Inductive acquisition of expert knowledge. - Stephen H. Muggleton (1986)

Structuring Knowledge by Asking Questions. - Stephen H. Muggleton (EWSL, 1987)

Duce, An Oracle-based Approach to Constructive Induction. - Stephen H. Muggleton (IJCAI, 1987)

A Strategy for Constructing New Predicates in First-Order Logic. - Stephen H. Muggleton (EWSL, 1988)

Machine Invention of First Order Predicates by Inverting Resolution. - Stephen H. Muggleton; Wray L. Buntine (ML, 1988)

An Experimental Comparison of Human and Machine Learning Formalisms. - Stephen H. Muggleton; Michael Bain 001; Jean Hayes Michie; Donald Michie (ML, 1989)

Inductive acquisition of expert knowledge. - Stephen H. Muggleton (Turing Institute Pr., 1990)

Efficient Induction of Logic Programs. - Stephen H. Muggleton; Cao Feng (ALT, 1990)

Learning to Relate Terms in a Multiple Agent Environment. - Pavel Brazdil; Stephen H. Muggleton (EWSL, 1991)

Learning Qualitative Models of Dynamic Systems. - Ivan Bratko; Stephen H. Muggleton; Alen Varsek (ML, 1991)

Inductive Logic Programming. - Stephen H. Muggleton (New Gener. Comput., 1991)

PAC-Learnability of Determinate Logic Programs. - Saso Dzeroski; Stephen H. Muggleton; Stuart Russell 001 (COLT, 1992)

Developments in Inductive Logic Programming, Panel Position Paper. - Stephen H. Muggleton (FGCS, 1992)

Towards Inductive Generalization in Higher Order Logic. - Cao Feng; Stephen H. Muggleton (ML, 1992)

Compression, Significance, and Accuracy. - Stephen H. Muggleton; Ashwin Srinivasan 001; Michael Bain 001 (ML, 1992)

Learning optimal chess strategies. - Michael Bain 001; Stephen H. Muggleton (Machine Intelligence 13, 1992)

Logic and Learning: Turing's legacy. - Stephen H. Muggleton (Machine Intelligence 13, 1992)

The Justification of Logical Theories based on Data Compression. - Ashwin Srinivasan 001; Stephen H. Muggleton; Michael Bain 001 (Machine Intelligence 13, 1992)

Machine Learning and biomolecular modelling. - Michael J. E. Sternberg; R. A. Lewis; Ross D. King; Stephen H. Muggleton (Machine Intelligence 13, 1992)

Statlog Project. - Cao Feng; A. Sutherland; S. King; Stephen H. Muggleton; Bob Henery (UCI Machine Learning Repository, 1992)

Optimal Layered Learning: A PAC Approach to Incremental Sampling. - Stephen H. Muggleton (ALT, 1993)

Learnability of Constrained Logic Programs. - Saso Dzeroski; Stephen H. Muggleton; Stuart Russell 001 (ECML, 1993)

Inverting Entailment and Progol. - Stephen H. Muggleton (Machine Intelligence 14, 1993)

Bayesian Inductive Logic Programming. - Stephen H. Muggleton (ICML, 1994)

Predicate invention and utilization. - Stephen H. Muggleton (J. Exp. Theor. Artif. Intell., 1994)

Inductive Logic Programming: Theory and Methods. - Stephen H. Muggleton; Luc De Raedt (J. Log. Program., 1994)

Inductive Logic Programming: Derivations, Successes and Shortcomings. - Stephen H. Muggleton (SIGART Bull., 1994)

Inductive Logic Programming: Inverse Resolution and Beyond. - Stephen H. Muggleton (IJCAI (1), 1995)

A Learnability Model for Universal Representations and Its Application to Top-down Induction of Decision Trees. - Stephen H. Muggleton; David Page (Machine Intelligence 15, 1995)

An Experiment with Browsers that Learn. - Rupert Parson; Stephen H. Muggleton (Machine Intelligence 15, 1995)

Drug Design by Machine Learning. - Michael J. E. Sternberg; Ross D. King; Ashwin Srinivasan 001; Stephen H. Muggleton (Machine Intelligence 15, 1995)

Applications of Inductive Logic Programming. - Ivan Bratko; Stephen H. Muggleton (Commun. ACM, 1995)

Inverse Entailment and Progol. - Stephen H. Muggleton (New Gener. Comput., 1995)

Special Issue on Inductive Logic Programming. - Stephen H. Muggleton; Fumio Mizoguchi; Koichi Furukawa (New Gener. Comput., 1995)

Learning from Positive Data. - Stephen H. Muggleton (Inductive Logic Programming Workshop, 1996)

An Initial Experiment into Stereochemistry-Based Drug Design Using Inductive Logic Programming. - Stephen H. Muggleton; David Page; Ashwin Srinivasan 001 (Inductive Logic Programming Workshop, 1996)

Theories for Mutagenicity: A Study in First-Order and Feature-Based Induction. - Ashwin Srinivasan 001; Stephen H. Muggleton; Michael J. E. Sternberg; Ross D. King (Artif. Intell., 1996)

Machine Intelligibility and the Duality Principle. - Stephen H. Muggleton; Donald Michie (Software Agents and Soft Computing, 1997)

The Predictive Toxicology Evaluation Challenge. - Ashwin Srinivasan 001; Ross D. King; Stephen H. Muggleton; Michael J. E. Sternberg (IJCAI (1), 1997)

Learning Programs in the Event Calculus. - Stephen Moyle; Stephen H. Muggleton (ILP, 1997)

Carcinogenesis Predictions Using ILP. - Ashwin Srinivasan 001; Ross D. King; Stephen H. Muggleton; Michael J. E. Sternberg (ILP, 1997)

Guest Editors' Introduction. - Stephen H. Muggleton; David Page (Mach. Learn., 1997)

Knowledge Discovery in Biological and Chemical Domains. - Stephen H. Muggleton (Discovery Science, 1998)

Biochemical Knowledge Discovery Using Inductive Logic Programming. - Stephen H. Muggleton; Ashwin Srinivasan 001; Ross D. King; Michael J. E. Sternberg (Discovery Science, 1998)

Inductive Logic Programming: Issues, Results and the LLL Challenge (abstract). - Stephen H. Muggleton (ECAI, 1998)

Detecting Traffic Problems with ILP. - Saso Dzeroski; Nico Jacobs; Martín Molina; Carlos Moure; Stephen H. Muggleton; Wim Van Laer (ILP, 1998)

Repeat Learning Using Predicate Invention. - Khalid Khan; Stephen H. Muggleton; Rupert Parson (ILP, 1998)

Advances in ILP Theory and Implementations (Abstract). - Stephen H. Muggleton (ILP, 1998)

Completing Inverse Entailment. - Stephen H. Muggleton (ILP, 1998)

A Comparison of ILP and Propositional Systems on Propositional Traffic Data. - Sam Roberts; Wim Van Laer; Nico Jacobs; Stephen H. Muggleton; Jeremy Broughton (ILP, 1998)

Application of Inductive Logic Programming to Discover Rules Governing the Three-Dimensional Topology of Protein Structure. - Marcel Turcotte; Stephen H. Muggleton; Michael J. E. Sternberg (ILP, 1998)

Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL. - Paul W. Finn; Stephen H. Muggleton; David Page; Ashwin Srinivasan 001 (Mach. Learn., 1998)

Analogical Prediction. - Stephen H. Muggleton; Michael Bain 001 (ILP, 1999)

Theory Recovery. - Rupert Parson; Khalid Khan; Stephen H. Muggleton (ILP, 1999)

Inductive Logic Programming: Issues, Results and the Challenge of Learning Language in Logic. - Stephen H. Muggleton (Artif. Intell., 1999)

Scientific Knowledge Discovery Using Inductive Logic Programming. - Stephen H. Muggleton (Commun. ACM, 1999)

Guest Editors' Introduction: Inductive Logic Programming. - Stephen H. Muggleton; David Page (J. Log. Program., 1999)

Measuring Performance when Positives Are Rare: Relative Advantage versus Predictive Accuracy - A Biological Case Study. - Stephen H. Muggleton; Christopher H. Bryant; Ashwin Srinivasan 001 (ECML, 2000)

Learning Chomsky-like Grammars for Biological Sequence Families. - Stephen H. Muggleton; Christopher H. Bryant; Ashwin Srinivasan 001 (ICML, 2000)

Theory Completion Using Inverse Entailment. - Stephen H. Muggleton; Christopher H. Bryant (ILP, 2000)

Searching the Subsumption Lattice by a Genetic Algorithm. - Alireza Tamaddoni-Nezhad; Stephen H. Muggleton (ILP, 2000)

Learning Stochastic Logic Programs. - Stephen H. Muggleton (Electron. Trans. Artif. Intell., 2000)

Use of Inductive Logic Programming to Learn Principles of Protein Structure. - Marcel Turcotte; Stephen H. Muggleton; Michael J. E. Sternberg (Electron. Trans. Artif. Intell., 2000)

Combining Inductive Logic Programming, Active Learning and Robotics to Discover the Function of Genes. - Christopher H. Bryant; Stephen H. Muggleton; Stephen G. Oliver; Douglas B. Kell; Philip G. K. Reiser; Ross D. King (Electron. Trans. Artif. Intell., 2001)

Automatic determination of protein fold signatures from structural superpositions. - A. P. Cootes; Stephen H. Muggleton; R. B. Greaves; Michael J. E. Sternberg (Electron. Trans. Artif. Intell., 2001)

Developing a Logical Model of Yeast Metabolism. - Philip G. K. Reiser; Ross D. King; Douglas B. Kell; Stephen H. Muggleton; Christopher H. Bryant; Stephen G. Oliver (Electron. Trans. Artif. Intell., 2001)

Are Grammatical Representations Useful for Learning from Biological Sequence Data? - A Case Study. - Stephen H. Muggleton; Christopher H. Bryant; Ashwin Srinivasan 001; Alex Whittaker; Simon Topp; Christopher J. Rawlings (J. Comput. Biol., 2001)

The Effect of Relational Background Knowledge on Learning of Protein Three-Dimensional Fold Signatures. - Marcel Turcotte; Stephen H. Muggleton; Michael J. E. Sternberg (Mach. Learn., 2001)

Scalable acceleration of inductive logic programs. - Andreas Fidjeland; Wayne Luk; Stephen H. Muggleton (FPT, 2002)

Learning Structure and Parameters of Stochastic Logic Programs. - Stephen H. Muggleton (ILP, 2002)

A Genetic Algorithms Approach to ILP. - Alireza Tamaddoni-Nezhad; Stephen H. Muggleton (ILP, 2002)

Generating Protein Three-dimensional Fold Signatures using Inductive Logic Programming. - Marcel Turcotte; Stephen H. Muggleton; Michael J. E. Sternberg (Comput. Chem., 2002)

ILP for Mathematical Discovery. - Simon Colton; Stephen H. Muggleton (ILP, 2003)

Induction of Enzyme Classes from Biological Databases. - Stephen H. Muggleton; Alireza Tamaddoni-Nezhad; Hiroaki Watanabe (ILP, 2003)

Interpretation of Hidden Node Methodology in Automated Classification of Neural Cell Morphology. - Jung-Wook Bang; Alexandros Pappas; Duncan Fyfe Gillies; Stephen H. Muggleton (METMBS, 2003)

Modelling Metabolic Pathways Using Stochastic Logic Programs-Based Ensemble Methods. - Huma Lodhi; Stephen H. Muggleton (CMSB, 2004)

Modelling Inhibition in Metabolic Pathways Through Abduction and Induction. - Alireza Tamaddoni-Nezhad; Antonis C. Kakas; Stephen H. Muggleton; Florencio Pazos (ILP, 2004)

Learning the Time Complexity of Logic Programs. - Stephen H. Muggleton (AAIP, 2005)

05051 Executive Summary - Probabilistic, Logical and Relational Learning - Towards a Synthesis. - Luc De Raedt; Thomas G. Dietterich; Lise Getoor; Stephen H. Muggleton (Probabilistic, Logical and Relational Learning, 2005)

05051 Abstracts Collection - Probabilistic, Logical and Relational Learning - Towards a Synthesis. - Luc De Raedt; Thomas G. Dietterich; Lise Getoor; Stephen H. Muggleton (Probabilistic, Logical and Relational Learning, 2005)

Support Vector Inductive Logic Programming. - Stephen H. Muggleton; Huma Lodhi; Ata Amini; Michael J. E. Sternberg (Discovery Science, 2005)

Abduction and induction for learning models of inhibition in metabolic networks. - Alireza Tamaddoni-Nezhad; Raphael Chaleil; Antonis C. Kakas; Stephen H. Muggleton (ICMLA, 2005)

Machine Learning for Systems Biology. - Stephen H. Muggleton (ILP, 2005)

Learning Stochastic Logical Automaton. - Hiroaki Watanabe; Stephen H. Muggleton (JSAI Workshops, 2005)

Computing Confidence Measures in Stochastic Logic Programs. - Huma Lodhi; Stephen H. Muggleton (MICAI, 2005)

Towards Chemical Universal Turing Machines. - Stephen H. Muggleton (AAAI, 2006)

Multi-class Prediction Using Stochastic Logic Programs. - Jianzhong Chen; Lawrence A. Kelley; Stephen H. Muggleton; Michael J. E. Sternberg (ILP, 2006)

The Complexity of Translating BLPs to RMMs. - Stephen H. Muggleton; Niels Pahlavi (ILP, 2006)

Mathematical applications of inductive logic programming. - Simon Colton; Stephen H. Muggleton (Mach. Learn., 2006)

Application of abductive ILP to learning metabolic network inhibition from temporal data. - Alireza Tamaddoni-Nezhad; Raphael Chaleil; Antonis C. Kakas; Stephen H. Muggleton (Mach. Learn., 2006)

07161 Abstracts Collection – Probabilistic, Logical and Relational Learning - A Further Synthesis. - Luc De Raedt; Thomas G. Dietterich; Lise Getoor; Kristian Kersting; Stephen H. Muggleton (Probabilistic, Logical and Relational Learning - A Further Synthesis, 2007)

Learning Probabilistic Logic Models from Probabilistic Examples (Extended Abstract). - Jianzhong Chen; Stephen H. Muggleton; Jose Santos 001 (ILP, 2007)

Abductive Stochastic Logic Programs for Metabolic Network Inhibition Learning. - Jianzhong Chen; Stephen H. Muggleton; Jose Santos 001 (MLG, 2007)

Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds. - Edward O. Cannon; Ata Amini; Andreas Bender 002; Michael J. E. Sternberg; Stephen H. Muggleton; Robert C. Glen; John B. O. Mitchell (J. Comput. Aided Mol. Des., 2007)

A Novel Logic-Based Approach for Quantitative Toxicology Prediction. - Ata Amini; Stephen H. Muggleton; Huma Lodhi; Michael J. E. Sternberg (J. Chem. Inf. Model., 2007)

A Customisable Multiprocessor for Application-Optimised Inductive Logic Programming. - Andreas Fidjeland; Wayne Luk; Stephen H. Muggleton (BCS Int. Acad. Conf., 2008)

TopLog: ILP Using a Logic Program Declarative Bias. - Stephen H. Muggleton; José Carlos Almeida Santos; Alireza Tamaddoni-Nezhad (ICLP, 2008)

Protein Fold Discovery Using Stochastic Logic Programs. - Jianzhong Chen; Lawrence A. Kelley; Stephen H. Muggleton; Michael J. E. Sternberg (Probabilistic Inductive Logic Programming, 2008)

A Behavioral Comparison of Some Probabilistic Logic Models. - Stephen H. Muggleton; Jianzhong Chen (Probabilistic Inductive Logic Programming, 2008)

A Note on Refinement Operators for IE-Based ILP Systems. - Alireza Tamaddoni-Nezhad; Stephen H. Muggleton (ILP, 2008)

Developing Robust Synthetic Biology Designs Using a Microfluidic Robot Scientist. - Stephen H. Muggleton (SBIA, 2008)

From ILP to PILP. - Stephen H. Muggleton (SBIA, 2008)

Scaffold Hopping in Drug Discovery Using Inductive Logic Programming. - Kazuhisa Tsunoyama; Ata Amini; Michael J. E. Sternberg; Stephen H. Muggleton (J. Chem. Inf. Model., 2008)

Learning probabilistic logic models from probabilistic examples. - Jianzhong Chen; Stephen H. Muggleton; José Carlos Almeida Santos (Mach. Learn., 2008)

Structured machine learning: the next ten years. - Thomas G. Dietterich; Pedro M. Domingos; Lise Getoor; Stephen H. Muggleton; Prasad Tadepalli (Mach. Learn., 2008)

Guest editorial: special issue on Inductive Logic Programming. - Stephen H. Muggleton; Ramón P. Otero; Simon Colton (Mach. Learn., 2008)

QG/GA: a stochastic search for Progol. - Stephen H. Muggleton; Alireza Tamaddoni-Nezhad (Mach. Learn., 2008)

Learning Large Margin First Order Decision Lists for Multi-Class Classification. - Huma Lodhi; Stephen H. Muggleton; Michael J. E. Sternberg (Discovery Science, 2009)

An ILP System for Learning Head Output Connected Predicates. - José Carlos Almeida Santos; Alireza Tamaddoni-Nezhad; Stephen H. Muggleton (EPIA, 2009)

Chess Revision: Acquiring the Rules of Chess Variants through FOL Theory Revision from Examples. - Stephen H. Muggleton; Aline Paes; Vítor Santos Costa; Gerson Zaverucha (ILP, 2009)

ProGolem: A System Based on Relative Minimal Generalisation. - Stephen H. Muggleton; José Carlos Almeida Santos; Alireza Tamaddoni-Nezhad (ILP, 2009)

Can ILP Be Applied to Large Datasets? - Hiroaki Watanabe; Stephen H. Muggleton (ILP, 2009)

Abstracting Probabilistic Relational Model by Specialisation. - Hiroaki Watanabe; Stephen H. Muggleton (MLG/SRL@ILP, 2009)

The lattice structure and refinement operators for the hypothesis space bounded by a bottom clause. - Alireza Tamaddoni-Nezhad; Stephen H. Muggleton (Mach. Learn., 2009)

Multi-Class protein fold recognition using large margin logic based divide and conquer learning. - Huma Lodhi; Stephen H. Muggleton; Michael J. E. Sternberg (SIGKDD Explor., 2009)

Subsumer: A Prolog theta-subsumption engine. - Jose Santos 001; Stephen H. Muggleton (ICLP (Technical Communications), 2010)

Knowledge Mining Biological Network Models. - Stephen H. Muggleton (IIP, 2010)

Variation of Background Knowledge in an Industrial Application of 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 (ILP, 2010)

Can HOLL Outperform FOLL? - Niels Pahlavi; Stephen H. Muggleton (ILP, 2010)

When Does It Pay Off to Use Sophisticated Entailment Engines in ILP? - Jose Santos 001; Stephen H. Muggleton (ILP, 2010)

Stochastic Refinement. - Alireza Tamaddoni-Nezhad; Stephen H. Muggleton (ILP, 2010)

Customisable Multi-Processor Acceleration of Inductive Logic Programming. - Andreas Kirkeby Fidjeland; Wayne Luk; Stephen H. Muggleton (ILP (Late Breaking Papers), 2011)

Automatic Invention of Functional Abstractions. - Robert J. Henderson; Stephen H. Muggleton (ILP (Late Breaking Papers), 2011)

Does Multi-Clause Learning Help in Real-World Applications? - 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 (ILP, 2011)

MC-TopLog: Complete Multi-clause Learning Guided by a Top Theory. - Stephen H. Muggleton; Dianhuan Lin; Alireza Tamaddoni-Nezhad (ILP, 2011)

Can ILP Learn Complete and Correct Game Strategies? - Stephen H. Muggleton; Changze Xu (ILP (Late Breaking Papers), 2011)

Towards Efficient Higher-Order Logic Learning in a First-Order Datalog Framework. - Niels Pahlavi; Stephen H. Muggleton (ILP (Late Breaking Papers), 2011)

Machine Learning a Probabilistic Network of Ecological Interactions. - Alireza Tamaddoni-Nezhad; David A. Bohan; Alan Raybould; Stephen H. Muggleton (ILP, 2011)

Projection-Based PILP: Computational Learning Theory with Empirical Results. - Hiroaki Watanabe; Stephen H. Muggleton (ILP, 2011)

WIBL: Workbench for Integrative Biological Learning. - Victor Lesk; Jan Taubert; Christopher J. Rawlings; Stuart J. Dunbar; Stephen H. Muggleton (J. Integr. Bioinform., 2011)

The Scientific Contribution of Marek Sergot. - Steve Barker; Andrew J. I. Jones; Antonis C. Kakas; Robert A. Kowalski; Alessio Lomuscio; Rob Miller 002; Stephen H. Muggleton; Giovanni Sartor (Logic Programs, Norms and Action, 2012)

Machine Learning and Text Mining of Trophic Links. - Ghazal Afroozi Milani; David A. Bohan; Stuart J. Dunbar; Stephen H. Muggleton; Alan Raybould; Alireza Tamaddoni-Nezhad (ICMLA (2), 2012)

Automated identification of protein-ligand interaction features using Inductive Logic Programming: a hexose binding case study. - José Carlos Almeida Santos; Houssam Nassif; David Page; Stephen H. Muggleton; Michael J. E. Sternberg (BMC Bioinform., 2012)

Guest editorial: special issue on Inductive Logic Programming (ILP 2011). - Stephen H. Muggleton; Jianzhong Chen (Mach. Learn., 2012)

ILP turns 20 - Biography and future challenges. - Stephen H. Muggleton; Luc De Raedt; David Poole 001; Ivan Bratko; Peter A. Flach; Katsumi Inoue; Ashwin Srinivasan 001 (Mach. Learn., 2012)

Meta-Interpretive Learning of Higher-Order Dyadic Datalog: Predicate Invention revisited. - Stephen H. Muggleton; Dianhuan Lin (IJCAI, 2013)

MetaBayes: Bayesian Meta-Interpretative Learning Using Higher-Order Stochastic Refinement. - Stephen H. Muggleton; Dianhuan Lin; Jianzhong Chen; Alireza Tamaddoni-Nezhad (ILP, 2013)

Editors's Introduction to the Special Issue on "100 Years of Alan Turing and 20 Years of SLAIS". - Dunja Mladenic; Stephen H. Muggleton; Ivan Bratko (Informatica (Slovenia), 2013)

Bias reformulation for one-shot function induction. - Dianhuan Lin; Eyal Dechter; Kevin Ellis; Joshua B. Tenenbaum; Stephen H. Muggleton (ECAI, 2014)

Logical Minimisation of Meta-Rules Within Meta-Interpretive Learning. - Andrew Cropper; Stephen H. Muggleton (ILP, 2014)

Towards Machine Learning of Predictive Models from Ecological Data. - Alireza Tamaddoni-Nezhad; David A. Bohan; Alan Raybould; Stephen H. Muggleton (ILP, 2014)

Alan Turing and the development of Artificial Intelligence. - Stephen H. Muggleton (AI Commun., 2014)

Meta-interpretive learning: application to grammatical inference. - Stephen H. Muggleton; Dianhuan Lin; Niels Pahlavi; Alireza Tamaddoni-Nezhad (Mach. Learn., 2014)

Learning Efficient Logical Robot Strategies Involving Composable Objects. - Andrew Cropper; Stephen H. Muggleton (IJCAI, 2015)

Meta-Interpretive Learning of Data Transformation Programs. - Andrew Cropper; Alireza Tamaddoni-Nezhad; Stephen H. Muggleton (ILP, 2015)

Logical Vision: Meta-Interpretive Learning for Simple Geometrical Concepts. - Wang-Zhou Dai; Stephen H. Muggleton; Zhi-Hua Zhou (ILP (Late Breaking Papers), 2015)

Typed meta-interpretive learning for proof strategies. - Colin Farquhar; Gudmund Grov; Andrew Cropper; Stephen H. Muggleton; Alan Bundy (ILP (Late Breaking Papers), 2015)

Can predicate invention compensate for incomplete background knowledge? - Andrew Cropper; Stephen H. Muggleton (SCAI, 2015)

Inductive programming meets the real world. - Sumit Gulwani; José Hernández-Orallo; Emanuel Kitzelmann; Stephen H. Muggleton; Ute Schmid; Benjamin G. Zorn (Commun. ACM, 2015)

Approaches and Applications of Inductive Programming (Dagstuhl Seminar 15442). - José Hernández-Orallo; Stephen H. Muggleton; Ute Schmid; Benjamin G. Zorn (Dagstuhl Reports, 2015)

Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited. - Stephen H. Muggleton; Dianhuan Lin; Alireza Tamaddoni-Nezhad (Mach. Learn., 2015)

Learning Higher-Order Logic Programs through Abstraction and Invention. - Andrew Cropper; Stephen H. Muggleton (IJCAI, 2016)

How Does Predicate Invention Affect Human Comprehensibility? - Ute Schmid; Christina Zeller; Tarek R. Besold; Alireza Tamaddoni-Nezhad; Stephen H. Muggleton (ILP, 2016)

The Effect of Predicate Order on Curriculum Learning in ILP. - Hank Conn; Stephen H. Muggleton (ILP (Late Breaking Papers), 2017)

Logical Vision: One-Shot Meta-Interpretive Learning from Real Images. - Wang-Zhou Dai; Stephen H. Muggleton; Jing Wen; Alireza Tamaddoni-Nezhad; Zhi-Hua Zhou (ILP, 2017)

Meta-Interpretive Learning: Achievements and Challenges (Invited Paper). - Stephen H. Muggleton (RuleML+RR, 2017)

Approaches and Applications of Inductive Programming (Dagstuhl Seminar 17382). - Ute Schmid; Stephen H. Muggleton; Rishabh Singh (Dagstuhl Reports, 2017)

How Much Can Experimental Cost Be Reduced in Active Learning of Agent Strategies? - Céline Hocquette; Stephen H. Muggleton (ILP, 2018)

Meta-Interpretive Learning from noisy images. - Stephen H. Muggleton; Wang-Zhou Dai; Claude Sammut; Alireza Tamaddoni-Nezhad; Jing Wen; Zhi-Hua Zhou (Mach. Learn., 2018)

Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP. - Stephen H. Muggleton; Ute Schmid; Christina Zeller; Alireza Tamaddoni-Nezhad; Tarek R. Besold (Mach. Learn., 2018)

Can Meta-Interpretive Learning outperform Deep Reinforcement Learning of Evaluable Game strategies? - Céline Hocquette; Stephen H. Muggleton (CoRR, 2019)

Approaches and Applications of Inductive Programming (Dagstuhl Seminar 19202). - Luc De Raedt; Richard Evans 001; Stephen H. Muggleton; Ute Schmid (Dagstuhl Reports, 2019)

Learning efficient logic programs. - Andrew Cropper; Stephen H. Muggleton (Mach. Learn., 2019)

Special Issue on Semantic Technology. - Ryutaro Ichise; Stephen H. Muggleton; Kouji Kozaki; Freddy Lécué; Dongyan Zhao 001; Takahiro Kawamura (New Gener. Comput., 2019)

Machine Discovery of Comprehensible Strategies for Simple Games Using Meta-interpretive Learning. - Stephen H. Muggleton; Céline Hocquette (New Gener. Comput., 2019)

Learning Higher-Order Programs through Predicate Invention. - Andrew Cropper; Rolf Morel; Stephen H. Muggleton (AAAI, 2020)

Turning 30: New Ideas in Inductive Logic Programming. - Andrew Cropper; Sebastijan Dumancic; Stephen H. Muggleton (IJCAI, 2020)

Complete Bottom-Up Predicate Invention in Meta-Interpretive Learning. - Céline Hocquette; Stephen H. Muggleton (IJCAI, 2020)

Learning higher-order logic programs. - Andrew Cropper; Rolf Morel; Stephen H. Muggleton (Mach. Learn., 2020)

Abductive Learning with Ground Knowledge Base. - Le-Wen Cai; Wang-Zhou Dai; Yu-Xuan Huang; Yufeng Li 008; Stephen H. Muggleton; Yuan Jiang 001 (IJCAI, 2021)

Abductive Knowledge Induction from Raw Data. - Wang-Zhou Dai; Stephen H. Muggleton (IJCAI, 2021)

Machine Learning of Microbial Interactions Using Abductive ILP and Hypothesis Frequency/Compression Estimation. - Didac Barroso-Bergada; Alireza Tamaddoni-Nezhad; Stephen H. Muggleton; Corinne Vacher; Nika Galic; David A. Bohan (ILP, 2021)

Human-Like Rule Learning from Images Using One-Shot Hypothesis Derivation. - Dany Varghese; Roman Bauer 001; Daniel Baxter-Beard; Stephen H. Muggleton; Alireza Tamaddoni-Nezhad (ILP, 2021)

Fast Abductive Learning by Similarity-based Consistency Optimization. - Yu-Xuan Huang; Wang-Zhou Dai; Le-Wen Cai; Stephen H. Muggleton; Yuan Jiang 001 (NeurIPS, 2021)

Automated Biodesign Engineering by Abductive Meta-Interpretive Learning. - Wang-Zhou Dai; Liam Hallett; Stephen H. Muggleton; Geoff S. Baldwin (CoRR, 2021)

Beneficial and harmful explanatory machine learning. - Lun Ai 001; Stephen H. Muggleton; Céline Hocquette; Mark Gromowski; Ute Schmid (Mach. Learn., 2021)

Top program construction and reduction for polynomial time Meta-Interpretive learning. - Stassa Patsantzis; Stephen H. Muggleton (Mach. Learn., 2021)

Human-like Computer Vision. - Stephen H. Muggleton; Wang-Zhou Dai (Human-Like Machine Intelligence, 2022)

Human-Machine Scientific Discovery. - Alireza Tamaddoni-Nezhad; David A. Bohan; Ghazal Afroozi Milani; Alan Raybould; Stephen H. Muggleton (Human-Like Machine Intelligence, 2022)

Inductive logic programming at 30. - Andrew Cropper; Sebastijan Dumancic; Richard Evans 001; Stephen H. Muggleton (Mach. Learn., 2022)

Correction to: Meta-interpretive learning as metarule specialisation. - Stassa Patsantzis; Stephen H. Muggleton (Mach. Learn., 2022)

Meta-interpretive learning as metarule specialisation. - Stassa Patsantzis; Stephen H. Muggleton (Mach. Learn., 2022)

Human Comprehensible Active Learning of Genome-Scale Metabolic Networks. - Lun Ai 001; Shi-Shun Liang; Wang-Zhou Dai; Liam Hallett; Stephen H. Muggleton; Geoff S. Baldwin (CoRR, 2023)

Explanatory machine learning for sequential human teaching. - Lun Ai 001; Johannes Langer; Stephen H. Muggleton; Ute Schmid (Mach. Learn., 2023)

Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models. - Lun Ai 001; Stephen H. Muggleton; Shi-Shun Liang; Geoff S. Baldwin (CoRR, 2024)

Simulating Petri nets with Boolean Matrix Logic Programming. - Lun Ai 001; Stephen H. Muggleton; Shi-Shun Liang; Geoff S. Baldwin (CoRR, 2024)

Boolean Matrix Logic Programming. - Lun Ai 001; Stephen H. Muggleton (CoRR, 2024)

Active learning of digenic functions with boolean matrix logic programming. - Lun Ai 001; Stephen H. Muggleton; Shi-Shun Liang; Geoff S. Baldwin (CoRR, 2024)