Learning Lifted STRIPS Models from Action Traces Alone: A Simple, General, and Scalable Solution
This work addresses the challenge of automated planning by providing a scalable and general solution for learning action models from traces, which is incremental as it builds on existing LOCM and SAT approaches.
The paper tackles the problem of learning STRIPS action models from action traces alone, which includes learning domain predicates, and introduces a novel approach that is scalable, sound, complete, and general without restrictions on the domain. The method is evaluated on standard domains like the 8-puzzle, involving hundreds of thousands of states and transitions, and verified on larger instances.
Learning STRIPS action models from action traces alone is a challenging problem as it involves learning the domain predicates as well. In this work, a novel approach is introduced which, like the well-known LOCM systems, is scalable, but like SAT approaches, is sound and complete. Furthermore, the approach is general and imposes no restrictions on the hidden domain or the number or arity of the predicates. The new learning method is based on an \emph{efficient, novel test} that checks whether the assumption that a predicate is affected by a set of action patterns, namely, actions with specific argument positions, is consistent with the traces. The predicates and action patterns that pass the test provide the basis for the learned domain that is then easily completed with preconditions and static predicates. The new method is studied theoretically and experimentally. For the latter, the method is evaluated on traces and graphs obtained from standard classical domains like the 8-puzzle, which involve hundreds of thousands of states and transitions. The learned representations are then verified on larger instances.