21 citations · 66 across the 14 of their papers we have counts for
6 papers · 1 filter
Failure Detection in Chemical Processes Using Symbolic Machine Learning: A Case Study on Ethylene Oxidation
Julien Amblard, Niklas Groll, Matthew Tait +3
Over the past decade, Artificial Intelligence has significantly advanced, mostly driven by large-scale neural approaches. However, in the chemical process industry, where safety is…
LearnAD: Learning Interpretable Rules for Brain Networks in Alzheimer's Disease Classification
Thomas Andrews, Mark Law, Sara Ahmadi-Abhari +1
We introduce LearnAD, a neuro-symbolic method for predicting Alzheimer's disease from brain magnetic resonance imaging data, learning fully interpretable rules. LearnAD applies sta…
Hierarchies of Reward Machines
Daniel Furelos-Blanco, Mark Law, Anders Jonsson +2
Reward machines (RMs) are a recent formalism for representing the reward function of a reinforcement learning task through a finite-state machine whose edges encode subgoals of the…
FF-NSL: Feed-Forward Neural-Symbolic Learner
Daniel Cunnington, Mark Law, Alessandra Russo +1
Logic-based machine learning aims to learn general, interpretable knowledge in a data-efficient manner. However, labelled data must be specified in a structured logical form. To ad…
NSL: Hybrid Interpretable Learning From Noisy Raw Data
Daniel Cunnington, Alessandra Russo, Mark Law +2
Inductive Logic Programming (ILP) systems learn generalised, interpretable rules in a data-efficient manner utilising existing background knowledge. However, current ILP systems re…
Induction of Subgoal Automata for Reinforcement Learning
Daniel Furelos-Blanco, Mark Law, Alessandra Russo +2
In this work we present ISA, a novel approach for learning and exploiting subgoals in reinforcement learning (RL). Our method relies on inducing an automaton whose transitions are…