6 papers
Ultra Strong Machine Learning: LLM-Generated Explanations Do Not Yet Suffice for Teaching Humans Active Learning Strategy
Lun Ai, Johannes Langer, Ute Schmid +1
Active learning is a general learning mechanism shared by artificial and human learners. Whether AI can teach humans such a strategy that transfers across domains is an open questi…
Active learning of digenic functions with boolean matrix logic programming
Lun Ai, Stephen H. Muggleton, Shi-shun Liang +1
We apply logic-based machine learning techniques to facilitate cellular engineering and drive biological discovery, based on comprehensive databases of metabolic processes called g…
Simulating Petri nets with Boolean Matrix Logic Programming
Lun Ai, Stephen H. Muggleton, Shi-Shun Liang +1
Recent attention to relational knowledge bases has sparked a demand for understanding how relations change between entities. Petri nets can represent knowledge structure and dynami…
Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models
Lun Ai, Stephen H. Muggleton, Shi-Shun Liang +1
Reasoning about hypotheses and updating knowledge through empirical observations are central to scientific discovery. In this work, we applied logic-based machine learning methods…
Meta-Interpretive Learning as Metarule Specialisation
Stassa Patsantzis, Stephen H. Muggleton
In Meta-Interpretive Learning (MIL) the metarules, second-order datalog clauses acting as inductive bias, are manually defined by the user. In this work we show that second-order m…
Turning 30: New Ideas in Inductive Logic Programming
Andrew Cropper, Sebastijan Dumančić, Stephen H. Muggleton
Common criticisms of state-of-the-art machine learning include poor generalisation, a lack of interpretability, and a need for large amounts of training data. We survey recent work…