Logic-Based Machine Learning: Recent Advances and Their Role in Neuro-Symbolic AI

Over the last two decades there has been a growing interest in logic-based machine learning, where the goal is to learn a logic program, called a hypothesis, that together with a given background knowledge explains a set of examples. Although logic-based machine learning has traditionally addressed the task of learning definite logic programs (with no negation), our logic-based machine learning approaches have extended this field to a wider class of formalisms for knowledge representation, captured by the answer set programming (ASP) semantics. The ASP formalism is truly declarative and due to its non-monotonicity it is particularly well suited to commonsense reasoning. It allows constructs such as choice rules, hard and weak constraints, and support for default inference and default assumptions. Choice rules and weak constraints are particularly useful for modelling human preferences, as the choice rules can represent the choices available to the user, and the weak constraints can specify which choices a human prefers. In the recent years we have made fundamental contributions to the field of logic-based machine learning by extending it to the learning of the full class of ASP programs and the first part of this talk provides an introduction to these results and to the general field of learning under the answer set semantics, referred here as learning from answer sets (LAS). To be applicable to real-world problems, LAS has to be tolerant to noise in the data, scalable over large search spaces, amenable to user-defined domain-specific optimisation criteria and capable of learning interpretable knowledge from structured and unstructured data. The second part of this talk shows how these problems are addressed by our recently proposed FastLAS approach for learning Answer Set Programs, which is targeted at solving restricted versions of observational and non-observational predicate learning from answer sets tasks. The advanced features of our family of LAS systems have made it possible to solve a variety of real-world problems in a manner that is data efficient, scalable and robust to noise. LAS can be combined with statistical learning methods to realise neuro-symbolic solutions that perform both fast, low-level prediction from unstructured data, and high-level logic-based learning of interpretable knowledge. The talk concludes with presenting two such neuro-symbolic solutions for respectively solving image classification problems in the presence of distribution shifts, and discovering sub-goal structures for reinforcement learning agents.