Discovering relational and numerical expressions from plan traces for learning action models

In this paper, we propose a domain learning process build on a machine learning-based process that, starting from plan traces with (partially known) intermediate states, returns a planning domain with numeric predicates, and expressive logical/arithmetic relations between domain predicates written in the planning domain definition language (PDDL). The novelty of our approach is that it can discover relations with little information about the ontology of the target domain to be learned. This is achieved by applying a selection of preprocessing, regression, and classification techniques to infer information from the input plan traces. These techniques are used to prepare the planning data, discover relational/numeric expressions, or extract the preconditions and effects of the domain’s actions. Our solution was evaluated using several metrics from the literature, taking as experimental data plan traces obtained from several domains from the International Planning Competition. The experiments demonstrate that our proposal—even with high levels of incompleteness—correctly learns a wide variety of domains discovering relational/arithmetic expressions, showing F-Score values above 0.85 and obtaining valid domains in most of the experiments.