2010 - Fellow of the Royal Academy of Engineering (UK)
2002 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to the theory and practice of inductive logic programming, especially applied to the discovery of new biomolecular theories from observational data.
Stephen Muggleton focuses on Artificial intelligence, Inductive logic programming, PROGOL, Machine learning and Programming language. His Artificial intelligence study combines topics from a wide range of disciplines, such as Field, Inductive programming and Golem. His Inductive logic programming research is multidisciplinary, incorporating perspectives in Theoretical computer science, Logic programming, Prolog, Structure and Rule-based machine translation.
His work in PROGOL tackles topics such as Logical programming which are related to areas like Upper and lower bounds and Greedy algorithm. His study in the field of Learning classifier system, Artificial neural network, Stability and Algorithmic learning theory is also linked to topics like Multi-task learning. As a part of the same scientific study, Stephen Muggleton usually deals with the Programming language, concentrating on Inductive reasoning and frequently concerns with Rule of inference, Inference, Knowledge acquisition and Probabilistic logic.
Stephen Muggleton mostly deals with Artificial intelligence, Inductive logic programming, Machine learning, Theoretical computer science and Programming language. His Artificial intelligence research incorporates elements of Natural language processing and Statistical relational learning. Stephen Muggleton works in the field of Inductive logic programming, focusing on PROGOL in particular.
The PROGOL study combines topics in areas such as Algorithm and Logical programming. Stephen Muggleton has researched Machine learning in several fields, including Domain, Sequence and Golem. His Programming language research is mostly focused on the topic Horn clause.
The scientist’s investigation covers issues in Artificial intelligence, Predicate, Inductive logic programming, Programming language and Machine learning. Stephen Muggleton interconnects Simple and Natural language processing in the investigation of issues within Artificial intelligence. His Predicate study integrates concerns from other disciplines, such as Structure, Key, Recursion and Higher-order logic.
His Inductive logic programming research incorporates themes from Theoretical computer science, Artificial neural network, Field, Class and String. His Theoretical computer science research focuses on Robot and how it relates to Sorting. When carried out as part of a general Machine learning research project, his work on Active learning is frequently linked to work in Human learning, Context and Predictive toxicology, therefore connecting diverse disciplines of study.
Stephen Muggleton mainly focuses on Artificial intelligence, Predicate, Inductive logic programming, Theoretical computer science and Programming language. As part of the same scientific family, he usually focuses on Artificial intelligence, concentrating on Natural language processing and intersecting with Prolog and Regular language. His study in Predicate is interdisciplinary in nature, drawing from both Set, Structure, Algorithm, Recursion and Machine learning.
Stephen Muggleton merges Inductive logic programming with Focus in his research. His research in Theoretical computer science tackles topics such as Robot which are related to areas like Time complexity and Sorting. His study in the field of Inductive programming, Higher-order logic and Logic program also crosses realms of Redistribution.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Inductive Logic Programming : Theory and Methods
Stephen Muggleton;Luc de Raedt.
Journal of Logic Programming (1994)
Inverse entailment and PROGOL
Stephen Muggleton.
New Generation Computing (1995)
Efficient Induction of Logic Programs
S. Muggleton;C. Feng.
algorithmic learning theory (1990)
Machine invention of first order predicates by inverting resolution
Stephen Muggleton;Wray L. Buntine.
international conference on machine learning (1988)
Functional genomic hypothesis generation and experimentation by a robot scientist
Ross D. King;Kenneth E. Whelan;Ffion M. Jones;Philip G. K. Reiser.
Nature (2004)
Theories for mutagenicity: a study in first-order and feature-based induction
Ashwin Srinivasan;S. H. Muggleton;M. J. E. Sternberg;R. D. King.
Artificial Intelligence (1996)
Protein secondary structure prediction using logic-based machine learning
S. Muggleton;R.D. King;M.J.E. Sternberg.
Protein Engineering Design & Selection (1992)
Drug design by machine learning: the use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase.
Ross D. King;Stephen Muggleton;Richard A. Lewis;Michael J. E. Sternberg.
Proceedings of the National Academy of Sciences of the United States of America (1992)
Structure-activity relationships derived by machine learning: the use of atoms and their bond connectivities to predict mutagenicity by inductive logic programming.
Ross D. King;Stephen H. Muggleton;Ashwin Srinivasan;Michael J. E. Sternberg.
Proceedings of the National Academy of Sciences of the United States of America (1996)
Learning from Positive Data
Stephen Muggleton.
inductive logic programming (1996)
Profile was last updated on December 6th, 2021.
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