To Örebro University

Automated planning (AI planning) solves complex sequential decision-making problems by searching for action sequences given a domain definition that models the actions a system can take. However, hand-crafting action models for complex problems is often challenging and cumbersome even for experts. To address this, we present Lifted Action Model Learning (LAML), a novel approach for learning planning domains from plan traces obtained by noisy observations of environment states. LAML integrates data lifting, decision-tree learning, and logic induction to derive abstract action models and generate planning domain representations. We evaluate our approach on 23 benchmark domains from the International Planning Competition, comparing its performance to state-of-the-art methods. Our evaluation considers multiple criteria, including domain reconstruction through comparison with reference domains, plan generation feasibility, comparison with historical plans, and plan validation success rate. Experimental results demonstrate that LAML not only reconstructs more accurate action models but also exhibits strong robustness to noise.