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

arXiv · AI, language, vision and robotics · article · Aug 31, 2026 · UTC

Accurate action models are critical for effective planning. Existing action-model learning methods largely assume simple action representations or become computationally intractable when learning conditional and quantified effects. We present Online Hypothesis-Driven Conditional Action Model Learning (OHCAM), an online approach for learning action models with conditional and quantified effects from limited interactions with the environment. OHCAM maintains a belief over hypothesized action models and actively selects informative actions to reduce uncertainty by maximizing disagreement among co

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