Stephen Muggleton

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Professor
Stephen Muggleton
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NewFellowPhoto.jpg
Muggleton in 2010
Born (1959-12-06) 6 December 1959 (age 64)
Alma mater University of Edinburgh
Known for
Awards
Scientific career
Fields
Institutions
Thesis Inductive acquisition of expert knowledge  (1987)
Doctoral advisor Donald Michie [3]
Website www.doc.ic.ac.uk/~shm

Stephen H. Muggleton (born 6 December 1959, son of Louis Muggleton) is Professor of Machine Learning and Head of the Computational Bioinformatics Laboratory at Imperial College London. [2] [4] [5] [6] [7] [8] [9]

Contents

Education

Muggleton received his Bachelor of Science degree in computer science (1982) and Doctor of Philosophy in artificial intelligence (1986) supervised by Donald Michie at the University of Edinburgh. [10]

Career

Following his PhD, Muggleton went on to work as a postdoctoral research associate at the Turing Institute in Glasgow (1987–1991) and later an EPSRC Advanced Research Fellow at Oxford University Computing Laboratory (OUCL) (1992–1997) where he founded the Machine Learning Group. [11] In 1997 he moved to the University of York and in 2001 to Imperial College London.

Research

Muggleton's research interests [5] [12] are primarily in Artificial intelligence. From 1997 to 2001 he held the Chair of Machine Learning at the University of York [13] and from 2001 to 2006 the EPSRC Chair of Computational Bioinformatics at Imperial College in London. Since 2013 he holds the Syngenta/Royal Academy of Engineering Research Chair [14] as well as the post of Director of Modelling for the Imperial College Centre for Integrated Systems Biology. [14] He is known for founding the field of Inductive logic programming. [15] [16] [17] [18] [19] In this field he has made contributions to theory introducing predicate invention, inverse entailment and stochastic logic programs. He has also played a role in systems development where he was instrumental in the systems Duce, Cigol, Golem, [20] Progol and Metagol [21] and applications – especially biological prediction tasks.

He worked on a Robot Scientist together with Ross D. King [22] that is capable of combining Inductive Logic Programming with active learning. [23] His present work concentrates on the development of Meta-Interpretive Learning, [21] a new form of Inductive Logic Programming which supports predicate invention and learning of recursive programs.

Related Research Articles

Logic programming is a programming, database and knowledge representation paradigm based on formal logic. A logic program is a set of sentences in logical form, representing knowledge about some problem domain. Computation is performed by applying logical reasoning to that knowledge, to solve problems in the domain. Major logic programming language families include Prolog, Answer Set Programming (ASP) and Datalog. In all of these languages, rules are written in the form of clauses:

Prolog is a logic programming language that has its origins in artificial intelligence, automated theorem proving and computational linguistics.

<span class="mw-page-title-main">Inductive logic programming</span>

Inductive logic programming (ILP) is a subfield of symbolic artificial intelligence which uses logic programming as a uniform representation for examples, background knowledge and hypotheses. The term "inductive" here refers to philosophical rather than mathematical induction. Given an encoding of the known background knowledge and a set of examples represented as a logical database of facts, an ILP system will derive a hypothesised logic program which entails all the positive and none of the negative examples.

Hypercomputation or super-Turing computation is a set of hypothetical models of computation that can provide outputs that are not Turing-computable. For example, a machine that could solve the halting problem would be a hypercomputer; so too would one that could correctly evaluate every statement in Peano arithmetic.

Solomonoff's theory of inductive inference proves that, under its common sense assumptions (axioms), the best possible scientific model is the shortest algorithm that generates the empirical data under consideration. In addition to the choice of data, other assumptions are that, to avoid the post-hoc fallacy, the programming language must be chosen prior to the data and that the environment being observed is generated by an unknown algorithm. This is also called a theory of induction. Due to its basis in the dynamical character of Algorithmic Information Theory, it encompasses statistical as well as dynamical information criteria for model selection. It was introduced by Ray Solomonoff, based on probability theory and theoretical computer science. In essence, Solomonoff's induction derives the posterior probability of any computable theory, given a sequence of observed data. This posterior probability is derived from Bayes' rule and some universal prior, that is, a prior that assigns a positive probability to any computable theory.

Golem is an inductive logic programming algorithm developed by Stephen Muggleton and Cao Feng in 1990. It uses the technique of relative least general generalisation proposed by Gordon Plotkin, leading to a bottom-up search through the subsumption lattice. In 1992, shortly after its introduction, Golem was considered the only inductive logic programming system capable of scaling to tens of thousands of examples.

<i>Machine Learning</i> (journal) Academic journal

Machine Learning is a peer-reviewed scientific journal, published since 1986.

<span class="mw-page-title-main">Donald Michie</span> British artificial intelligence researcher

Donald Michie was a British researcher in artificial intelligence. During World War II, Michie worked for the Government Code and Cypher School at Bletchley Park, contributing to the effort to solve "Tunny", a German teleprinter cipher.

<span class="mw-page-title-main">Alan Bundy</span> British artificial intelligence researcher (born 1947)

Alan Richard Bundy is a professor at the School of Informatics at the University of Edinburgh, known for his contributions to automated reasoning, especially to proof planning, the use of meta-level reasoning to guide proof search.

Progol is an implementation of inductive logic programming that combines inverse entailment with general-to-specific search through a refinement graph.

<span class="mw-page-title-main">Allan M. Ramsay</span>

Allan M. Ramsay is a Professor of Formal Linguistics in the Department of Computer Science at the University of Manchester.

<span class="mw-page-title-main">Ross D. King</span> Professor at the University of Manchester

Ross Donald King is a Professor of Machine Intelligence at Chalmers University of Technology.

<span class="mw-page-title-main">Turing Institute</span> Scottish artificial intelligence laboratory

The Turing Institute was an artificial intelligence laboratory in Glasgow, Scotland, between 1983 and 1994. The company undertook basic and applied research, working directly with large companies across Europe, the United States and Japan developing software as well as providing training, consultancy and information services.

Inductive programming (IP) is a special area of automatic programming, covering research from artificial intelligence and programming, which addresses learning of typically declarative and often recursive programs from incomplete specifications, such as input/output examples or constraints.

This glossary of artificial intelligence is a list of definitions of terms and concepts relevant to the study of artificial intelligence (AI), its subdisciplines, and related fields. Related glossaries include Glossary of computer science, Glossary of robotics, and Glossary of machine vision.

<span class="mw-page-title-main">Thomas G. Dietterich</span> American computer scientist and academic

Thomas G. Dietterich is emeritus professor of computer science at Oregon State University. He is one of the pioneers of the field of machine learning. He served as executive editor of Machine Learning (journal) (1992–98) and helped co-found the Journal of Machine Learning Research. In response to the media's attention on the dangers of artificial intelligence, Dietterich has been quoted for an academic perspective to a broad range of media outlets including National Public Radio, Business Insider, Microsoft Research, CNET, and The Wall Street Journal.

Kristian Kersting is a German computer scientist. He is Professor of Artificial intelligence and Machine Learning at the Department of Computer Science at the Technische Universität Darmstadt, Head of the Artificial Intelligence and Machine Learning Lab (AIML) and Co-Director of hessian.AI, the Hessian Center for Artificial Intelligence.

Javier Andreu-Perez is a British computer scientist and a Senior Lecturer and Chair in Smart Health Technologies at the University of Essex. He is also associate editor-in-chief of Neurocomputing for the area of Deep Learning and Machine Learning. Andreu-Perez research is mainly focused on Human-Centered Artificial Intelligence (HCAI). He also chairs a interdisciplinary lab in this area, HCAI-Essex.

Deepak Kapur is a Distinguished Professor in the Department of Computer Science at the University of New Mexico.

Alessandra Russo is a professor in Applied Computational Logic at the Department of Computing, Imperial College London.

References

  1. ↑ http://www.raeng.org.uk/about/fellowship/fellowslist.htm List of Fellows of the Royal Academy of Engineering
  2. 1 2 Stephen Muggleton publications indexed by Google Scholar
  3. ↑ Stephen Muggleton at the Mathematics Genealogy Project
  4. ↑ "Professor Stephen H. Muggleton". Academic staff list. Imperial College. Retrieved 8 August 2010.
  5. 1 2 Stephen Muggleton at DBLP Bibliography Server OOjs UI icon edit-ltr-progressive.svg
  6. ↑ Grants awarded to Stephen Muggleton by the Engineering and Physical Sciences Research Council
  7. ↑ Stephen Muggleton's publications indexed by the Scopus bibliographic database. (subscription required)
  8. ↑ Srinivasan, A.; Muggleton, S.H.; Sternberg, M.J.E.; King, R.D. (1996). "Theories for mutagenicity: A study in first-order and feature-based induction". Artificial Intelligence. 85 (1–2): 277–299. doi:10.1016/0004-3702(95)00122-0. hdl: 10338.dmlcz/135595 .
  9. ↑ Stephen Muggleton author profile page at the ACM Digital Library
  10. ↑ Muggleton, Stephen (1987). Inductive acquisition of expert knowledge (PhD thesis). University of Edinburgh. hdl:1842/8124.
  11. ↑ Muggleton, S. (1997). "Learning from positive data". Inductive Logic Programming. Lecture Notes in Computer Science. Vol. 1314. pp. 358–376. doi:10.1007/3-540-63494-0_65. ISBN   978-3-540-63494-2. S2CID   18451163.
  12. ↑ Stephen Muggleton publications indexed by Microsoft Academic
  13. ↑ Muggleton, S. (1999). "Scientific knowledge discovery using inductive logic programming". Communications of the ACM. 42 (11): 42–46. doi: 10.1145/319382.319390 . S2CID   1013641.
  14. 1 2 "Prof Stephen Muggleton". The Royal Institution of Great Britain. Archived from the original on 25 June 2010. Retrieved 8 August 2010.
  15. ↑ Muggleton, S. (1991). "Inductive logic programming". New Generation Computing. 8 (4): 295–318. doi:10.1007/BF03037089. S2CID   5462416.
  16. ↑ Muggleton S.H. "Inductive Logic Programming", Academic Press, 1992.
  17. ↑ Muggleton, S. (1995). "Inverse entailment and progol". New Generation Computing. 13 (3–4): 245–286. CiteSeerX   10.1.1.31.1630 . doi:10.1007/BF03037227. S2CID   12643399.
  18. ↑ Muggleton, S.; De Raedt, L. (1994). "Inductive Logic Programming: Theory and methods". The Journal of Logic Programming. 19–20: 629–679. doi: 10.1016/0743-1066(94)90035-3 .
  19. ↑ Muggleton, S.; Page, D.; Srinivasan, A. (1997). "An initial experiment into stereochemistry-based drug design using inductive logic programming". Inductive Logic Programming. Lecture Notes in Computer Science. Vol. 1314. p. 23. doi:10.1007/3-540-63494-0_46. ISBN   978-3-540-63494-2.
  20. ↑ "Golem". AI Japanese Institute for Science. Retrieved 8 August 2010.
  21. 1 2 Muggleton, S. H.; Lin, D.; Tamaddoni-Nezhad, A. (2015). "Meta-interpretive learning of higher-order dyadic datalog: Predicate invention revisited". Machine Learning. 100: 49–73. doi: 10.1007/s10994-014-5471-y . hdl: 10044/1/23814 .
  22. ↑ King, R. D.; Whelan, K. E.; Jones, F. M.; Reiser, P. G. K.; Bryant, C. H.; Muggleton, S. H.; Kell, D. B.; Oliver, S. G. (2004). "Functional genomic hypothesis generation and experimentation by a robot scientist". Nature. 427 (6971): 247–252. Bibcode:2004Natur.427..247K. doi:10.1038/nature02236. PMID   14724639. S2CID   4428725.
  23. ↑ "What computing can teach biology, and vice versa". The Economist. 12 July 2007. Retrieved 8 August 2010.(subscription required)
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