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Cited by 22

Learning action models with minimal observability

Aineto, Diego, Jiménez Celorrio, Sergio, Onaindia, Eva·2019·Artificial Intelligence
DOI 10.1016/j.artint.2019.05.003
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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 Aineto et al., 2019

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Research works printed but not linked: 36

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  10. A. M. MacNally, N. Lipovetzky, M. Ramírez, A. R. Pearce, Action selection for transparent planning, in: International Conference on Autonomous Agents and MultiAgent Systems, (AAMAS-18), 2018, pp. 1327-1335.
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  14. A. Pozanco, Y. E.-Martín, S. Fernández, D. Borrajo, Counterplanning using goal recognition and landmarks, in: International Joint Confer- ence on Artificial Intelligence, (IJCAI-18), 2018, pp. 4808-4814.
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  36. G. Francès, M. Ramírez, N. Lipovetzky, H. Geffner, Purely declarative action descriptions are overrated: Classical planning with simulators, in: International Joint Conference on Artificial Intelligence, (IJCAI-17), 2017, pp. 4294-4301.

Cited by

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Authors
Aineto, D., Jiménez Celorrio, S., & Onaindia, E.
Venue
Artificial Intelligence
Publisher
Elsevier BV
Published
2019-10
Volume
275
Pages
104-137
ISSN
0004-3702
DOI
10.1016/j.artint.2019.05.003
Cited by
22
References
2
Language
en
Licence
Funders
  • Agencia Estatal de Investigación
  • Ministerio de Educación, Cultura y Deporte

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