AGENP: An ASGrammar-based GENerative Policy Framework. - Seraphin B. Calo; Irene Manotas; Geeth de Mel; Daniel Cunnington; Mark Law; Dinesh C. Verma; Alessandra Russo; Elisa Bertino (PADG@ESORICS, 2018)

Reduce Cognitive Burden on Drivers through Contextualising Environments. - Daniel Cunnington; Geeth de Mel; Darren Shaw (VTC Spring, 2018)

Towards a Neural-Symbolic Generative Policy Model. - Daniel Cunnington; Mark Law; Alessandra Russo; Elisa Bertino; Seraphin B. Calo (IEEE BigData, 2019)

Generative Policies for Coalition Systems - A Symbolic Learning Framework. - Elisa Bertino; Graham White 002; Jorge Lobo 001; John Ingham; Gregory H. Cirincione; Alessandra Russo; Mark Law; Seraphin B. Calo; Irene Manotas; Dinesh C. Verma; Amani Abu Jabal; Daniel Cunnington; et al. (ICDCS, 2019)

A Comparison Between Statistical and Symbolic Learning Approaches for Generative Policy Models. - Graham White 002; Daniel Cunnington; Mark Law; Elisa Bertino; Geeth de Mel; Alessandra Russo (ICMLA, 2019)

A Generative Policy Model for Connected and Autonomous Vehicles. - Daniel Cunnington; Irene Manotas; Mark Law; Geeth de Mel; Seraphin B. Calo; Elisa Bertino; Alessandra Russo (ITSC, 2019)

A Demonstration of Generative Policy Models in Coalition Environments. - Daniel Cunnington; Graham White 002; Mark Law; Geeth de Mel (PAAMS, 2019)

Synthetic Ground Truth Generation for Evaluating Generative Policy Models. - Daniel Cunnington; Graham White 002; Geeth de Mel (CoRR, 2019)

NSL: Hybrid Interpretable Learning From Noisy Raw Data. - Daniel Cunnington; Alessandra Russo; Mark Law; Jorge Lobo 001; Lance M. Kaplan (CoRR, 2020)

Inductive Learning of Complex Knowledge from Raw Data. - Daniel Cunnington; Mark Law; Jorge Lobo 001; Alessandra Russo (TFSOCTAI@AAAI Fall Symposium, 2021)

Towards Neural-Symbolic Learning to support Human-Agent Operations. - Daniel Cunnington; Mark Law; Alessandra Russo; Jorge Lobo 001; Lance M. Kaplan (FUSION, 2021)

FF-NSL: Feed-Forward Neural-Symbolic Learner. - Daniel Cunnington; Mark Law; Alessandra Russo; Jorge Lobo 001 (CoRR, 2021)

Neuro-Symbolic Learning of Answer Set Programs from Raw Data. - Daniel Cunnington; Mark Law; Jorge Lobo 001; Alessandra Russo (IJCAI, 2023)

Towards a Deeper Understanding of Concept Bottleneck Models Through End-to-End Explanation. - Jack Furby; Daniel Cunnington; Dave Braines; Alun D. Preece (CoRR, 2023)

Symbolic Learning for Material Discovery. - Daniel Cunnington; Flaviu Cipcigan; Rodrigo Neumann Barros Ferreira; Jonathan Booth (CoRR, 2023)

Cybersecurity in Motion: A Survey of Challenges and Requirements for Future Test Facilities of CAVs. - Ioannis Mavromatis; Theodoros Spyridopoulos; Pietro Edoardo Carnelli; Woon Hau Chin; Ahmed Khalil; Jennifer Chakravarty; Lucia Cipolina Kun; Robert J. Piechocki; Colin Robbins; Daniel Cunnington; Leigh Chase; Lamogha Chiazor; et al. (CoRR, 2023)

FFNSL: Feed-Forward Neural-Symbolic Learner. - Daniel Cunnington; Mark Law; Jorge Lobo 001; Alessandra Russo (Mach. Learn., 2023)

The Role of Foundation Models in Neuro-Symbolic Learning and Reasoning. - Daniel Cunnington; Mark Law; Jorge Lobo 001; Alessandra Russo (NeSy (1), 2024)

Can we Constrain Concept Bottleneck Models to Learn Semantically Meaningful Input Features? - Jack Furby; Daniel Cunnington; Dave Braines; Alun D. Preece (CoRR, 2024)