5 papers
Learning and Enforcing Context-Sensitive Control for LLMs
Mohammad Albinhassan, Pranava Madhyastha, Mark Law +1
Controlling the output of Large Language Models (LLMs) through context-sensitive constraints has emerged as a promising approach to overcome the limitations of Context-Free Grammar…
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…
Data-Dependent Goal Modeling for ML-Enabled Law Enforcement Systems
Dalal Alrajeh, Vesna Nowack, Patrick Benjamin +15
Investigating serious crimes is inherently complex and resource-constrained. Law enforcement agencies (LEAs) grapple with overwhelming volumes of offender and incident data, making…
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…
A Unifying Framework for Learning Argumentation Semantics
Zlatina Mileva, Antonis Bikakis, Fabio Aurelio D'Asaro +2
Argumentation is a very active research field of Artificial Intelligence concerned with the representation and evaluation of arguments used in dialogues between humans and/or artif…