Learning action models with minimal observability

DOI10.1016/J.ARTINT.2019.05.003zbMath1478.68277OpenAlexW2945938295WikidataQ127872914 ScholiaQ127872914MaRDI QIDQ2321324

Diego Aineto, Sergio Jiménez Celorrio, Eva Onaindia

Publication date: 28 August 2019

Published in: Artificial Intelligence (Search for Journal in Brave)

Full work available at URL: http://hdl.handle.net/10251/144560

zbMATH Keywords

machine learningAI planningaction model learning

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (2)

A constraint-based approach to learn temporal features on action models from multiple plans ⋮ A Comprehensive Framework for Learning Declarative Action Models

Uses Software

• PDDL

Cites Work

• Learning action models from plan examples using weighted MAX-SAT
• Reasoning about action. II: The qualification problem
• Model-lite planning: case-based vs. model-based approaches
• STRIPS: A new approach to the application of theorem proving to problem solving
• From Skills to Symbols: Learning Symbolic Representations for Abstract High-Level Planning
• The complexity of theorem-proving procedures
• Blocks World revisited
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