htn planning, in: Proceedings of the Sixth International Conference on AI Planning and Scheduling AIPS-02, Toulouse, France, 2002, pp. 168–178
  15. Jeroslow, 1990, Solving propositional satisfiability problems, Annals of Mathematics and AI, 1, 167
  16. H. Kautz, B. Selman, Pushing the envelope: Planning, propositional logic, and stochastic search, in: Proceedings of the Thirteenth National Conference on Artificial Intelligence (AAAI 1996), Portland, OR, 1996, pp. 1194–1201
  17. H. Kautz, B. Selman, Y. Jiang, A general stochastic approach to solving problems with hard and soft constraints, in: The Satisfiability Problem: Theory and Applications, 1997 · DOI
  18. T. Leo McCluskey, D. Liu, R.M. Simpson, GIPO II: HTN planning in a tool-supported knowledge engineering environment, in: Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS 2003), Trento, Italy, 2003, pp. 92–101
  19. M.W. Moskewicz, C.F. Madigan, Y. Zhao, L. Zhang, S. Malik, Chaff: Engineering an efficient sat solver, in: Proceedings of the 38th Design Automation Conference (DAC), 2001 · DOI
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  30. Yang, 1990, Formalizing planning knowledge for hierarchical planning, Computational Intelligence Journal, 6, 12, 10.1111/j.1467-8640.1990.tb00126.x · DOI
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Metadata

Authors
Yang, Q., Wu, K., & Jiang, Y.
Affiliations
  • Hong Kong University of Science and Technology (HK)
  • Sun Yat-sen University (CN)
Corresponding author
Yang, Qiang
ORCID
Venue
Artificial Intelligence
Publisher
Elsevier BV
Published
2007-02
Volume
171
Issue
2-3
Pages
107-143
ISSN
0004-3702 (ISSN-L)
DOI
10.1016/j.artint.2006.11.005
OpenAlex
W2024113144
Cited by
206
References
2
Citations by year
2026: 4 · 2025: 3 · 2024: 10 · 2023: 8 · 2022: 11 · 2021: 19 · 2020: 17 · 2019: 15 · 2018: 16 · 2017: 11 · 2016: 9 · 2015: 7 · 2014: 21 · 2013: 14 · 2012: 10
FWCI
6.80
Type
journal article
Language
en
Open access
Licence
Locations
  • Artificial Intelligence (publishedVersion)
  • Rare & Special e-Zone (The Hong Kong University of Science and Technology) (submittedVersion)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI-based Problem Solving and Planning
  • Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
  • Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
Keywords
action model learning, weighted MaxSAT, arms, PDDL, automated planning, partial state information, propositional satisfiability

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