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 Inverse Entailment 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 Using Abductive ILP and Hypothesis Frequency/Compression Estimation 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.

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.