Stephen H. Muggleton

216 papers A* 16A 2B 58C 1Misc 1Journal 73Unranked 50

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models. Mach. Learn. Lun Ai, Stephen H. Muggleton, Shi-Shun Liang, Geoff S. Baldwin
2025	J	`jnl`	Ultra Strong Machine Learning: Teaching Humans Active Learning Strategies via Automated AI Explanations. CoRR Lun Ai, Johannes Langer, Ute Schmid, Stephen H. Muggleton
2024	—	`conf`	Active Learning of Digenic Functions with Boolean Matrix Logic Programming. IJCLR Lun Ai, Stephen H. Muggleton, Shi-Shun Liang, Geoff S. Baldwin
2024	J	`jnl`	Active learning of digenic functions with boolean matrix logic programming. CoRR Lun Ai, Stephen H. Muggleton, Shi-Shun Liang, Geoff S. Baldwin
2024	J	`jnl`	Boolean Matrix Logic Programming. CoRR Lun Ai, Stephen H. Muggleton
2024	J	`jnl`	Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models. CoRR Lun Ai, Stephen H. Muggleton, Shi-Shun Liang, Geoff S. Baldwin
2024	B	`ed.`	Inductive Logic Programming - 31st International Conference, ILP 2022, Windsor Great Park, UK, September 28-30, 2022, Proceedings ILP Stephen H. Muggleton, Alireza Tamaddoni-Nezhad
2024	J	`jnl`	Simulating Petri nets with Boolean Matrix Logic Programming. CoRR Lun Ai, Stephen H. Muggleton, Shi-Shun Liang, Geoff S. Baldwin
2023	J	`jnl`	Explanatory machine learning for sequential human teaching. Mach. Learn. Lun Ai, Johannes Langer, Stephen H. Muggleton, Ute Schmid
2023	J	`jnl`	Human Comprehensible Active Learning of Genome-Scale Metabolic Networks. CoRR Lun Ai, Shi-Shun Liang, Wang-Zhou Dai, Liam Hallett, Stephen H. Muggleton, Geoff S. Baldwin
2022	J	`jnl`	Correction to: Meta-interpretive learning as metarule specialisation. Mach. Learn. Stassa Patsantzis, Stephen H. Muggleton
2022	J	`jnl`	Explanatory machine learning for sequential human teaching. CoRR Lun Ai, Johannes Langer, Stephen H. Muggleton, Ute Schmid
2022	—	`book`	Human-Like Machine Intelligence. Stephen H. Muggleton, Nicholas Chater
2022	—	`ch.`	Human-Machine Scientific Discovery. Human-Like Machine Intelligence Alireza Tamaddoni-Nezhad, David A. Bohan, Ghazal Afroozi Milani, Alan Raybould, Stephen H. Muggleton
2022	—	`ch.`	Human-like Computer Vision. Human-Like Machine Intelligence Stephen H. Muggleton, Wang-Zhou Dai
2022	J	`jnl`	Inductive logic programming at 30. Mach. Learn. Andrew Cropper, Sebastijan Dumancic, Richard Evans, Stephen H. Muggleton
2022	J	`jnl`	Meta-interpretive learning as metarule specialisation. Mach. Learn. Stassa Patsantzis, Stephen H. Muggleton
2021	A*	`conf`	Abductive Knowledge Induction from Raw Data. IJCAI Wang-Zhou Dai, Stephen H. Muggleton
2021	A*	`conf`	Abductive Learning with Ground Knowledge Base. IJCAI Le-Wen Cai, Wang-Zhou Dai, Yu-Xuan Huang, Yufeng Li, Stephen H. Muggleton, Yuan Jiang
2021	J	`jnl`	Automated Biodesign Engineering by Abductive Meta-Interpretive Learning. CoRR Wang-Zhou Dai, Liam Hallett, Stephen H. Muggleton, Geoff S. Baldwin
2021	J	`jnl`	Beneficial and harmful explanatory machine learning. Mach. Learn. Lun Ai, Stephen H. Muggleton, Céline Hocquette, Mark Gromowski, Ute Schmid
2021	A*	`conf`	Fast Abductive Learning by Similarity-based Consistency Optimization. NeurIPS Yu-Xuan Huang, Wang-Zhou Dai, Le-Wen Cai, Stephen H. Muggleton, Yuan Jiang
2021	B	`conf`	Human-Like Rule Learning from Images Using One-Shot Hypothesis Derivation. ILP Dany Varghese, Roman Bauer, Daniel Baxter-Beard, Stephen H. Muggleton, Alireza Tamaddoni-Nezhad
2021	J	`jnl`	Inductive logic programming at 30. CoRR Andrew Cropper, Sebastijan Dumancic, Richard Evans, Stephen H. Muggleton
2021	B	`conf`	Machine Learning of Microbial Interactions Using Abductive ILP and Hypothesis Frequency/Compression Estimation. ILP Didac Barroso-Bergada, Alireza Tamaddoni-Nezhad, Stephen H. Muggleton, Corinne Vacher, Nika Galic, David A. Bohan
2021	J	`jnl`	Meta-Interpretive Learning as Metarule Specialisation. CoRR Stassa Patsantzis, Stephen H. Muggleton
2021	J	`jnl`	Top Program Construction and Reduction for polynomial time Meta-Interpretive Learning. CoRR Stassa Patsantzis, Stephen H. Muggleton
2021	J	`jnl`	Top program construction and reduction for polynomial time Meta-Interpretive learning. Mach. Learn. Stassa Patsantzis, Stephen H. Muggleton
2020	J	`jnl`	Abductive Knowledge Induction From Raw Data. CoRR Wang-Zhou Dai, Stephen H. Muggleton
2020	J	`jnl`	Beneficial and Harmful Explanatory Machine Learning. CoRR Lun Ai, Stephen H. Muggleton, Céline Hocquette, Mark Gromowski, Ute Schmid
2020	A*	`conf`	Complete Bottom-Up Predicate Invention in Meta-Interpretive Learning. IJCAI Céline Hocquette, Stephen H. Muggleton
2020	A*	`conf`	Learning Higher-Order Programs through Predicate Invention. AAAI Andrew Cropper, Rolf Morel, Stephen H. Muggleton
2020	J	`jnl`	Learning higher-order logic programs. Mach. Learn. Andrew Cropper, Rolf Morel, Stephen H. Muggleton
2020	A*	`conf`	Turning 30: New Ideas in Inductive Logic Programming. IJCAI Andrew Cropper, Sebastijan Dumancic, Stephen H. Muggleton
2020	J	`jnl`	Turning 30: New Ideas in Inductive Logic Programming. CoRR Andrew Cropper, Sebastijan Dumancic, Stephen H. Muggleton
2019	J	`jnl`	Approaches and Applications of Inductive Programming (Dagstuhl Seminar 19202). Dagstuhl Reports Luc De Raedt, Richard Evans, Stephen H. Muggleton, Ute Schmid
2019	J	`jnl`	Can Meta-Interpretive Learning outperform Deep Reinforcement Learning of Evaluable Game strategies? CoRR Céline Hocquette, Stephen H. Muggleton
2019	J	`jnl`	Learning efficient logic programs. Mach. Learn. Andrew Cropper, Stephen H. Muggleton
2019	J	`jnl`	Learning higher-order logic programs. CoRR Andrew Cropper, Rolf Morel, Stephen H. Muggleton
2019	J	`jnl`	Machine Discovery of Comprehensible Strategies for Simple Games Using Meta-interpretive Learning. New Gener. Comput. Stephen H. Muggleton, Céline Hocquette
2019	J	`jnl`	Special Issue on Semantic Technology. New Gener. Comput. Ryutaro Ichise, Stephen H. Muggleton, Kouji Kozaki, Freddy Lécué, Dongyan Zhao, Takahiro Kawamura
2018	B	`conf`	How Much Can Experimental Cost Be Reduced in Active Learning of Agent Strategies? ILP Céline Hocquette, Stephen H. Muggleton
2018	J	`jnl`	Meta-Interpretive Learning from noisy images. Mach. Learn. Stephen H. Muggleton, Wang-Zhou Dai, Claude Sammut, Alireza Tamaddoni-Nezhad, Jing Wen, Zhi-Hua Zhou
2018	—	`ed.`	Semantic Technology - 8th Joint International Conference, JIST 2018, Awaji, Japan, November 26-28, 2018, Proceedings JIST Ryutaro Ichise, Freddy Lécué, Takahiro Kawamura, Dongyan Zhao, Stephen H. Muggleton, Kouji Kozaki
2018	J	`jnl`	Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP. Mach. Learn. Stephen H. Muggleton, Ute Schmid, Christina Zeller, Alireza Tamaddoni-Nezhad, Tarek R. Besold
2017	J	`jnl`	Approaches and Applications of Inductive Programming (Dagstuhl Seminar 17382). Dagstuhl Reports Ute Schmid, Stephen H. Muggleton, Rishabh Singh
2017	B	`conf`	Logical Vision: One-Shot Meta-Interpretive Learning from Real Images. ILP Wang-Zhou Dai, Stephen H. Muggleton, Jing Wen, Alireza Tamaddoni-Nezhad, Zhi-Hua Zhou
2017	B	`conf`	Meta-Interpretive Learning: Achievements and Challenges (Invited Paper). RuleML+RR Stephen H. Muggleton
2017	—	`conf`	The Effect of Predicate Order on Curriculum Learning in ILP. ILP (Late Breaking Papers) Hank Conn, Stephen H. Muggleton
2016	B	`conf`	How Does Predicate Invention Affect Human Comprehensibility? ILP Ute Schmid, Christina Zeller, Tarek R. Besold, Alireza Tamaddoni-Nezhad, Stephen H. Muggleton
2016	A*	`conf`	Learning Higher-Order Logic Programs through Abstraction and Invention. IJCAI Andrew Cropper, Stephen H. Muggleton
2015	J	`jnl`	Approaches and Applications of Inductive Programming (Dagstuhl Seminar 15442). Dagstuhl Reports José Hernández-Orallo, Stephen H. Muggleton, Ute Schmid, Benjamin G. Zorn
2015	—	`conf`	Can predicate invention compensate for incomplete background knowledge? SCAI Andrew Cropper, Stephen H. Muggleton
2015	J	`jnl`	Inductive programming meets the real world. Commun. ACM Sumit Gulwani, José Hernández-Orallo, Emanuel Kitzelmann, Stephen H. Muggleton, Ute Schmid, Benjamin G. Zorn
2015	A*	`conf`	Learning Efficient Logical Robot Strategies Involving Composable Objects. IJCAI Andrew Cropper, Stephen H. Muggleton
2015	—	`conf`	Logical Vision: Meta-Interpretive Learning for Simple Geometrical Concepts. ILP (Late Breaking Papers) Wang-Zhou Dai, Stephen H. Muggleton, Zhi-Hua Zhou
2015	B	`conf`	Meta-Interpretive Learning of Data Transformation Programs. ILP Andrew Cropper, Alireza Tamaddoni-Nezhad, Stephen H. Muggleton
2015	J	`jnl`	Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited. Mach. Learn. Stephen H. Muggleton, Dianhuan Lin, Alireza Tamaddoni-Nezhad
2015	—	`conf`	Typed meta-interpretive learning for proof strategies. ILP (Late Breaking Papers) Colin Farquhar, Gudmund Grov, Andrew Cropper, Stephen H. Muggleton, Alan Bundy
2014	J	`jnl`	Alan Turing and the development of Artificial Intelligence. AI Commun. Stephen H. Muggleton
2014	A	`conf`	Bias reformulation for one-shot function induction. ECAI Dianhuan Lin, Eyal Dechter, Kevin Ellis, Joshua B. Tenenbaum, Stephen H. Muggleton
2014	—	`ed.`	Latest Advances in Inductive Logic Programming, ILP 2011, Late Breaking Papers, Windsor Great Park, UK, July 31 - August 3, 2011. ILP (Late Breaking Papers) Stephen H. Muggleton, Hiroaki Watanabe
2014	B	`conf`	Logical Minimisation of Meta-Rules Within Meta-Interpretive Learning. ILP Andrew Cropper, Stephen H. Muggleton
2014	J	`jnl`	Meta-interpretive learning: application to grammatical inference. Mach. Learn. Stephen H. Muggleton, Dianhuan Lin, Niels Pahlavi, Alireza Tamaddoni-Nezhad
2014	B	`conf`	Towards Machine Learning of Predictive Models from Ecological Data. ILP Alireza Tamaddoni-Nezhad, David A. Bohan, Alan Raybould, Stephen H. Muggleton
2013	J	`jnl`	Editors's Introduction to the Special Issue on "100 Years of Alan Turing and 20 Years of SLAIS". Informatica (Slovenia) Dunja Mladenic, Stephen H. Muggleton, Ivan Bratko
2013	A*	`conf`	Meta-Interpretive Learning of Higher-Order Dyadic Datalog: Predicate Invention revisited. IJCAI Stephen H. Muggleton, Dianhuan Lin
2013	B	`conf`	MetaBayes: Bayesian Meta-Interpretative Learning Using Higher-Order Stochastic Refinement. ILP Stephen H. Muggleton, Dianhuan Lin, Jianzhong Chen, Alireza Tamaddoni-Nezhad
2012	J	`jnl`	Automated identification of protein-ligand interaction features using Inductive Logic Programming: a hexose binding case study. BMC Bioinform. José Carlos Almeida Santos, Houssam Nassif, David Page, Stephen H. Muggleton, Michael J. E. Sternberg
2012	J	`jnl`	Guest editorial: special issue on Inductive Logic Programming (ILP 2011). Mach. Learn. Stephen H. Muggleton, Jianzhong Chen
2012	J	`jnl`	ILP turns 20 - Biography and future challenges. Mach. Learn. Stephen H. Muggleton, Luc De Raedt, David Poole, Ivan Bratko, Peter A. Flach, Katsumi Inoue, Ashwin Srinivasan
2012	B	`ed.`	Inductive Logic Programming - 21st International Conference, ILP 2011, Windsor Great Park, UK, July 31 - August 3, 2011, Revised Selected Papers ILP Stephen H. Muggleton, Alireza Tamaddoni-Nezhad, Francesca A. Lisi
2012	—	`conf`	Machine Learning and Text Mining of Trophic Links. ICMLA (2) Ghazal Afroozi Milani, David A. Bohan, Stuart J. Dunbar, Stephen H. Muggleton, Alan Raybould, Alireza Tamaddoni-Nezhad
2012	—	`conf`	The Scientific Contribution of Marek Sergot. Logic Programs, Norms and Action Steve Barker, Andrew J. I. Jones, Antonis C. Kakas, Robert A. Kowalski, Alessio Lomuscio, Rob Miller, Stephen H. Muggleton, Giovanni Sartor
2011	—	`conf`	Automatic Invention of Functional Abstractions. ILP (Late Breaking Papers) Robert J. Henderson, Stephen H. Muggleton
2011	—	`conf`	Can ILP Learn Complete and Correct Game Strategies? ILP (Late Breaking Papers) Stephen H. Muggleton, Changze Xu
2011	—	`conf`	Customisable Multi-Processor Acceleration of Inductive Logic Programming. ILP (Late Breaking Papers) Andreas Kirkeby Fidjeland, Wayne Luk, Stephen H. Muggleton
2011	B	`conf`	Does Multi-Clause Learning Help in Real-World Applications? ILP Dianhuan Lin, Jianzhong Chen, Hiroaki Watanabe, Stephen H. Muggleton, Pooja Jain, Michael J. E. Sternberg, Charles Baxter, Richard A. Currie, Stuart J. Dunbar, Mark Earll, José Domingo Salazar
2011	B	`conf`	MC-TopLog: Complete Multi-clause Learning Guided by a Top Theory. ILP Stephen H. Muggleton, Dianhuan Lin, Alireza Tamaddoni-Nezhad
2011	B	`conf`	Machine Learning a Probabilistic Network of Ecological Interactions. ILP Alireza Tamaddoni-Nezhad, David A. Bohan, Alan Raybould, Stephen H. Muggleton
2011	B	`conf`	Projection-Based PILP: Computational Learning Theory with Empirical Results. ILP Hiroaki Watanabe, Stephen H. Muggleton
2011	—	`conf`	Towards Efficient Higher-Order Logic Learning in a First-Order Datalog Framework. ILP (Late Breaking Papers) Niels Pahlavi, Stephen H. Muggleton
2011	J	`jnl`	WIBL: Workbench for Integrative Biological Learning. J. Integr. Bioinform. Victor Lesk, Jan Taubert, Christopher J. Rawlings, Stuart J. Dunbar, Stephen H. Muggleton
2010	B	`conf`	Can HOLL Outperform FOLL? ILP Niels Pahlavi, Stephen H. Muggleton
2010	—	`conf`	Knowledge Mining Biological Network Models. IIP Stephen H. Muggleton
2010	B	`conf`	Stochastic Refinement. ILP Alireza Tamaddoni-Nezhad, Stephen H. Muggleton
2010	—	`conf`	Subsumer: A Prolog theta-subsumption engine. ICLP (Technical Communications) Jose Santos, Stephen H. Muggleton
2010	B	`conf`	Variation of Background Knowledge in an Industrial Application of ILP. ILP Stephen H. Muggleton, Jianzhong Chen, Hiroaki Watanabe, Stuart J. Dunbar, Charles Baxter, Richard A. Currie, José Domingo Salazar, Jan Taubert, Michael J. E. Sternberg
2010	B	`conf`	When Does It Pay Off to Use Sophisticated Entailment Engines in ILP? ILP Jose Santos, Stephen H. Muggleton
2009	—	`conf`	Abstracting Probabilistic Relational Model by Specialisation. MLG/SRL@ILP Hiroaki Watanabe, Stephen H. Muggleton
2009	Misc	`conf`	An ILP System for Learning Head Output Connected Predicates. EPIA José Carlos Almeida Santos, Alireza Tamaddoni-Nezhad, Stephen H. Muggleton
2009	B	`conf`	Can ILP Be Applied to Large Datasets? ILP Hiroaki Watanabe, Stephen H. Muggleton
2009	B	`conf`	Chess Revision: Acquiring the Rules of Chess Variants through FOL Theory Revision from Examples. ILP Stephen H. Muggleton, Aline Paes, Vítor Santos Costa, Gerson Zaverucha
2009	B	`conf`	Learning Large Margin First Order Decision Lists for Multi-Class Classification. Discovery Science Huma Lodhi, Stephen H. Muggleton, Michael J. E. Sternberg
2009	J	`jnl`	Multi-Class protein fold recognition using large margin logic based divide and conquer learning. SIGKDD Explor. Huma Lodhi, Stephen H. Muggleton, Michael J. E. Sternberg
2009	—	`conf`	Multi-class protein fold recognition using large margin logic based divide and conquer learning. KDD Workshop on Statistical and Relational Learning in Bioinformatics Huma Lodhi, Stephen H. Muggleton, Michael J. E. Sternberg
2009	B	`conf`	ProGolem: A System Based on Relative Minimal Generalisation. ILP Stephen H. Muggleton, José Carlos Almeida Santos, Alireza Tamaddoni-Nezhad
2009	J	`jnl`	The lattice structure and refinement operators for the hypothesis space bounded by a bottom clause. Mach. Learn. Alireza Tamaddoni-Nezhad, Stephen H. Muggleton
2008	—	`ch.`	A Behavioral Comparison of Some Probabilistic Logic Models. Probabilistic Inductive Logic Programming Stephen H. Muggleton, Jianzhong Chen
2008	—	`conf`	A Customisable Multiprocessor for Application-Optimised Inductive Logic Programming. BCS Int. Acad. Conf. Andreas Fidjeland, Wayne Luk, Stephen H. Muggleton
2008	B	`conf`	A Note on Refinement Operators for IE-Based ILP Systems. ILP Alireza Tamaddoni-Nezhad, Stephen H. Muggleton
2008	—	`conf`	Developing Robust Synthetic Biology Designs Using a Microfluidic Robot Scientist. SBIA Stephen H. Muggleton
2008	—	`conf`	From ILP to PILP. SBIA Stephen H. Muggleton
2008	J	`jnl`	Guest editorial: special issue on Inductive Logic Programming. Mach. Learn. Stephen H. Muggleton, Ramón P. Otero, Simon Colton
2008	J	`jnl`	Learning probabilistic logic models from probabilistic examples. Mach. Learn. Jianzhong Chen, Stephen H. Muggleton, José Carlos Almeida Santos
2008	—	`book`	Probabilistic Inductive Logic Programming - Theory and Applications Probabilistic Inductive Logic Programming Luc De Raedt, Paolo Frasconi, Kristian Kersting, Stephen H. Muggleton
2008	—	`ed.`	Probabilistic, Logical and Relational Learning - A Further Synthesis, 15.04. - 20.04.2007 Probabilistic, Logical and Relational Learning - A Further Synthesis Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Kristian Kersting, Stephen H. Muggleton
2008	—	`ch.`	Protein Fold Discovery Using Stochastic Logic Programs. Probabilistic Inductive Logic Programming Jianzhong Chen, Lawrence A. Kelley, Stephen H. Muggleton, Michael J. E. Sternberg
2008	J	`jnl`	QG/GA: a stochastic search for Progol. Mach. Learn. Stephen H. Muggleton, Alireza Tamaddoni-Nezhad
2008	J	`jnl`	Scaffold Hopping in Drug Discovery Using Inductive Logic Programming. J. Chem. Inf. Model. Kazuhisa Tsunoyama, Ata Amini, Michael J. E. Sternberg, Stephen H. Muggleton
2008	J	`jnl`	Structured machine learning: the next ten years. Mach. Learn. Thomas G. Dietterich, Pedro M. Domingos, Lise Getoor, Stephen H. Muggleton, Prasad Tadepalli
2008	B	`conf`	TopLog: ILP Using a Logic Program Declarative Bias. ICLP Stephen H. Muggleton, José Carlos Almeida Santos, Alireza Tamaddoni-Nezhad
2007	—	`conf`	07161 Abstracts Collection -- Probabilistic, Logical and Relational Learning - A Further Synthesis. Probabilistic, Logical and Relational Learning - A Further Synthesis Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Kristian Kersting, Stephen H. Muggleton
2007	J	`jnl`	A Novel Logic-Based Approach for Quantitative Toxicology Prediction. J. Chem. Inf. Model. Ata Amini, Stephen H. Muggleton, Huma Lodhi, Michael J. E. Sternberg
2007	—	`conf`	Abductive Stochastic Logic Programs for Metabolic Network Inhibition Learning. MLG Jianzhong Chen, Stephen H. Muggleton, Jose Santos
2007	B	`ed.`	Inductive Logic Programming, 16th International Conference, ILP 2006, Santiago de Compostela, Spain, August 24-27, 2006, Revised Selected Papers ILP Stephen H. Muggleton, Ramón P. Otero, Alireza Tamaddoni-Nezhad
2007	B	`conf`	Learning Probabilistic Logic Models from Probabilistic Examples (Extended Abstract). ILP Jianzhong Chen, Stephen H. Muggleton, Jose Santos
2007	J	`jnl`	Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds. J. Comput. Aided Mol. Des. Edward O. Cannon, Ata Amini, Andreas Bender, Michael J. E. Sternberg, Stephen H. Muggleton, Robert C. Glen, John B. O. Mitchell
2006	J	`jnl`	Application of abductive ILP to learning metabolic network inhibition from temporal data. Mach. Learn. Alireza Tamaddoni-Nezhad, Raphael Chaleil, Antonis C. Kakas, Stephen H. Muggleton
2006	J	`jnl`	Mathematical applications of inductive logic programming. Mach. Learn. Simon Colton, Stephen H. Muggleton
2006	B	`conf`	Multi-class Prediction Using Stochastic Logic Programs. ILP Jianzhong Chen, Lawrence A. Kelley, Stephen H. Muggleton, Michael J. E. Sternberg
2006	—	`ed.`	Probabilistic, Logical and Relational Learning - Towards a Synthesis, 30. January - 4. February 2005 Probabilistic, Logical and Relational Learning Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Stephen H. Muggleton
2006	B	`conf`	QG/GA: A Stochastic Search for Progol. ILP Stephen H. Muggleton, Alireza Tamaddoni-Nezhad
2006	B	`conf`	The Complexity of Translating BLPs to RMMs. ILP Stephen H. Muggleton, Niels Pahlavi
2006	A*	`conf`	Towards Chemical Universal Turing Machines. AAAI Stephen H. Muggleton
2005	—	`conf`	05051 Abstracts Collection - Probabilistic, Logical and Relational Learning - Towards a Synthesis. Probabilistic, Logical and Relational Learning Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Stephen H. Muggleton
2005	—	`conf`	05051 Executive Summary - Probabilistic, Logical and Relational Learning - Towards a Synthesis. Probabilistic, Logical and Relational Learning Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Stephen H. Muggleton
2005	C	`conf`	Abduction and induction for learning models of inhibition in metabolic networks. ICMLA Alireza Tamaddoni-Nezhad, Raphael Chaleil, Antonis C. Kakas, Stephen H. Muggleton
2005	—	`conf`	Computing Confidence Measures in Stochastic Logic Programs. MICAI Huma Lodhi, Stephen H. Muggleton
2005	—	`conf`	Learning Stochastic Logical Automaton. JSAI Workshops Hiroaki Watanabe, Stephen H. Muggleton
2005	—	`conf`	Learning the Time Complexity of Logic Programs. AAIP Stephen H. Muggleton
2005	B	`conf`	Machine Learning for Systems Biology. ILP Stephen H. Muggleton
2005	B	`conf`	Support Vector Inductive Logic Programming. Discovery Science Stephen H. Muggleton, Huma Lodhi, Ata Amini, Michael J. E. Sternberg
2004	B	`conf`	Modelling Inhibition in Metabolic Pathways Through Abduction and Induction. ILP Alireza Tamaddoni-Nezhad, Antonis C. Kakas, Stephen H. Muggleton, Florencio Pazos
2004	—	`conf`	Modelling Metabolic Pathways Using Stochastic Logic Programs-Based Ensemble Methods. CMSB Huma Lodhi, Stephen H. Muggleton
2003	B	`conf`	ILP for Mathematical Discovery. ILP Simon Colton, Stephen H. Muggleton
2003	B	`conf`	Induction of Enzyme Classes from Biological Databases. ILP Stephen H. Muggleton, Alireza Tamaddoni-Nezhad, Hiroaki Watanabe
2003	—	`conf`	Interpretation of Hidden Node Methodology in Automated Classification of Neural Cell Morphology. METMBS Jung-Wook Bang, Alexandros Pappas, Duncan Fyfe Gillies, Stephen H. Muggleton
2002	B	`conf`	A Genetic Algorithms Approach to ILP. ILP Alireza Tamaddoni-Nezhad, Stephen H. Muggleton
2002	J	`jnl`	Generating Protein Three-dimensional Fold Signatures using Inductive Logic Programming. Comput. Chem. Marcel Turcotte, Stephen H. Muggleton, Michael J. E. Sternberg
2002	B	`conf`	Learning Structure and Parameters of Stochastic Logic Programs. ILP Stephen H. Muggleton
2002	—	`conf`	Scalable acceleration of inductive logic programs. FPT Andreas Fidjeland, Wayne Luk, Stephen H. Muggleton
2001	J	`jnl`	Are Grammatical Representations Useful for Learning from Biological Sequence Data? - A Case Study. J. Comput. Biol. Stephen H. Muggleton, Christopher H. Bryant, Ashwin Srinivasan, Alex Whittaker, Simon Topp, Christopher J. Rawlings
2001	J	`jnl`	Automatic determination of protein fold signatures from structural superpositions. Electron. Trans. Artif. Intell. A. P. Cootes, Stephen H. Muggleton, R. B. Greaves, Michael J. E. Sternberg
2001	J	`jnl`	Combining Inductive Logic Programming, Active Learning and Robotics to Discover the Function of Genes. Electron. Trans. Artif. Intell. Christopher H. Bryant, Stephen H. Muggleton, Stephen G. Oliver, Douglas B. Kell, Philip G. K. Reiser, Ross D. King
2001	J	`jnl`	Developing a Logical Model of Yeast Metabolism. Electron. Trans. Artif. Intell. Philip G. K. Reiser, Ross D. King, Douglas B. Kell, Stephen H. Muggleton, Christopher H. Bryant, Stephen G. Oliver
2001	J	`jnl`	The Effect of Relational Background Knowledge on Learning of Protein Three-Dimensional Fold Signatures. Mach. Learn. Marcel Turcotte, Stephen H. Muggleton, Michael J. E. Sternberg
2000	A*	`conf`	Learning Chomsky-like Grammars for Biological Sequence Families. ICML Stephen H. Muggleton, Christopher H. Bryant, Ashwin Srinivasan
2000	J	`jnl`	Learning Stochastic Logic Programs. Electron. Trans. Artif. Intell. Stephen H. Muggleton
2000	—	`conf`	Measuring Performance when Positives Are Rare: Relative Advantage versus Predictive Accuracy - A Biological Case Study. ECML Stephen H. Muggleton, Christopher H. Bryant, Ashwin Srinivasan
2000	B	`conf`	Searching the Subsumption Lattice by a Genetic Algorithm. ILP Alireza Tamaddoni-Nezhad, Stephen H. Muggleton
2000	B	`conf`	Theory Completion Using Inverse Entailment. ILP Stephen H. Muggleton, Christopher H. Bryant
2000	J	`jnl`	Use of Inductive Logic Programming to Learn Principles of Protein Structure. Electron. Trans. Artif. Intell. Marcel Turcotte, Stephen H. Muggleton, Michael J. E. Sternberg
1999	B	`conf`	Analogical Prediction. ILP Stephen H. Muggleton, Michael Bain
1999	J	`jnl`	Guest Editors' Introduction: Inductive Logic Programming. J. Log. Program. Stephen H. Muggleton, David Page
1999	J	`jnl`	Inductive Logic Programming: Issues, Results and the Challenge of Learning Language in Logic. Artif. Intell. Stephen H. Muggleton
1999	—	`ed.`	Machine Intelligence 15, Intelligent Agents [St. Catherine's College, Oxford, UK, July 1995] Machine Intelligence 15 Koichi Furukawa, Donald Michie, Stephen H. Muggleton
1999	J	`jnl`	Scientific Knowledge Discovery Using Inductive Logic Programming. Commun. ACM Stephen H. Muggleton
1999	B	`conf`	Theory Recovery. ILP Rupert Parson, Khalid Khan, Stephen H. Muggleton
1998	B	`conf`	A Comparison of ILP and Propositional Systems on Propositional Traffic Data. ILP Sam Roberts, Wim Van Laer, Nico Jacobs, Stephen H. Muggleton, Jeremy Broughton
1998	B	`conf`	Advances in ILP Theory and Implementations (Abstract). ILP Stephen H. Muggleton
1998	B	`conf`	Application of Inductive Logic Programming to Discover Rules Governing the Three-Dimensional Topology of Protein Structure. ILP Marcel Turcotte, Stephen H. Muggleton, Michael J. E. Sternberg
1998	B	`conf`	Biochemical Knowledge Discovery Using Inductive Logic Programming. Discovery Science Stephen H. Muggleton, Ashwin Srinivasan, Ross D. King, Michael J. E. Sternberg
1998	B	`conf`	Completing Inverse Entailment. ILP Stephen H. Muggleton
1998	B	`conf`	Detecting Traffic Problems with ILP. ILP Saso Dzeroski, Nico Jacobs, Martín Molina, Carlos Moure, Stephen H. Muggleton, Wim Van Laer
1998	A	`conf`	Inductive Logic Programming: Issues, Results and the LLL Challenge (abstract). ECAI Stephen H. Muggleton
1998	B	`conf`	Knowledge Discovery in Biological and Chemical Domains. Discovery Science Stephen H. Muggleton
1998	J	`jnl`	Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL. Mach. Learn. Paul W. Finn, Stephen H. Muggleton, David Page, Ashwin Srinivasan
1998	B	`conf`	Repeat Learning Using Predicate Invention. ILP Khalid Khan, Stephen H. Muggleton, Rupert Parson
1997	B	`conf`	Carcinogenesis Predictions Using ILP. ILP Ashwin Srinivasan, Ross D. King, Stephen H. Muggleton, Michael J. E. Sternberg
1997	J	`jnl`	Guest Editors' Introduction. Mach. Learn. Stephen H. Muggleton, David Page
1997	B	`ed.`	Inductive Logic Programming, 6th International Workshop, ILP-96, Stockholm, Sweden, August 26-28, 1996, Selected Papers ILP Stephen H. Muggleton
1997	B	`conf`	Learning Programs in the Event Calculus. ILP Stephen Moyle, Stephen H. Muggleton
1997	—	`conf`	Machine Intelligibility and the Duality Principle. Software Agents and Soft Computing Stephen H. Muggleton, Donald Michie
1997	—	`conf`	The Predictive Toxicology Evaluation Challenge. IJCAI (1) Ashwin Srinivasan, Ross D. King, Stephen H. Muggleton, Michael J. E. Sternberg
1996	B	`conf`	An Initial Experiment into Stereochemistry-Based Drug Design Using Inductive Logic Programming. Inductive Logic Programming Workshop Stephen H. Muggleton, David Page, Ashwin Srinivasan
1996	B	`conf`	Learning from Positive Data. Inductive Logic Programming Workshop Stephen H. Muggleton
1996	J	`jnl`	Theories for Mutagenicity: A Study in First-Order and Feature-Based Induction. Artif. Intell. Ashwin Srinivasan, Stephen H. Muggleton, Michael J. E. Sternberg, Ross D. King
1995	—	`conf`	A Learnability Model for Universal Representations and Its Application to Top-down Induction of Decision Trees. Machine Intelligence 15 Stephen H. Muggleton, David Page
1995	—	`conf`	An Experiment with Browsers that Learn. Machine Intelligence 15 Rupert Parson, Stephen H. Muggleton
1995	J	`jnl`	Applications of Inductive Logic Programming. Commun. ACM Ivan Bratko, Stephen H. Muggleton
1995	—	`conf`	Drug Design by Machine Learning. Machine Intelligence 15 Michael J. E. Sternberg, Ross D. King, Ashwin Srinivasan, Stephen H. Muggleton
1995	A*	`conf`	Inductive Logic Programming: Inverse Resolution and Beyond. IJCAI (1) Stephen H. Muggleton
1995	J	`jnl`	Inverse Entailment and Progol. New Gener. Comput. Stephen H. Muggleton
1995	—	`ed.`	Machine Intelligence 14, Proceedings of the Fourteenth Machine Intelligence Workshop, held at Hitachi Advanced Research Laboratories, Tokyo, Japan, November 1993 Machine Intelligence 14 Koichi Furukawa, Donald Michie, Stephen H. Muggleton
1995	J	`jnl`	Special Issue on Inductive Logic Programming. New Gener. Comput. Stephen H. Muggleton, Fumio Mizoguchi, Koichi Furukawa
1994	A*	`conf`	Bayesian Inductive Logic Programming. COLT Stephen H. Muggleton
1994	A*	`conf`	Bayesian Inductive Logic Programming. ICML Stephen H. Muggleton
1994	J	`jnl`	Inductive Logic Programming: Derivations, Successes and Shortcomings. SIGART Bull. Stephen H. Muggleton
1994	J	`jnl`	Inductive Logic Programming: Theory and Methods. J. Log. Program. Stephen H. Muggleton, Luc De Raedt
1994	—	`ed.`	Machine Intelligence 13, Machine Intelligence and Inductive Learning, Loch Lomond, UK, 1992 Machine Intelligence 13 Koichi Furukawa, Donald Michie, Stephen H. Muggleton
1994	J	`jnl`	Predicate invention and utilization. J. Exp. Theor. Artif. Intell. Stephen H. Muggleton
1993	—	`conf`	Inductive Logic Programming: Derivations, Successes and Shortcomings. ECML Stephen H. Muggleton
1993	—	`conf`	Inverting Entailment and Progol. Machine Intelligence 14 Stephen H. Muggleton
1993	—	`conf`	Learnability of Constrained Logic Programs. ECML Saso Dzeroski, Stephen H. Muggleton, Stuart Russell
1993	B	`conf`	Optimal Layered Learning: A PAC Approach to Incremental Sampling. ALT Stephen H. Muggleton
1992	—	`conf`	Compression, Significance, and Accuracy. ML Stephen H. Muggleton, Ashwin Srinivasan, Michael Bain
1992	—	`conf`	Developments in Inductive Logic Programming, Panel Position Paper. FGCS Stephen H. Muggleton
1992	—	`conf`	Learning optimal chess strategies. Machine Intelligence 13 Michael Bain, Stephen H. Muggleton
1992	—	`conf`	Logic and Learning: Turing's legacy. Machine Intelligence 13 Stephen H. Muggleton
1992	—	`conf`	Machine Learning and biomolecular modelling. Machine Intelligence 13 Michael J. E. Sternberg, R. A. Lewis, Ross D. King, Stephen H. Muggleton
1992	A*	`conf`	PAC-Learnability of Determinate Logic Programs. COLT Saso Dzeroski, Stephen H. Muggleton, Stuart Russell
1992	—	`conf`	The Justification of Logical Theories based on Data Compression. Machine Intelligence 13 Ashwin Srinivasan, Stephen H. Muggleton, Michael Bain
1992	—	`conf`	Towards Inductive Generalization in Higher Order Logic. ML Cao Feng, Stephen H. Muggleton
1991	J	`jnl`	Inductive Logic Programming. New Gener. Comput. Stephen H. Muggleton
1991	—	`conf`	Learning Qualitative Models of Dynamic Systems. ML Ivan Bratko, Stephen H. Muggleton, Alen Varsek
1991	—	`conf`	Learning to Relate Terms in a Multiple Agent Environment. EWSL Pavel Brazdil, Stephen H. Muggleton
1990	B	`conf`	Efficient Induction of Logic Programs. ALT Stephen H. Muggleton, Cao Feng
1990	B	`conf`	Inductive Logic Programming. ALT Stephen H. Muggleton
1990	—	`book`	Inductive acquisition of expert knowledge. Stephen H. Muggleton
1989	—	`conf`	An Experimental Comparison of Human and Machine Learning Formalisms. ML Stephen H. Muggleton, Michael Bain, Jean Hayes Michie, Donald Michie
1988	—	`conf`	A Strategy for Constructing New Predicates in First-Order Logic. EWSL Stephen H. Muggleton
1988	—	`conf`	Machine Invention of First Order Predicates by Inverting Resolution. ML Stephen H. Muggleton, Wray L. Buntine
1987	A*	`conf`	Duce, An Oracle-based Approach to Constructive Induction. IJCAI Stephen H. Muggleton
1987	—	`conf`	Structuring Knowledge by Asking Questions. EWSL Stephen H. Muggleton
1986	—	`—`	Inductive acquisition of expert knowledge. Stephen H. Muggleton