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ORCIDStephen H. Muggleton
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- affiliation: Imperial College London, Department of Computing, UK
- affiliation: University of York, Department of Computer Science, UK
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2020 – today
- 2024
[e13]Stephen H. Muggleton, Alireza Tamaddoni-Nezhad:
Inductive Logic Programming - 31st International Conference, ILP 2022, Windsor Great Park, UK, September 28-30, 2022, Proceedings. Lecture Notes in Computer Science 13779, Springer 2024, ISBN 978-3-031-55629-6 [contents]- [i21]Lun Ai, Stephen H. Muggleton, Shi-Shun Liang, Geoff S. Baldwin:
Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models. CoRR abs/2405.06724 (2024) - [i20]Lun Ai, Stephen H. Muggleton, Shi-Shun Liang, Geoff S. Baldwin:
Simulating Petri nets with Boolean Matrix Logic Programming. CoRR abs/2405.11412 (2024) - [i19]Lun Ai, Stephen H. Muggleton:
Boolean Matrix Logic Programming. CoRR abs/2408.10369 (2024) - [i18]Lun Ai, Stephen H. Muggleton, Shi-Shun Liang, Geoff S. Baldwin:
Active learning of digenic functions with boolean matrix logic programming. CoRR abs/2408.14487 (2024) - 2023
- [j53]Lun Ai
, Johannes Langer, Stephen H. Muggleton, Ute Schmid:
Explanatory machine learning for sequential human teaching. Mach. Learn. 112(10): 3591-3632 (2023) - [i17]Lun Ai, Shi-Shun Liang, Wang-Zhou Dai, Liam Hallett, Stephen H. Muggleton, Geoff S. Baldwin:
Human Comprehensible Active Learning of Genome-Scale Metabolic Networks. CoRR abs/2308.12740 (2023) - 2022
- [j52]Andrew Cropper, Sebastijan Dumancic, Richard Evans, Stephen H. Muggleton:
Inductive logic programming at 30. Mach. Learn. 111(1): 147-172 (2022) - [j51]Stassa Patsantzis, Stephen H. Muggleton:
Correction to: Meta-interpretive learning as metarule specialisation. Mach. Learn. 111(8): 3061 (2022) - [j50]Stassa Patsantzis, Stephen H. Muggleton:
Meta-interpretive learning as metarule specialisation. Mach. Learn. 111(10): 3703-3731 (2022) - [p4]Stephen H. Muggleton, Wang-Zhou Dai:
Human-like Computer Vision. Human-Like Machine Intelligence 2022: 199-217 - [p3]Alireza Tamaddoni-Nezhad, David A. Bohan, Ghazal Afroozi Milani, Alan Raybould, Stephen H. Muggleton:
Human-Machine Scientific Discovery. Human-Like Machine Intelligence 2022: 297-315 - [e12]Stephen H. Muggleton, Nicholas Chater:
Human-Like Machine Intelligence. Oxford University Press 2022, ISBN 9780191895333 [contents] - [i16]Lun Ai, Johannes Langer, Stephen H. Muggleton, Ute Schmid:
Explanatory machine learning for sequential human teaching. CoRR abs/2205.10250 (2022) - 2021
- [j49]Lun Ai, Stephen H. Muggleton, Céline Hocquette, Mark Gromowski, Ute Schmid:
Beneficial and harmful explanatory machine learning. Mach. Learn. 110(4): 695-721 (2021) - [j48]Stassa Patsantzis, Stephen H. Muggleton:
Top program construction and reduction for polynomial time Meta-Interpretive learning. Mach. Learn. 110(4): 755-778 (2021) - [c119]Le-Wen Cai, Wang-Zhou Dai, Yu-Xuan Huang, Yufeng Li, Stephen H. Muggleton, Yuan Jiang:
Abductive Learning with Ground Knowledge Base. IJCAI 2021: 1815-1821 - [c118]Wang-Zhou Dai, Stephen H. Muggleton:
Abductive Knowledge Induction from Raw Data. IJCAI 2021: 1845-1851 - [c117]Didac Barroso-Bergada, Alireza Tamaddoni-Nezhad, Stephen H. Muggleton, Corinne Vacher, Nika Galic, David A. Bohan:
Machine Learning of Microbial Interactions Using Abductive ILP and Hypothesis Frequency/Compression Estimation. ILP 2021: 26-40 - [c116]Dany Varghese, Roman Bauer, Daniel Baxter-Beard, Stephen H. Muggleton, Alireza Tamaddoni-Nezhad:
Human-Like Rule Learning from Images Using One-Shot Hypothesis Derivation. ILP 2021: 234-250 - [c115]Yu-Xuan Huang, Wang-Zhou Dai, Le-Wen Cai, Stephen H. Muggleton, Yuan Jiang:
Fast Abductive Learning by Similarity-based Consistency Optimization. NeurIPS 2021: 26574-26584 - [i15]Stassa Patsantzis, Stephen H. Muggleton:
Top Program Construction and Reduction for polynomial time Meta-Interpretive Learning. CoRR abs/2101.05050 (2021) - [i14]Andrew Cropper, Sebastijan Dumancic, Richard Evans, Stephen H. Muggleton:
Inductive logic programming at 30. CoRR abs/2102.10556 (2021) - [i13]Wang-Zhou Dai, Liam Hallett, Stephen H. Muggleton, Geoff S. Baldwin:
Automated Biodesign Engineering by Abductive Meta-Interpretive Learning. CoRR abs/2105.07758 (2021) - [i12]Stassa Patsantzis, Stephen H. Muggleton:
Meta-Interpretive Learning as Metarule Specialisation. CoRR abs/2106.07464 (2021) - 2020
- [j47]Andrew Cropper, Rolf Morel, Stephen H. Muggleton:
Learning higher-order logic programs. Mach. Learn. 109(7): 1289-1322 (2020) - [c114]Andrew Cropper, Rolf Morel, Stephen H. Muggleton:
Learning Higher-Order Programs through Predicate Invention. AAAI 2020: 13655-13658 - [c113]Céline Hocquette, Stephen H. Muggleton:
Complete Bottom-Up Predicate Invention in Meta-Interpretive Learning. IJCAI 2020: 2312-2318 - [c112]Andrew Cropper, Sebastijan Dumancic, Stephen H. Muggleton:
Turning 30: New Ideas in Inductive Logic Programming. IJCAI 2020: 4833-4839 - [i11]Andrew Cropper, Sebastijan Dumancic, Stephen H. Muggleton:
Turning 30: New Ideas in Inductive Logic Programming. CoRR abs/2002.11002 (2020) - [i10]Lun Ai, Stephen H. Muggleton, Céline Hocquette, Mark Gromowski, Ute Schmid:
Beneficial and Harmful Explanatory Machine Learning. CoRR abs/2009.06410 (2020) - [i9]Wang-Zhou Dai, Stephen H. Muggleton:
Abductive Knowledge Induction From Raw Data. CoRR abs/2010.03514 (2020)
2010 – 2019
- 2019
- [j46]Andrew Cropper, Stephen H. Muggleton:
Learning efficient logic programs. Mach. Learn. 108(7): 1063-1083 (2019) - [j45]Stephen H. Muggleton, Céline Hocquette:
Machine Discovery of Comprehensible Strategies for Simple Games Using Meta-interpretive Learning. New Gener. Comput. 37(2): 203-217 (2019) - [j44]Ryutaro Ichise, Stephen H. Muggleton, Kouji Kozaki, Freddy Lécué, Dongyan Zhao, Takahiro Kawamura:
Special Issue on Semantic Technology. New Gener. Comput. 37(4): 359-360 (2019) - [i8]Céline Hocquette, Stephen H. Muggleton:
Can Meta-Interpretive Learning outperform Deep Reinforcement Learning of Evaluable Game strategies? CoRR abs/1902.09835 (2019) - [i7]Andrew Cropper, Rolf Morel, Stephen H. Muggleton:
Learning higher-order logic programs. CoRR abs/1907.10953 (2019) - [i6]Luc De Raedt, Richard Evans, Stephen H. Muggleton, Ute Schmid:
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 19202). Dagstuhl Reports 9(5): 58-88 (2019) - 2018
- [j43]Stephen H. Muggleton, Wang-Zhou Dai, Claude Sammut, Alireza Tamaddoni-Nezhad, Jing Wen, Zhi-Hua Zhou:
Meta-Interpretive Learning from noisy images. Mach. Learn. 107(7): 1097-1118 (2018) - [j42]Stephen H. Muggleton, Ute Schmid, Christina Zeller, Alireza Tamaddoni-Nezhad, Tarek R. Besold:
Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP. Mach. Learn. 107(7): 1119-1140 (2018) - [c111]Céline Hocquette, Stephen H. Muggleton:
How Much Can Experimental Cost Be Reduced in Active Learning of Agent Strategies? ILP 2018: 38-53 - [e11]Ryutaro Ichise, Freddy Lécué, Takahiro Kawamura, Dongyan Zhao, Stephen H. Muggleton, Kouji Kozaki:
Semantic Technology - 8th Joint International Conference, JIST 2018, Awaji, Japan, November 26-28, 2018, Proceedings. Lecture Notes in Computer Science 11341, Springer 2018, ISBN 978-3-030-04283-7 [contents] - 2017
- [c110]Hank Conn, Stephen H. Muggleton:
The Effect of Predicate Order on Curriculum Learning in ILP. ILP (Late Breaking Papers) 2017: 17-21 - [c109]Wang-Zhou Dai, Stephen H. Muggleton, Jing Wen, Alireza Tamaddoni-Nezhad, Zhi-Hua Zhou:
Logical Vision: One-Shot Meta-Interpretive Learning from Real Images. ILP 2017: 46-62 - [c108]Stephen H. Muggleton:
Meta-Interpretive Learning: Achievements and Challenges (Invited Paper). RuleML+RR 2017: 1-6 - [i5]Ute Schmid, Stephen H. Muggleton, Rishabh Singh:
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 17382). Dagstuhl Reports 7(9): 86-108 (2017) - 2016
- [c107]Andrew Cropper, Stephen H. Muggleton:
Learning Higher-Order Logic Programs through Abstraction and Invention. IJCAI 2016: 1418-1424 - [c106]Ute Schmid, Christina Zeller, Tarek R. Besold, Alireza Tamaddoni-Nezhad, Stephen H. Muggleton:
How Does Predicate Invention Affect Human Comprehensibility? ILP 2016: 52-67 - 2015
- [j41]Sumit Gulwani, José Hernández-Orallo, Emanuel Kitzelmann, Stephen H. Muggleton, Ute Schmid, Benjamin G. Zorn:
Inductive programming meets the real world. Commun. ACM 58(11): 90-99 (2015) - [j40]Stephen H. Muggleton, Dianhuan Lin, Alireza Tamaddoni-Nezhad:
Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited. Mach. Learn. 100(1): 49-73 (2015) - [c105]Andrew Cropper, Stephen H. Muggleton:
Learning Efficient Logical Robot Strategies Involving Composable Objects. IJCAI 2015: 3423-3429 - [c104]Wang-Zhou Dai, Stephen H. Muggleton, Zhi-Hua Zhou:
Logical Vision: Meta-Interpretive Learning for Simple Geometrical Concepts. ILP (Late Breaking Papers) 2015: 1-16 - [c103]Colin Farquhar, Gudmund Grov, Andrew Cropper, Stephen H. Muggleton, Alan Bundy:
Typed meta-interpretive learning for proof strategies. ILP (Late Breaking Papers) 2015: 17-32 - [c102]Andrew Cropper, Alireza Tamaddoni-Nezhad, Stephen H. Muggleton:
Meta-Interpretive Learning of Data Transformation Programs. ILP 2015: 46-59 - [c101]Andrew Cropper, Stephen H. Muggleton:
Can predicate invention compensate for incomplete background knowledge? SCAI 2015: 27-36 - [i4]José Hernández-Orallo, Stephen H. Muggleton, Ute Schmid, Benjamin G. Zorn:
Approaches and Applications of Inductive Programming (Dagstuhl Seminar 15442). Dagstuhl Reports 5(10): 89-111 (2015) - 2014
- [j39]Stephen H. Muggleton:
Alan Turing and the development of Artificial Intelligence. AI Commun. 27(1): 3-10 (2014) - [j38]Stephen H. Muggleton, Dianhuan Lin, Niels Pahlavi, Alireza Tamaddoni-Nezhad:
Meta-interpretive learning: application to grammatical inference. Mach. Learn. 94(1): 25-49 (2014) - [c100]Dianhuan Lin, Eyal Dechter, Kevin Ellis, Joshua B. Tenenbaum, Stephen H. Muggleton:
Bias reformulation for one-shot function induction. ECAI 2014: 525-530 - [c99]Andrew Cropper, Stephen H. Muggleton:
Logical Minimisation of Meta-Rules Within Meta-Interpretive Learning. ILP 2014: 62-75 - [c98]Alireza Tamaddoni-Nezhad, David A. Bohan, Alan Raybould, Stephen H. Muggleton:
Towards Machine Learning of Predictive Models from Ecological Data. ILP 2014: 154-167 - [e10]Stephen H. Muggleton, Hiroaki Watanabe:
Latest Advances in Inductive Logic Programming, ILP 2011, Late Breaking Papers, Windsor Great Park, UK, July 31 - August 3, 2011. Imperial College Press / World Scientific 2014, ISBN 978-1-78326-508-4 [contents] - 2013
- [j37]Dunja Mladenic, Stephen H. Muggleton, Ivan Bratko:
Editors's Introduction to the Special Issue on "100 Years of Alan Turing and 20 Years of SLAIS". Informatica (Slovenia) 37(1): 1 (2013) - [c97]Stephen H. Muggleton, Dianhuan Lin:
Meta-Interpretive Learning of Higher-Order Dyadic Datalog: Predicate Invention revisited. IJCAI 2013: 1551-1557 - [c96]Stephen H. Muggleton, Dianhuan Lin, Jianzhong Chen, Alireza Tamaddoni-Nezhad:
MetaBayes: Bayesian Meta-Interpretative Learning Using Higher-Order Stochastic Refinement. ILP 2013: 1-17 - 2012
- [j36]José Carlos Almeida Santos, Houssam Nassif, David Page, Stephen H. Muggleton, Michael J. E. Sternberg:
Automated identification of protein-ligand interaction features using Inductive Logic Programming: a hexose binding case study. BMC Bioinform. 13: 162 (2012) - [j35]Stephen H. Muggleton, Luc De Raedt, David Poole, Ivan Bratko, Peter A. Flach, Katsumi Inoue, Ashwin Srinivasan:
ILP turns 20 - Biography and future challenges. Mach. Learn. 86(1): 3-23 (2012) - [j34]Stephen H. Muggleton, Jianzhong Chen:
Guest editorial: special issue on Inductive Logic Programming (ILP 2011). Mach. Learn. 89(3): 213-214 (2012) - [c95]Steve Barker, Andrew J. I. Jones, Antonis C. Kakas, Robert A. Kowalski, Alessio Lomuscio, Rob Miller, Stephen H. Muggleton, Giovanni Sartor:
The Scientific Contribution of Marek Sergot. Logic Programs, Norms and Action 2012: 4-11 - [c94]Ghazal Afroozi Milani, David A. Bohan, Stuart J. Dunbar, Stephen H. Muggleton, Alan Raybould, Alireza Tamaddoni-Nezhad:
Machine Learning and Text Mining of Trophic Links. ICMLA (2) 2012: 410-415 - [e9]Stephen H. Muggleton, Alireza Tamaddoni-Nezhad, Francesca A. Lisi:
Inductive Logic Programming - 21st International Conference, ILP 2011, Windsor Great Park, UK, July 31 - August 3, 2011, Revised Selected Papers. Lecture Notes in Computer Science 7207, Springer 2012, ISBN 978-3-642-31950-1 [contents] - 2011
- [j33]Victor Lesk, Jan Taubert, Christopher J. Rawlings, Stuart J. Dunbar, Stephen H. Muggleton:
WIBL: Workbench for Integrative Biological Learning. J. Integr. Bioinform. 8(2) (2011) - [c93]Stephen H. Muggleton, Changze Xu:
Can ILP Learn Complete and Correct Game Strategies? ILP (Late Breaking Papers) 2011: 3-10 - [c92]Andreas Kirkeby Fidjeland, Wayne Luk, Stephen H. Muggleton:
Customisable Multi-Processor Acceleration of Inductive Logic Programming. ILP (Late Breaking Papers) 2011: 123-141 - [c91]Niels Pahlavi, Stephen H. Muggleton:
Towards Efficient Higher-Order Logic Learning in a First-Order Datalog Framework. ILP (Late Breaking Papers) 2011: 209-216 - [c90]Robert J. Henderson, Stephen H. Muggleton:
Automatic Invention of Functional Abstractions. ILP (Late Breaking Papers) 2011: 217-224 - [c89]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:
Does Multi-Clause Learning Help in Real-World Applications? ILP 2011: 221-237 - [c88]Stephen H. Muggleton, Dianhuan Lin, Alireza Tamaddoni-Nezhad:
MC-TopLog: Complete Multi-clause Learning Guided by a Top Theory. ILP 2011: 238-254 - [c87]Alireza Tamaddoni-Nezhad, David A. Bohan, Alan Raybould, Stephen H. Muggleton:
Machine Learning a Probabilistic Network of Ecological Interactions. ILP 2011: 332-346 - [c86]Hiroaki Watanabe, Stephen H. Muggleton:
Projection-Based PILP: Computational Learning Theory with Empirical Results. ILP 2011: 358-372 - 2010
- [c85]Jose Santos, Stephen H. Muggleton:
Subsumer: A Prolog theta-subsumption engine. ICLP (Technical Communications) 2010: 172-181 - [c84]Stephen H. Muggleton:
Knowledge Mining Biological Network Models. Intelligent Information Processing 2010: 2 - [c83]Stephen H. Muggleton, Jianzhong Chen, Hiroaki Watanabe, Stuart J. Dunbar, Charles Baxter, Richard A. Currie, José Domingo Salazar, Jan Taubert, Michael J. E. Sternberg:
Variation of Background Knowledge in an Industrial Application of ILP. ILP 2010: 158-170 - [c82]Niels Pahlavi, Stephen H. Muggleton:
Can HOLL Outperform FOLL? ILP 2010: 198-205 - [c81]Jose Santos, Stephen H. Muggleton:
When Does It Pay Off to Use Sophisticated Entailment Engines in ILP? ILP 2010: 214-221 - [c80]Alireza Tamaddoni-Nezhad, Stephen H. Muggleton:
Stochastic Refinement. ILP 2010: 222-237
2000 – 2009
- 2009
- [j32]Alireza Tamaddoni-Nezhad, Stephen H. Muggleton:
The lattice structure and refinement operators for the hypothesis space bounded by a bottom clause. Mach. Learn. 76(1): 37-72 (2009) - [j31]Huma Lodhi, Stephen H. Muggleton, Michael J. E. Sternberg:
Multi-Class protein fold recognition using large margin logic based divide and conquer learning. SIGKDD Explor. 11(2): 117-122 (2009) - [c79]Huma Lodhi, Stephen H. Muggleton, Michael J. E. Sternberg:
Learning Large Margin First Order Decision Lists for Multi-Class Classification. Discovery Science 2009: 168-183 - [c78]José Carlos Almeida Santos, Alireza Tamaddoni-Nezhad, Stephen H. Muggleton:
An ILP System for Learning Head Output Connected Predicates. EPIA 2009: 150-159 - [c77]Stephen H. Muggleton, Aline Paes, Vítor Santos Costa, Gerson Zaverucha:
Chess Revision: Acquiring the Rules of Chess Variants through FOL Theory Revision from Examples. ILP 2009: 123-130 - [c76]Stephen H. Muggleton, José Carlos Almeida Santos, Alireza Tamaddoni-Nezhad:
ProGolem: A System Based on Relative Minimal Generalisation. ILP 2009: 131-148 - [c75]Hiroaki Watanabe, Stephen H. Muggleton:
Can ILP Be Applied to Large Datasets? ILP 2009: 249-256 - [c74]Huma Lodhi, Stephen H. Muggleton, Michael J. E. Sternberg:
Multi-class protein fold recognition using large margin logic based divide and conquer learning. KDD Workshop on Statistical and Relational Learning in Bioinformatics 2009: 22-26 - 2008
- [j30]Kazuhisa Tsunoyama, Ata Amini, Michael J. E. Sternberg, Stephen H. Muggleton:
Scaffold Hopping in Drug Discovery Using Inductive Logic Programming. J. Chem. Inf. Model. 48(5): 949-957 (2008) - [j29]Stephen H. Muggleton, Ramón P. Otero, Simon Colton:
Guest editorial: special issue on Inductive Logic Programming. Mach. Learn. 70(2-3): 119-120 (2008) - [j28]Stephen H. Muggleton, Alireza Tamaddoni-Nezhad:
QG/GA: a stochastic search for Progol. Mach. Learn. 70(2-3): 121-133 (2008) - [j27]Thomas G. Dietterich, Pedro M. Domingos, Lise Getoor, Stephen H. Muggleton, Prasad Tadepalli:
Structured machine learning: the next ten years. Mach. Learn. 73(1): 3-23 (2008) - [j26]Jianzhong Chen, Stephen H. Muggleton, José Carlos Almeida Santos:
Learning probabilistic logic models from probabilistic examples. Mach. Learn. 73(1): 55-85 (2008) - [c73]Andreas Fidjeland, Wayne Luk, Stephen H. Muggleton:
A Customisable Multiprocessor for Application-Optimised Inductive Logic Programming. BCS Int. Acad. Conf. 2008: 318-330 - [c72]Stephen H. Muggleton, José Carlos Almeida Santos, Alireza Tamaddoni-Nezhad:
TopLog: ILP Using a Logic Program Declarative Bias. ICLP 2008: 687-692 - [c71]Alireza Tamaddoni-Nezhad, Stephen H. Muggleton:
A Note on Refinement Operators for IE-Based ILP Systems. ILP 2008: 297-314 - [c70]Stephen H. Muggleton:
Developing Robust Synthetic Biology Designs Using a Microfluidic Robot Scientist. SBIA 2008: 4 - [c69]Stephen H. Muggleton:
From ILP to PILP. SBIA 2008: 7 - [p2]Jianzhong Chen, Lawrence A. Kelley, Stephen H. Muggleton, Michael J. E. Sternberg:
Protein Fold Discovery Using Stochastic Logic Programs. Probabilistic Inductive Logic Programming 2008: 244-262 - [p1]Stephen H. Muggleton, Jianzhong Chen:
A Behavioral Comparison of Some Probabilistic Logic Models. Probabilistic Inductive Logic Programming 2008: 305-324 - [e8]Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Kristian Kersting, Stephen H. Muggleton:
Probabilistic, Logical and Relational Learning - A Further Synthesis, 15.04. - 20.04.2007. Dagstuhl Seminar Proceedings 07161, Internationales Begegnungs- und Forschungszentrum fuer Informatik (IBFI), Schloss Dagstuhl, Germany 2008 [contents] - [e7]Luc De Raedt, Paolo Frasconi, Kristian Kersting, Stephen H. Muggleton:
Probabilistic Inductive Logic Programming - Theory and Applications. Lecture Notes in Computer Science 4911, Springer 2008, ISBN 978-3-540-78651-1 [contents] - 2007
- [j25]Edward O. Cannon, Ata Amini, Andreas Bender, Michael J. E. Sternberg, Stephen H. Muggleton, Robert C. Glen, John B. O. Mitchell:
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. 21(5): 269-280 (2007) - [j24]Ata Amini, Stephen H. Muggleton, Huma Lodhi, Michael J. E. Sternberg:
A Novel Logic-Based Approach for Quantitative Toxicology Prediction. J. Chem. Inf. Model. 47(3): 998-1006 (2007) - [c68]Jianzhong Chen, Stephen H. Muggleton, Jose Santos:
Learning Probabilistic Logic Models from Probabilistic Examples (Extended Abstract). ILP 2007: 22-23 - [c67]Jianzhong Chen, Stephen H. Muggleton, Jose Santos:
Abductive Stochastic Logic Programs for Metabolic Network Inhibition Learning. MLG 2007 - [e6]Stephen H. Muggleton, Ramón P. Otero, Alireza Tamaddoni-Nezhad:
Inductive Logic Programming, 16th International Conference, ILP 2006, Santiago de Compostela, Spain, August 24-27, 2006, Revised Selected Papers. Lecture Notes in Computer Science 4455, Springer 2007, ISBN 978-3-540-73846-6 [contents] - [i3]Luc De Raedt, Thomas G. Dietterich, Lise Getoor, Kristian Kersting, Stephen H. Muggleton:
07161 Abstracts Collection -- Probabilistic, Logical and Relational Learning - A Further Synthesis. Probabilistic, Logical and Relational Learning - A Further Synthesis 2007 - 2006
- [j23]Simon Colton, Stephen H. Muggleton:
Mathematical applications of inductive logic programming. Mach. Learn. 64(1-3): 25-64 (2006) - [j22]Alireza Tamaddoni-Nezhad, Raphael Chaleil, Antonis C. Kakas, Stephen H. Muggleton:
Application of abductive ILP to learning metabolic network inhibition from temporal data. Mach. Learn. 64(1-3): 209-230 (2006) - [c66]