Publications

D. Varghese, D. Cyrus, S. Patsantzis, J. Trewern, A. Treloar, A. Hunter and A. Tamaddoni-Nezhad, One-Shot Learning of Autonomous Behaviour: A Meta-interpretive Learning approach, In Proc. of the 33rd Int. Conf. on Inductive Logic Programming (ILP), Springer, 2024 (In-Press).

S. Patsantzis and A. Tamaddoni-Nezhad, From model-based learning to model-free behaviour with Meta-Interpretive Learning, In Proc. of the 33rd Int. Conf. on Ind. Log. Prog (ILP), Springer, 2024 (In-Press).

Z. Chaghazardi, S. Fallah, and A. Tamaddoni-Nezhad, Trustworthy Vision for Autonomous Vehicles: A Robust Logic-infused Deep Learning Approach, In Proc. of the IEEE Int. Conf. Intel. Tran. Sys (ITSC), 2024 (In-Press).

D. Cyrus, D. Varghese, and A. Tamaddoni-Nezhad, An Inductive Logic Programming approach for feature-range discovery, In Proc. of the 33rd Int. Conf. on ILP, Springer, 2024 (In-Press, Best Student Paper Award)

Z. Chaghazardi, S. Fallah, and A. Tamaddoni-Nezhad, Leveraging Inductive Logic Programming and Deep Learning for Trustworthy Vision, In Proc. of the 33rd Int. Conf. on ILP, Springer, 2024 (In-Press).

J. Trewern and S. Patsantzis and Alireza Tamaddoni-Nezhad, Meta-Interpretive learning as Second Order Resolution, In Proc. of the 33rd Int. Conf. on Inductive Logic Programming (ILP), Springer, 2024 (In-Press).

D. Varghese, G Afroozi Milani, and A. Tamaddoni-Nezhad, Towards enhancing LLMs with logic-based reasoning, In Proc. of the 4th Int. Conf. on Learn and Reasoning (IJCLR), Springer, 2024 (In-Press).

D Cyrus, J Trewern, A Tamaddoni-Nezhad, Meta Interpretive Learning of Fractals. In Proc. of the 32nd Int Conf on Inductive Logic Programming, ILP 2023, pp. 166-174, Springer, 2023

D Varghese, R Bauer, A Tamaddoni-Nezhad, Few-shot learning of diagnostic rules for neurodegenerative diseases using Inductive Logic Programming, In Proc. of the Int Conf on ILP, pp. 109-123, Springer, 2023

Z Chaghazardi, S Fallah, A Tamaddoni-Nezhad, A Logic-based Compositional Generalisation Approach for Robust Traffic Sign Detection, In IJCAI 2023 Workshop on Knowledge-Based Compos Generalization, 2023

D Cyrus, G Afroozi Milani, A Tamaddoni-Nezhad, Explainable Game Strategy Rule Learning from Video. In Proc. of 17th Int. Rule Challenge Conf on RuleML+RR Challenge, 8-10, 2023.

Z Chaghazardi, S Fallah, A Tamaddoni-Nezhad, Explainable and Trustworthy Traffic Sign Detection for Safe Autonomous Driving: An ILP Approach, In Proc. of Int Conf on Logic Programming, 201-212, 2023.

G. Afroozi Milani, D Cyrus, A Tamaddoni-Nezhad, Towards One-Shot Learning for Text Classification using ILP, In Proc of Int Conf on Logic Programming, 69-79, 2023

D. Varghese, D. Barroso-Bergada, D. Bohan and A. Tamaddoni-Nezhad, Efficient Abductive Learning of Microbial Interactions using Meta Inverse Entailment, In Proc. of the 31th Int. Conf. on Inductive Logic Programming, 127-141, Springer, 2023 (Best Application Paper Award)

M. Yildirim, S. Mozaffari, L McCutcheon, M Dianati, A Tamaddoni-Nezhad, S Fallah, Prediction based decision making for autonomous highway driving, In Proc. of IEEE 25th Int Conf. on Intel. Trans. Sys (ITSC), 138-145, 2022

D. Varghese, U. Patel, P. Krause, A. Tamaddoni-Nezhad, Few-Shot Learning for Plant Disease Classification Using ILP. In Proc. Int Advanced Computing Conf., 321-336, 2022

A. Soliman, J. O'Connell, A. Tamaddoni-Nezhad, Application of Relational Machine Learning to construct Explainable AI models from airline big data, In Proc. of the 25th Air Trans. Res. Soc., ATRS, 103:102242, 2022

D. Barroso-Bergada, A. Tamaddoni-Nezhad, S. Muggleton, C. Vacher, N. Galic, D. Bohan, Machine learning of microbial interactions In Proc. of the Int. Conf. on ILP, pp 26-40, Springer-Verlag, 2022

D. Varghese, R. Bauer, D. Baxter-Beard, S. Muggleton, A. Tamaddoni-Nezhad, Human-like rule learning from images using one-shot hypothesis derivation, In Proc. of the Int. Conf. on ILP, pp 234-250, Springer, 2022

D. Varghese, A. Tamaddoni-Nezhad, One-Shot Rule Learning for Challenging Character Recognition, In Proc. of Int Conf. Declarative AI / RuleML Challenge, CEUR-WS, vol 2644, pages 10-27, 2020.

W-Z Dai, S.H. Muggleton, J. Wen, A. Tamaddoni-Nezhad, and Z-H. Zhou. Logical vision: One-shot meta-interpretive learning from real images. In Proc. of the Int. Conf. on ILP, Springer, pp 46-62, 2018..

A. Cropper, A. Tamaddoni-Nezhad, and S. Muggleton. Meta-interpretive learning of data transformation programs. In Proc. of the 25th Intl. Con. on Inductive Logic Programming, pages 46-59, 2016.

A. Tamaddoni-Nezhad, D. Bohan, A. Raybould and S. Muggleton. Towards machine learning of predictive models from ecological data. In Proc. of the Int. Conf. on ILP, Springer, pages 154-167, 2015.

S.H. Muggleton, D. Lin, J. Chen, and A. Tamaddoni-Nezhad. Metabayes: Bayesian meta-interpretative learning using higher-order stochastic refinement. In Proc. of the Int. Conf. on ILP, pages 1-17, 2014

A. Tamaddoni-Nezhad, D. Bohan, A. Raybould and S. Muggleton. Machine learning a probabilistic network of ecological interactions. In Proc. of the Int. Conf. on ILP, 332-346, 2012. (Best Application Paper Award)

S.H. Muggleton, D. Lin, and A. Tamaddoni-Nezhad. MC-Toplog: Complete multi-clause learning guided by a top theory. In Proc. of the 21st Int. Conf. on ILP, LNAI 7207, pages 238-254, 2012.

A. Tamaddoni-Nezhad and S.H. Muggleton. Stochastic Refinement. In Proceedings of the 20th International Conference on Inductive Logic Programming, pages 222-237, 2011.

S.H. Muggleton, J. Santos, and A. Tamaddoni-Nezhad. ProGolem: a system based on relative minimal generalisation. In Proc. of the 19th Int. Conf. on ILP, pages 131-148. Springer-Verlag, 2010. (Citations: 68)

S.H. Muggleton, J. Santos, and A. Tamaddoni-Nezhad. TopLog: ILP using a logic program declarative bias. In Proceedings of the Int. Conf. on Logic Programming 2008, LNCS 5366, pages 687-692. Springer-Verlag, 2010.

A. Tamaddoni-Nezhad, R. Barton,..M. Sternberg, B. Wren, S. Muggleton, A logic-based approach for modeling genotype-phenotype relations in Campylobacter, In Proc. Int Con. Sys. Biology (ICSB-2008), 2008.

A. Tamaddoni-Nezhad and S.H. Muggleton. A note on refinement operators for IE-based ILP systems. In Proceedings of the 18th Int. Conf. on Inductive Logic Programming, pages 297-314. Springer-Verlag, 2008.

A. Tamaddoni-Nezhad, A. Kakas, S.H. Muggleton, and F. Pazos. Modelling inhibition in metabolic pathways through abduction and induction. In Proc. of the 14th Int. Conf. on ILP, pages 305-322. Springer-Verlag, 2004.

S.H. Muggleton, A. Tamaddoni-Nezhad, and H. Watanabe. Induction of enzyme classes from biological databases. In Proc. of the 13th Int. Conf. on Inductive Logic Programming, pp 269-280. Springer-Verlag, 2003.

A. Tamaddoni-Nezhad, S. Muggleton, and J. Bang. A Bayesian model for metabolic pathways. In Int. Joint Con. on AI (IJCAI03) Workshop on Learning Statistical Models from Relational Data, pp 50-57, 2003.

A. Tamaddoni-Nezhad and S.H. Muggleton. A genetic algorithms approach to ILP. In Proceedings of the 12th International Conference on Inductive Logic Programming, pages 285-300. Springer-Verlag, 2002.

A. Tamaddoni-Nezhad and S.H. Muggleton. Using genetic algorithms for learning clauses in first-order logic. In Proc. of the Genetic and Evolutionary Computation Conference, GECCO-2001, pp 639-646, 2001

A. Tamaddoni-Nezhad and S.H. Muggleton. Searching the subsumption lattice by a genetic algorithm. In Proceedings of the 10th Int. Conf. on Inductive Logic Programming, pages 243-252. Springer-Verlag, 2000.