An Architecture for Predicate Learning and its Uses in Planning

We introduce a new architecture for learning unary and binary predicates from positive and negative examples over relational structures with a fixed set of primitive predicates. The architecture, called Neural Description Logic Machine or NDLM, is based on a rich description logic which has the expressive power of 3-variable first-order logic, FO${}^3$. The architecture is restricted to predicates of arity one and two, and unlike related architectures such as neural logic machines (NLMs), edge-transformers, and (oblivious) 3-GNNs, it achieves this expressivity requiring quadratic rather than cubic space. The predicate learning task considered is a generalization and variation of inductive logical tasks such as concept learning and inductive logic programming but where the representation of the learned predicates is not given in terms of a first-order formula or logic program. Still we show that the learned, neural representation of concepts and roles is not fully opaque either, and in many cases, can be understood logically as well. We also show that a number of tasks in "learning for planning" can be reduced to predicate learning tasks of this form, including lifted STRIPS model learning from state-action traces, and general policy learning from plans. The performance of NDLMs is evaluated experimentally over a number of planning and concept learning tasks where it is compared with NLMs and state-of-the-art symbolic and neural unary and binary-relational learners.