Learning Action Models with Conditional and Quantified Effects via Uncertainty-Guided Exploration

arXiv:2608.30955 · cs.AI · Submitted 2026-08-31 · Read on arXiv

cs.AI

Submitted: 2026-08-31

Updated: 2026-08-31

Code: https://github.com/potassco/pddl-instances

Terminology

• Action Model Learning — 13× in this paper
• Benchmark — 10× in this paper · explained in 2 episodes
• Active Learning — 8× in this paper · explained in 6 episodes
• Sample Efficiency — 8× in this paper · explained in 4 episodes
• Robustness — 5× in this paper · explained in 5 episodes
• Active Exploration — 3× in this paper · explained in 2 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Grounding — 3× in this paper · explained in 3 episodes
• Information Gain — 3× in this paper · explained in 2 episodes
• Online Hypothesis-Driven Conditional Action Model Learning — 2× in this paper
• Segmentation Masks — 2× in this paper · explained in 2 episodes
• Trace — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Admissibility — 1× in this paper · explained in 2 episodes
• Bayesian Active Learning by Disagreement — 1× in this paper
• Clustering — 1× in this paper · explained in 2 episodes
• Hypergraph — 1× in this paper · explained in 2 episodes
• Task Planning — 1× in this paper · explained in 2 episodes

Sources

• SAM 3: Segment Anything with Concepts
• Bayesian Active Learning for Classification and Preference Learning
• Safe Learning of Lifted Action Models
• PDSketch: Integrated Planning Domain Programming and Learning

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