@article{Finn+Muggleton+Page+Srinivasan/98/Discovery, AUTHOR = "P. Finn and S.H. Muggleton and D. Page and A. Srinivasan", TITLE = "Pharmacophore Discovery using the {I}nductive {L}ogic {P}rogramming system {Progol}", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/finnpharm.pdf", JOURNAL = "Machine Learning", VOLUME = "30", PAGES = "241--271", YEAR = "1998"} @article{bratmug:ilpapp, AUTHOR = "I. Bratko and S.H. Muggleton", TITLE = "Applications of {I}nductive {L}ogic {P}rogramming", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/cacm.pdf", YEAR = 1995, JOURNAL = "Communications of the ACM", VOLUME = 38, NUMBER = 11, PAGES = "65--70"} @article{cootes:mi18, TITLE = "Automatic determination of protein fold signatures from structured superposition", AUTHOR = "A.P. Cootes and S.H. Muggleton and R.B. Greaves and M.J. Sternberg", YEAR = 2001, JOURNAL = "Electronic Transactions in Artificial Intelligence", VOLUME = "6-B2", NUMBER = "026", MONTH = "November", PAGES = "245-274", URL = "http://www.ida.liu.se/ext/epa/cis/2001/026/tcover.html" } @article{kmuggs:drugs, AUTHOR = "R.D. King and S.H. Muggleton and R. Lewis and M.J.E. Sternberg", TITLE = "Drug design by machine learning: The use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase", JOURNAL = "Proceedings of the National Academy of Sciences", VOLUME = "89", NUMBER = "23", YEAR = 1992, PAGES = "11322--11326", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/pnas92.pdf"} @article{kmuggs:muta, AUTHOR = "R.D. King and S.H. Muggleton and A. Srinivasan and M.J.E. Sternberg", TITLE = "Structure-activity relationships derived by machine learning: the use of atoms and their bond connectives to predict mutagenicity by inductive logic programming", JOURNAL = "Proceedings of the National Academy of Sciences", VOLUME = "93", PAGES = "438--442", YEAR = 1996, URL = "http://www.doc.ic.ac.uk/\~shm/Papers/pnas96.pdf"} @article{kmuggs:robot, AUTHOR = "R.D. King and K.E. Whelan and F.M. Jones and P.K.G. Reiser and C.H. Bryant and S.H. Muggleton and D.B. Kell and S.G. Oliver", TITLE = "Functional genomic hypothesis generation and experimentation by a robot scientist", JOURNAL = "Nature", VOLUME = "427", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/Oliver_Jan15_hi.pdf", YEAR = 2004, PAGES = "247--252"} @article{mugg:ilp, TITLE = "Inductive {L}ogic {P}rogramming", AUTHOR = "S.H. Muggleton", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ilp.pdf", YEAR = "1991", JOURNAL = "New Generation Computing", VOLUME = 8, NUMBER = 4, PAGES = "295--318"} @article{muggks:proteins, AUTHOR = "S.H. Muggleton and R.D. King and M.J.E. Sternberg", TITLE = "Protein secondary structure prediction using logic-based machine learning", JOURNAL = "Protein Engineering", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/prot92.pdf", VOLUME = "5", NUMBER = "7", PAGES = "647--657", YEAR = 1992 } @article{mugg:predinv, AUTHOR = "S.H. Muggleton", TITLE = "Predicate Invention and Utilisation", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/util.pdf", YEAR = "1994", JOURNAL = "Journal of Experimental and Theoretical Artificial Intelligence", PUBLISHER = "Taylor \& Francis", VOLUME = 6, NUMBER = 1, PAGES = "127--130"} @article{mugg:sigart, AUTHOR = "S.H. Muggleton", TITLE = "Inductive Logic Programming: derivations, successes and shortcomings", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/sigart.pdf", JOURNAL = "SIGART Bulletin", PAGES = "5-11", VOLUME = "5", NUMBER = "1", YEAR = 1994 } @article{mugg:bttj, AUTHOR = "S.H. Muggleton and D. Michie", TITLE = "Machine intelligibility and the duality principle", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/bttj.pdf", JOURNAL = "British Telecom Technology Journal", PAGES = "15--23", VOLUME = "14", NUMBER = "4", YEAR = 1996} @article{srinmugg:aijmuta, AUTHOR = "A. Srinivasan and S.H. Muggleton and R.D. King and M.J.E. Sternberg", TITLE = "Theories for mutagenicity: a study of first-order and feature based induction", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ash_aij95.pdf", YEAR = "1996", JOURNAL = "Artificial Intelligence", VOLUME = "85", NUMBER = "1,2", PAGES = "277--299"} @article{mugg:der, AUTHOR = "S.H. Muggleton and L. De Raedt", TITLE = "Inductive Logic Programming: Theory and Methods", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/lpj.pdf", YEAR = "1994", JOURNAL = "Journal of Logic Programming", VOLUME = "19,20", PAGES = "629--679"} @article{mugg:progol, AUTHOR = "S.H. Muggleton", TITLE = "Inverse entailment and {P}rogol", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/InvEnt.pdf", YEAR = "1995", JOURNAL = "New Generation Computing", VOLUME = "13", PAGES = "245--286"} @article{mugg:slp, AUTHOR = "S.H. Muggleton", TITLE = "Stochastic Logic Programs", ABSTRACT = "One way to represent a machine learning algorithm's {\it bias} over the hypothesis and instance space is as a pair of probability distributions. This approach has been taken both within Bayesian learning schemes and the framework of U-learnability. However, it is not obvious how an Inductive Logic Programming (ILP) system should best be provided with a probability distribution. This paper extends the results of a previous paper by the author which introduced {\it stochastic logic programs} as a means of providing a structured definition of such a probability distribution. Stochastic logic programs are a generalisation of stochastic grammars. A stochastic logic program consists of a set of labelled clauses $p:C$ where $p$ is from the interval $[0,1]$ and $C$ is a range-restricted definite clause. A stochastic logic program $P$ has a distributional semantics, that is one which assigns a probability distribution to the atoms of each predicate in the Herbrand base of the clauses in $P$. These probabilities are assigned to atoms according to an SLD-resolution strategy which employs a stochastic selection rule. It is shown that the probabilities can be computed directly for {\it fail-free} logic programs and by normalisation for arbitrary logic programs. The stochastic proof strategy can be used to provide three distinct functions: 1) a method of sampling from the Herbrand base which can be used to provide selected targets or example sets for ILP experiments, 2) a measure of the information content of examples or hypotheses; this can be used to guide the search in an ILP system and 3) a simple method for conditioning a given stochastic logic program on samples of data. Functions 1) and 3) are used to measure the generality of hypotheses in the ILP system Progol4.2. This supports an implementation of a Bayesian technique for learning from positive examples only. ", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/slp.pdf", YEAR = 2001, JOURNAL = "Journal of Logic Programming", NOTE = "Accepted subject to revision"} @article{mugg:cacm2, TITLE = "Scientific Knowledge Discovery using {I}nductive {L}ogic {P}rogramming", AUTHOR = "S.H. Muggleton", ABSTRACT = "This paper is an overview of scientific knowledge discovery tasks carried out using Inductive Logic Programming (ILP). The results reviewed have been published in some of the top general science journals, and as such are among the strongest examples of semi-automated scientific discovery in the Artificial Intelligence literature. Space restrictions do not permit this paper to cover other discovery areas of ILP. These include the discovery of linguistic features in natural language data and the discovery of patterns in traffic data.", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/cacm2.pdf", MONTH = "November", YEAR = "1999", JOURNAL = "Communications of the ACM", VOLUME = "42", NUMBER = "11", PAGES = "42-46" } @article{stern:rschem, TITLE = "Modelling the structure and function of enzymes by machine learning", AUTHOR = "M.J.E. Sternberg and R. Lewis and R.D. King and S.H. Muggleton", YEAR = 1992, JOURNAL = "Proceedings of the Royal Society of Chemistry: Faraday Discussions", VOLUME = 93, PAGES = "269--280"} @article{stern:roysoc, TITLE = "Application of Machine Learning to Structural Molecular Biology", AUTHOR = "M.J.E. Sternberg and R.D. King and R. Lewis and S.H. Muggleton", YEAR = "1994", JOURNAL = "Philosophical Transactions of the Royal Society B", VOLUME = "344", PAGES = "365--371" } @article{sternmugg:campy, TITLE = "Gene Function Hypotheses for the Campylobacter jejuni Glycome Generated by a Logic-Based Approach", AUTHOR = "M.J.E. Sternberg and A. Tamaddoni-Nezhad and V.I.. Lesk and and E. Kay and P.G. Hitchen and A. Cootes and L.B. Alphen and M.P. Lamoureux and H.C. Jarrell and C.J. Rawlings and E.C. Soo and C.M. Szymanski and A. Dell and B.W. Wren and S.H. Muggleton", YEAR = "2013", VOLUME = "425", NUMBER = "1", PAGES = "186-197", JOURNAL = "Journal of Moleular Biology", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/campy.pdf", } @article{mugg:aij99, AUTHOR = "S.H. Muggleton", TITLE = "Inductive Logic Programming: issues, results and the {LLL} challenge", ABSTRACT = "Inductive Logic Programming (ILP) is the area of AI which deals with the induction of hypothesised predicate definitions from examples and background knowledge. Logic programs are used as a single representation for examples, background knowledge and hypotheses. ILP is differentiated from most other forms of Machine Learning (ML) both by its use of an expressive representation language and its ability to make use of logically encoded background knowledge. This has allowed successful applications of ILP in areas such as molecular biology and natural language which both have rich sources of background knowledge and both benefit from the use of an expressive concept representation languages. For instance, the ILP system Progol has recently been used to generate comprehensible descriptions of the 23 most populated fold classes of proteins, where no such descriptions had previously been formulated manually. In the natural language area ILP has not only been shown to have higher accuracies than various other ML approaches in learning the past tense of English but also shown to be capable of learning accurate grammars which translate sentences into deductive database queries. The area of Learning Language in Logic (LLL) is producing a number of challenges to existing ILP theory and implementations. In particular, language applications of ILP require revision and extension of a hierarchically defined set of predicates in which the examples are typically only provided for predicates at the top of the hierarchy. New predicates often need to be invented, and complex recursion is usually involved. Advances in ILP theory and implementation related to the challenges of LLL are already producing beneficial advances in other sequence-oriented applications of ILP. In addition LLL is starting to develop its own character as a sub-discipline of AI involving the confluence of computational linguistics, machine learning and logic programming.", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/aij99.pdf", JOURNAL = "Artificial Intelligence", VOLUME = "114", NUMBER = "1--2", MONTH = "December", YEAR = "1999", PAGES = "283--296"} @article{mugg:mljfolds, AUTHOR = "M. Turcotte and S.H. Muggleton and M.J.E. Sternberg", TITLE = "The Effect of Relational Background Knowledge on Learning of Protein Three-Dimensional Fold Signatures", ABSTRACT = "As a form of Machine Learning the study of Inductive Logic Programming (ILP) is motivated by a central belief: relational description languages are better (in terms of accuracy and understandability) than propositional ones for certain real-world applications. This claim is investigated here for a particular application in structural molecular biology, that of constructing readable descriptions of the major protein folds. To the authors' knowledge Machine Learning has not previously been applied systematically to this task. In this application domain the domain expert (thord author) identified a natural divide between essentially propositional features and more structurally-orientated relational ones. The following null hypotheses are tested: 1) for a given ILP system (Progol) provision of relational background knowledge does not increase predictive accuracy, 2) a good propositional learning system (C5.0) without relational background knowledge will outperform Progol with relational background knowledge, 3) relational background knowledge does not produce improved explanatory insight. Null hypotheses 1) and 2) are both refuted on cross-validation results carried out over 20 of the most populated protein folds. Hypothesis 3 is refuted by demonstration of various insightful rules discovered only in the relationally-oriented learned rules.", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ml2000.pdf", JOURNAL = "Machine Learning", MONTH = "April-May", VOLUME = "1,2", PAGES = "81--96", YEAR = "2001" } @article{turc:jmbfolds, AUTHOR = "M. Turcotte and S.H. Muggleton and M.J.E. Sternberg", TITLE = "Automated Discovery of Structural Signatures of Protein Fold and Function", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/foldsjmb.pdf", JOURNAL = "Journal of Molecular Biology", VOLUME = "306", PAGES = "591--605", YEAR = "2001" } @article{mugg:statilp, AUTHOR = "S.H. Muggleton", TITLE = "Statistical Aspects of Logic-Based Machine Learning", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/statilp.pdf", JOURNAL = "ACM Transactions on Computational Logic", YEAR = "2001", NOTE = "Under revision"} @article{turcotte:genfold, AUTHOR = "M. Turcotte and S.H. Muggleton and M.J. Sternberg", TITLE = "Generating protein three-dimensional fold signatures using inductive logic programming", JOURNAL = "Computational Chemistry", VOLUME = "26", PAGES = "57--64", YEAR = "2001" } @article{bryant:jcb, TITLE = "Are grammatical representations useful for learning from biological sequence data? - a case study", AUTHOR = "S.H. Muggleton and C.H. Bryant and A. Srinivasan and A. Whittaker and S. Topp and C. Rawlings", YEAR = 2001, JOURNAL = "Journal of Computational Biology", VOLUME = 8, NUMBER = 5, PAGES = "493-521", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/bryantgram.pdf", ABSTRACT = "This paper investigates whether Chomsky-like grammar representations are useful for learning cost-effective, comprehensible predictors of members of biological sequence families. The Inductive Logic Programming (ILP) Bayesian approach to learning from positive examples is used to generate a grammar for recognising a class of proteins known as human neuropeptide precursors (NPPs). Collectively, five of the co-authors of this paper, have extensive expertise on NPPs and general bioinformatics methods. Their motivation for generating a NPP grammar was that none of the existing bioinformatics methods could provide sufficient cost-savings during the search for new NPPs. Prior to this project experienced specialists at SmithKline Beecham had tried for many months to hand-code such a grammar but without success. Our best predictor makes the search for novel NPPs {\bf more than 100 times more efficient} than randomly selecting proteins for synthesis and testing them for biological activity. As far as these authors are aware, this is both the first biological grammar learnt using ILP and the first real-world scientific application of the ILP Bayesian approach to learning from positive examples. A group of features is derived from this grammar. Other groups of features of NPPs are derived using other learning strategies. Amalgams of these groups are formed. A recognition model is generated for each amalgam using C4.5 and C4.5rules and its performance is measured using both predictive accuracy and a new cost function, {\em Relative Advantage} ($RA$). The highest $RA$ was achieved by a model which includes grammar-derived features. This $RA$ is significantly higher than the best $RA$ achieved without the use of the grammar-derived features. Predictive accuracy is not a good measure of performance for this domain because it does not discriminate well between NPP recognition models: despite covering varying numbers of (the rare) positives, all the models are awarded a similar (high) score by predictive accuracy because they all exclude most of the abundant negatives."} @article{mugg:mi17:slplearn, TITLE = "Learning Stochastic Logic Programs", AUTHOR = "S.H. Muggleton", YEAR = 2000, JOURNAL = "Electronic Transactions in Artificial Intelligence", VOLUME = 4, NUMBER = "041", URL = "http://www.ida.liu.se/ext/epa/cis/2000/041/tcover.html" } @article{bryant:mi18, TITLE = "Combining Inductive Logic Programming, Active Learning and Robotics to Discover the Function of Genes", AUTHOR = "C.H. Bryant and S.H. Muggleton and S.G. Oliver and D.B. Kell and P. Reiser and R.D. King", YEAR = 2001, JOURNAL = "Electronic Transactions in Artificial Intelligence", VOLUME = "5-B1", NUMBER = "012", MONTH = "November", PAGES = "1--36", URL = "http://www.ida.liu.se/ext/epa/cis/2001/012/tcover.html" } @article{reiser:mi18, TITLE = "Developing a Logical Model of Yeast Metabolism", AUTHOR = "P.G.K. Reiser and R.D. King and D.B. Kell and S.H. Muggleton and C.H. Bryant and S.G. Oliver", YEAR = 2001, JOURNAL = "Electronic Transactions in Artificial Intelligence", VOLUME = "5-B2", NUMBER = "024", MONTH = "November", PAGES = "223--244", URL = "http://www.ida.liu.se/ext/epa/cis/2001/024/tcover.html" } @article{mugg:mi19:slplearn, TITLE = "Learning Structure and Parameters of Stochastic Logic Programs", AUTHOR = "S.H. Muggleton", YEAR = 2002, JOURNAL = "Electronic Transactions in Artificial Intelligence", VOLUME = 6, URL = "http://www.ida.liu.se/ext/etai/received/machi/mi19.html" } @article{angmugg:mi19:metabolism, TITLE = "Machine Learning Metabolic Pathway descriptions using a Probabilistic Relational Representation", AUTHOR = "N. Angelopoulos and S.H. Muggleton", YEAR = 2002, JOURNAL = "Electronic Transactions in Artificial Intelligence", VOLUME = 6, URL = "http://www.ida.liu.se/ext/etai/received/machi/mi19.html" } @article{watmugg:mi19:stochact, TITLE = "First-order Stochastic Action Languages", AUTHOR = "H. Watanabe and S.H. Muggleton", YEAR = 2002, JOURNAL = "Electronic Transactions in Artificial Intelligence", VOLUME = 6, URL = "http://www.ida.liu.se/ext/etai/received/machi/mi19.html" } @article{cootes:jmbfolds, AUTHOR = "A. Cootes and S.H. Muggleton and M.J.E. Sternberg", TITLE = "The automatic discovery of structural principles describing protein fold space", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/cootesfoldjmb.pdf", JOURNAL = "Journal of Molecular Biology", VOLUME = 330, NUMBER = 4, PAGES = "839--850", YEAR = "2003" } @article{cootes:jmbnetworks, AUTHOR = "A. Cootes and S.H. Muggleton and M.J.E. Sternberg", TITLE = "The Identification of Similarities between Biological Networks: Application to the Metabolome and Interactome", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/cootesjmbnets.pdf", JOURNAL = "Journal of Molecular Biology", VOLUME = 369, PAGES = "1126--1139", YEAR = "2003" } @article{sternmugg:qsar, AUTHOR = "M.J.E. Sternberg and S.H. Muggleton", TITLE = "Structure Activity Relationships ({SAR}) and Pharmacophore Discovery Using Inductive Logic Programming ({ILP})", JOURNAL = "QSAR and Combinatorial Science", VOLUME = "22", NUMBER = "5", PAGES = "527--532", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/qsarilp.pdf", YEAR = "2003" } @article{mugg:nature, AUTHOR = "S.H. Muggleton", TITLE = "Exceeding Human Limits", JOURNAL = "Nature", VOLUME = "440", NUMBER = "7083", PAGES = "409--410", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/NatureCommentaryFinal.pdf", YEAR = "2006" } @article{coltmugg:mlj, AUTHOR = "S. Colton and S.H. Muggleton", TITLE = "Mathematical Applications of Inductive Logic Programming", JOURNAL = "Machine Learning", VOLUME = "64", NUMBERS = "1-3", PAGES = "25--64", NOTE = " DOI: 10.1007/s10994-006-8259-x", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/mathsilp.pdf", YEAR = "2006" } @article{alimugg:mljmetabduce, AUTHOR = "A. Tamaddoni-Nezhad and R. Chaleil and A. Kakas and S.H. Muggleton", TITLE = "Application of abductive {ILP} to learning metabolic network inhibition from temporal data", JOURNAL = "Machine Learning", VOLUME = "64", NUMBERS = "1-3", PAGES = "209-230", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/mljmetabduce.pdf", YEAR = "2006" } @article{alimugg:ieeemetabduce, AUTHOR = "A. Tamaddoni-Nezhad and R. Chaleil and A. Kakas and M.J.E. Sternberg and J. Nicholson and S.H. Muggleton", TITLE = "Modeling the Effects of Toxins in Metabolic Networks", JOURNAL = "IEEE Engineering in Medicine and Biology", VOLUME = "26", NUMBERS = "2", PAGES = "37-46", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ieeemetabduce.pdf", YEAR = "2007" } @article{cannon:svilpapp, AUTHOR = "E.O. Cannon and A. Amini and A. Bender and M. J. E. Sternberg and S.H. Muggleton and R.C. Glen and J.B.O. Mitchell", TITLE = "Support vector inductive logic programming outperforms the naive {B}ayes classifier and inductive logic programming for the classification of bioactive chemical compounds", JOURNAL = "Journal of Computer Aided Molecular Design", VOLUME = "21", NUMBERS = "5", PAGES = "269-280", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/cannonsvilp.pdf", YEAR = "2007" } @article{amini:svilpapp, AUTHOR = "A. Amini and S.H. Muggleton, H. Lodhi and M.J.E. Sternberg", TITLE = "A Novel Logic-Based Approach for Quantitative Toxicology Prediction", JOURNAL = "Journal of Chemical Information and Modelling", VOLUME = "47", NUMBER = "3", PAGES = "998-1006", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/aminisvilpapp.pdf", YEAR = "2007" } @article{cootes:networks, AUTHOR = "A.P. Cootes and S.H. Muggleton and M.J.E. Sternberg", TITLE = "The Identification of Similarities between Biological Networks: Application to the Metabolome and Interactome Prediction", JOURNAL = "Journal of Molecular Biology", VOLUME = "369", NUMBER = "4", PAGES = "1126--1139", NOTE = "DOI: 10.1016/j.jmb.2007.03.013", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/cootesnet.pdf", YEAR = "2007" } @article{shrimpton:docksvilp, AUTHOR = "A. Amini and P.J. Shrimpton and S.H. Muggleton and M.J.E. Sternberg", TITLE = "A general approach for developing system-specific functions to score protein-ligand docked complexes using support vector inductive logic programming", JOURNAL = "Proteins", VOLUME = "69", NUMBER = "4", PAGES = "823-831", NOTE = "DOI: 10.1002/prot.21782", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/svilp_proteins.pdf", YEAR = "2007" } @article{mugali:qgga, AUTHOR = "S.H. Muggleton and A. Tamaddoni-Nezhad", TITLE = "{QG/GA}: A Stochastic Search for {P}rogol", JOURNAL = "Machine Learning", VOLUME = "70", NUMBER = "2--3", PAGES = "123--133", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/qgga_mlj1.pdf", YEAR = "2007" } @article{mugkaz:scaffolds, AUTHOR = "K. Tsunoyama and A. Amini and M.J.E. Sternberg and S.H. Muggleton", TITLE = "Scaffold Hopping in Drug Discovery Using Inductive Logic Programming", JOURNAL = "Journal of Chemical Information and Modelling", VOLUME = "48", NUMBER = "5", PAGES = "949--957", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/Scaffolds.pdf", YEAR = "2008" } @article{chenmugg:probexs, AUTHOR = "J. Chen and S.H. Muggleton and J. Santos", TITLE = "Learning probabilistic logic models from probabilistic examples", JOURNAL = "Machine Learning", VOLUME = "73", NUMBER = "1", PAGES = "55--85", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/probexs.pdf", YEAR = "2008" } @article{dietmugg:strucml, AUTHOR = "T. Dietterich and P. Domingos and L. Getoor and S.H. Muggleton and P. Tadepalli", TITLE = " Structured machine learning: the next ten years", JOURNAL = "Machine Learning", VOLUME = "73", NUMBER = "1", PAGES = "3--23", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/strucml.pdf", YEAR = "2008" } @article{bang:bayesnets, AUTHOR = "J-W. Bang and D.J. Crockford and E. Holmes and F. Pazos and M.J.E. Sternberg and S.H. Muggleton and J.K. Nicholson", TITLE = "Integrative top-down system metabolic modeling in experimental disease states via data-driven Bayesian methods", JOURNAL = "Journal of Proteome Research", VOLUME = "7", NUMBER = "2", PAGES = "497--503", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/BN-Metalog_JPR_08.pdf", YEAR = "2008" } @article{alimugg:lattice, AUTHOR = "A. Tamaddoni-Nezhad and S.H. Muggleton", TITLE = "The lattice structure and refinement operators for the hypothesis space bounded by a bottom clause ", JOURNAL = "Machine Learning", VOLUME = "76", NUMBER = "1", PAGES = "37--72", NOTE = "10.1007/s10994-009-5117-7", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/lattref.pdf", YEAR = "2009" } @article{lodhi:multisvilp, AUTHOR = "H. Lodhi and S.H. Muggleton and M.J.E. Sternberg", TITLE = "Multi-class protein fold recognition using large margin logic based divide and conquer learning", JOURNAL = "SIGKDD Exploration", VOLUME = "11", NUMBER = "2", PAGES = "117-122", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/multisvilp.pdf", YEAR = "2010" } @article{lodhi:dssmulti, AUTHOR = "H. Lodhi and S.H. Muggleton and M.J.E. Sternberg", TITLE = "Multi-class mode of action classification of toxic compounds using logic based kernel methods", JOURNAL = "Molecular Informatics", PAGES = "655--664", YEAR = "2010" } @article{mugg:ILPturns20, AUTHOR = "S.H. Muggleton and L. De Raedt and D. Poole and I. Bratko and P. Flach and K. Inoue", TITLE = "{ILP} turns 20: biography and future challenges", JOURNAL = "Machine Learning", VOLUME = "86", NUMBER = "1", PAGES = "3--23", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ILPturns20.pdf", YEAR = "2011" } @article{lesk:wibl, AUTHOR = "V. Lesk and J. Taubert and C. Rawlings and S. Dunbarand S.H. Muggleton", TITLE = "{WIBL}: Workbench for Integrative Biological Learning", JOURNAL = "Journal of Integrative Bioinformatics", VOLUME = "8", NUMBER = "2", YEAR = "2011", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/wibl2011.pdf", DOI = "10.2390/biecoll-jib-2011-156"} @article{mugg:ecoPlosOne, AUTHOR = "D.A. Bohan and G. Caron-Lormier and S.H. Muggleton and A. Raybould and A. Tamaddoni-Nezhad", TITLE = "Automated Discovery of Food Webs from Ecological Data using Logic-based Machine Learning", JOURNAL = "PloS ONE", VOLUME = "6", NUMBER = "12", YEAR = "2011", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ecoilp_plosone.pdf", DOI = "10.1371/journal.pone.0029028"} @article{reynoldsmugs:inddex, AUTHOR = "C.R. Reynolds and S.H. Muggleton and M.J.E. Sternberg", TITLE = "Assessment of a rule-based virtual screening technology (INDDEx) on a benchmark data set", JOURNAL = "The Journal of Physical Chemistry B", VOLUME = "116", NUMBER = "23", YEAR = "2012", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/inddex.pdf"} @article{santosmugg:hexose, AUTHOR = "J.C.A. Santos and H. Nassif and C.D. Page, S.H. Muggleton M.J.E. Sternberg ", TITLE = "Automated identification of protein-ligand interaction features using Inductive Logic Programming: A hexose binding case study", JOURNAL = "BMC Bioinformatics", VOLUME = "13", NUMBER = "162", YEAR = "2012", DOI = "10.1186/1471-2105-13-162", URL = "http://www.biomedcentral.com/1471-2105/13/162/abstract"} @article{mugg:turingai, AUTHOR = "S. H. Muggleton", TITLE = "Alan {T}uring and the development of {A}rtificial {I}ntelligence", JOURNAL = "AI Communications", YEAR = "2014", VOLUME = "27", NUMBER = "1", PAGES= "3--10", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/TuringAI_1.pdf"} @article{mugg:metalearn, AUTHOR = "S.H. Muggleton and D. Lin and N. Pahlavi and A. Tamaddoni-Nezhad", TITLE = "Meta-interpretive Learning: application to Grammatical Inference", YEAR = 2014, JOURNAL = "Machine Learning", VOLUME = "94", PAGES = "25--49", DOI = "10.1007/s10994-013-5358-3", URL = "https://link.springer.com/article/10.1007/s10994-013-5358-3" } @article{alireza:aecr49, TITLE = {Construction and Validation of Food-webs using Logic-based Machine Learning and Text-mining}, AUTHOR = {A. Tamaddoni-Nezhad and G. Afroozi Milani and A. Raybould and S. Muggleton and D.Bohan}, YEAR = 2013, JOURNAL = {Advances in Ecological Research}, VOLUME = 49, PAGES = "225--289"} @article{mugetal:indprogreal, AUTHOR = "S. Gulwani and J. Hernandez-Orallo and E. Kitzelmann and S.H. Muggleton and U. Schmid and B. Zorn", TITLE = "Inductive Programming Meets the Real World", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/indprogreal.pdf", YEAR = 2015, JOURNAL = "Communications of the ACM", VOLUME = "58", NUMBER = "11", PAGES = "90--99"} @article{mugg:metagolD:mlj, AUTHOR = "S.H. Muggleton and D. Lin and A. Tamaddoni-Nezhad", TITLE = "Meta-Interpretive Learning of Higher-Order Dyadic Datalog: Predicate Invention revisited", URL = "https://link.springer.com/article/10.1007/s10994-014-5471-y", YEAR = 2015, JOURNAL = "Machine Learning", VOLUME = "100", NUMBER = "1", PAGES = "49--73"} @article{reynolds:virtual, AUTHOR = "C.R. Reynolds and S.H. Muggleton and M.J.E. Sternberg", TITLE = "Incorporating Virtual Reactions into a Logic-Based Ligand-Based Virtual Screening Method to Discover New Leads", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/reynvirtual.pdf", YEAR = 2015, JOURNAL = "Molecular Informatics", NOTE = "DOI: 10.1002/minf.201400162" } @article{mugg:logvismlj, AUTHOR = "S.H. Muggleton and W-Z. Dai and C. Sammut and A. Tamaddoni-Nezhad and J. Wen and Z-H. Zhou", TITLE = "Meta-Interpretive Learning from noisy images", URL = "http://link.springer.com/article/10.1007/s10994-018-5710-8", YEAR = 2018, JOURNAL = "Machine Learning", VOLUME = "107", ISSUE = "7", PAGES = "1097--1118" } @article{mugg:compmlj, AUTHOR = "S.H. Muggleton and U. Schmid and C. Zeller and A. Tamaddoni-Nezhad and T. Besold", TITLE = "Ultra-Strong Machine Learning - Comprehensibility of Programs Learned with {ILP}", URL = "http://link.springer.com/article/10.1007/s10994-018-5707-3", YEAR = 2018, JOURNAL = "Machine Learning", VOLUME = "107", ISSUE = "7", PAGES = "1119--1140" } @article{cropmugg:metaopt, AUTHOR = "A. Cropper and S.H. Muggleton", TITLE = "Learning efficient logic programs", YEAR = 2019, JOURNAL = "Machine Learning", VOLUME = "108", ISSUE = "7", PAGES = "1063--1083", URL = "http://link.springer.com/article/10.1007/s10994-018-5712-6"} @article{mugghoq:newgengamestrat, AUTHOR = "S.H. Muggleton and C. Hocquette", TITLE = "Machine Discovery of Comprehensible Strategies for Simple Games Using Meta-Interpretive Learning", JOURNAL = "New Generation Computing", YEAR = 2019, VOLUME = "37", PAGES = "203--217", URL = "https://doi.org/10.1007/s00354-019-00054-2"} @article{cropmug:learnholp, AUTHOR = "A. Cropper and R. Morel and S.H. Muggleton", TITLE = "Learning higher-order logic programs", JOURNAL = "Machine Learning", VOLUME = "109", PAGES = "1289--1322", YEAR = 2020, URL = "https://link.springer.com/article/10.1007/s10994-019-05862-7"} @article{lunmug:mljharmexp, AUTHOR = "L. Ai and S.H. Muggleton and C. Hocquette and M. Gromowski and U. Schmid", TITLE = "Beneficial and Harmful Explanatory Machine Learning", JOURNAL = "Machine Learning", VOLUME = "110", PAGES = "695-721", YEAR = 2021, URL = "https://link.springer.com/article/10.1007/s10994-020-05941-0"} @article{lunmug:bmlp, AUTHOR = "L. Ai and S.H. Muggleton and S-S. Liang and G. Baldwin", TITLE = "Boolean matrix logic programming for active learning of gene functions in genome-scale metabolic network models", JOURNAL = "Machine Learning", VOLUME = "114,254", YEAR = 2025, URL = "https://doi.org/10.1007/s10994-025-06868-0"} @article{patmug:topprog, AUTHOR = "S. Patsantzis and S.H. Muggleton", TITLE = "Top Program Construction and Reduction for polynomial time Meta-Interpretive Learning", JOURNAL = "Machine Learning", VOLUME = 110, PAGES = "755-778", YEAR = 2021, URL = "https://link.springer.com/article/10.1007/s10994-020-05945-w" } @article{cropmug:ilp30mlj, AUTHOR = "A. Cropper and S. Duman\u{c}i\'{c} and Richard Evans and S.H. Muggleton", TITLE = "{I}nductive {L}ogic {P}rogramming at 30", JOURNAL = "Machine Learning", VOLUME = "111", PAGES = "147--172", YEAR = 2021, URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ilp30.pdf" } @article{patmug:metaspec, AUTHOR = "S. Patsantzis and S.H. Muggleton", TITLE = "Meta-Interpretive Learning as Metarule Specialisation", JOURNAL = "Machine Learning", VOLUME = "111", PAGES = "3703--3731", YEAR = 2022, URL = "https://link.springer.com/article/10.1007/s10994-022-06156-1"} @article{mugg:hypalg1ex, AUTHOR = "S.H. Muggleton", TITLE = "Hypothesising an Algorithm from One Example: the Role of Specificity", JOURNAL = "Philosophical Transaction of the Royal Society A", YEAR = 2023, VOLUME = "381:20220046", URL = "https://royalsocietypublishing.org/doi/10.1098/rsta.2022.0046"} @article{lunmug:SeqTeach, AUTHOR = "L. Ai and J. Langer and S.H. Muggleton and U. Schmid", TITLE = "Explanatory machine learning for sequential human teaching", JOURNAL = "Machine Learning", YEAR = 2023, VOLUME = 112, PAGES = "3591--3632", URL = "https://link.springer.com/article/10.1007/s10994-023-06351-8" } @inproceedings{mugg:fgcsilp, AUTHOR = "S.H. Muggleton", TITLE = "Developments in {I}nductive {L}ogic {P}rogramming", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/fgcs.pdf", YEAR = 1992, BOOKTITLE= "Proceedings of the International Conference on Fifth Generation Computer Systems 1992", PAGES = "1071--1073", PUBLISHER = "Ohmsha", ADDRESS= "Tokyo"} @inproceedings{bratmugg:utube, AUTHOR = "I. Bratko and S.H. Muggleton and A. Varsek", TITLE = "Learning Qualitative Models of Dynamic Systems", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ewsl91.pdf", YEAR = 1991, BOOKTITLE= "Proceedings of the Eighth International Machine Learning Workshop", PUBLISHER = "Morgan-Kaufmann", ADDRESS= "San Mateo, Ca"} @inproceedings{srinmug:mutacompare, AUTHOR = "A. Srinivasan and S.H. Muggleton and and R.D. King", TITLE = "Comparing the use of background knowledge by inductive logic programming systems", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/mutacompare.pdf", EDITOR = "L. De Raedt", YEAR = "1995", BOOKTITLE = "Proceedings of the Fifth International Inductive Logic Programming Workshop", PUBLISHER = "Katholieke Universteit Leuven" } InProceedingsaedtPagMugSri97:ECML97, author = "James Cussens and David Page and Stephen Muggleton and Ashwin Srinivasan", title = "Using {Inductive Logic Programming} for {Natural Logic Processing}", url = "http://www.doc.ic.ac.uk/\~shm/Papers/ecml97mlnet.pdf", editor = "W. Daelemans and T. Weijters and A. van der Bosch", pages = "25--34", booktitle = "{ECML'97} -- Workshop Notes on Empirical Learning of Natural Language Tasks", year = 1997, publisher = "University of Economics", address = "Prague", note = "Invited keynote paper" } @inproceedings{dze:mug:rus:constrained, AUTHOR = "S. D\v{z}eroski and S.H. Muggleton and S. Russell", TITLE = "Learnability of Constrained Logic Programs", YEAR = 1993, BOOKTITLE = "Proceedings of the European Conference on Machine Learning ", PAGES = "342--347", PUBLISHER = "Springer-Verlag", ADDRESS = "London, UK"} @inproceedings{dze:mug:rus, AUTHOR = "S. D\v{z}eroski and S.H. Muggleton and S. Russell", TITLE = "{PAC}-learnability of determinate logic programs", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/colt92.pdf", PAGES = "128--135", YEAR = 1992, BOOKTITLE = "Proceedings of the 5th {ACM} Workshop on Computational Learning Theory", PUBLISHER = "ACM Press", ADDRESS = "New York, NY"} @inproceedings{cao:hlgg, AUTHOR = "C. Feng and S.H. Muggleton", TITLE = "Towards inductive generalisation in higher order logic", YEAR = 1992, PAGES = "154--162", BOOKTITLE = "Proceedings of the Ninth International Workshop on Machine Learning", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/holgg.pdf", EDITOR = "D. Sleeman and P. Edwards", PUBLISHER = "Morgan Kaufmann", ADDRESS = "San Mateo, CA"} @inproceedings{rulemaster, AUTHOR = "D. Michie and S.H. Muggleton and C. Riese and S. Zubrick", TITLE = "RuleMaster: a second-generation knowledge-engineering facility", YEAR = 1984, PAGES = "591--597", BOOKTITLE = "Proceedings of the First Conference on Artificial Intelligence Applications", PUBLISHER = "IEEE Computer Soc."} @inproceedings{mug:duce, AUTHOR = "S.H. Muggleton", TITLE = "Duce, an oracle based approach to constructive induction", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ijcai87.pdf", BOOKTITLE = "IJCAI-87", YEAR = 1987, PUBLISHER = "Kaufmann", PAGES = "287--292"} @inproceedings{cigol:mugbun, AUTHOR = "S.H. Muggleton and W. Buntine", TITLE = "Machine invention of first-order predicates by inverting resolution", BOOKTITLE = "Proceedings of the 5th International Conference on Machine Learning", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/cigol.pdf", YEAR = 1988, PUBLISHER = "Kaufmann", PAGES = "339--352"} @inproceedings{algcomp:mug, AUTHOR = "S.H. Muggleton", TITLE = "A strategy for constructing new predicates in first order logic", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ewsl88.pdf", BOOKTITLE = "Proceedings of the Third European Working Session on Learning", YEAR = 1988, PUBLISHER = "Pitman", PAGES = "123--130"} @inproceedings{mugfeng:golem, AUTHOR = "S.H. Muggleton and C. Feng", TITLE = "Efficient induction of logic programs", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/alt90.pdf", BOOKTITLE = "Proceedings of the First Conference on Algorithmic Learning Theory", PAGES = "368--381", YEAR = 1990, PUBLISHER = "Ohmsha", ADDRESS = "Tokyo" } @inproceedings{expform:cigol, AUTHOR = "S.H. Muggleton and M.E. Bain and J. Hayes-Michie and D. Michie", TITLE = "An experimental comparison of human and machine learning formalisms", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ml6paper.pdf", BOOKTITLE = "Proceedings of the Sixth International Workshop on Machine Learning", YEAR = 1989, PUBLISHER = "Kaufmann", ADDRESS = "Los Altos, CA"} @inproceedings{mug:co.pdfig, AUTHOR = "S.H. Muggleton and A. Srinivasan and M. Bain", TITLE = "Compression, significance and accuracy", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/compsig.pdf", YEAR = 1992, BOOKTITLE = "Proceedings of the Ninth International Machine Learning Conference", PAGES = "338--347", EDITOR = "D. Sleeman and P. Edwards", PUBLISHER = "Morgan-Kaufmann", ADDRESS = "San Mateo, CA"} @inproceedings{mugg:invimp, TITLE = "Inverting Implication", AUTHOR = "S.H. Muggleton", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/invimp.pdf", BOOKTITLE = "Proceedings of the Second Inductive Logic Programming Workshop", YEAR = 1992, PUBLISHER = "ICOT (Technical report TM-1182)", PAGES = "19--39", ADDRESS = "Tokyo"} @InProceedings(Muggleton93:proc, Author = "Muggleton, S.H.", Title = "Optimal layered learning: {A} {PAC} approach to incremental sampling", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/layered.pdf", Booktitle = "Proceedings of the 4th Conference on Algorithmic Learning Theory", Editor = "K. Jantke and S. Kobayashi and E. Tomita and T. Yokomori", Publisher = "Springer-Verlag", Series = "LNAI 744", Year = 1993, Pages = "37-44" ) @inproceedings{mugg:bayesml, AUTHOR = "S.H. Muggleton", TITLE = "Bayesian {I}nductive {L}ogic {P}rogramming", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/bayesian.pdf", YEAR = 1994, BOOKTITLE = "Proceedings of the Eleventh International Machine Learning Conference", PAGES = "371--379", EDITOR = "W. Cohen and H. Hirsh", PUBLISHER = "Morgan-Kaufmann", NOTE = "Keynote presentation", ADDRESS = "San Mateo, CA"} @inproceedings{mugg:bayescolt, AUTHOR = "S.H. Muggleton", TITLE = "Bayesian {I}nductive {L}ogic {P}rogramming", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/bayesian.pdf", YEAR = 1994, BOOKTITLE = "Proceedings of the Seventh Annual ACM Conference on Computational Learning Theory", PAGES = "3--11", EDITOR = "M. Warmuth", NOTE = "Keynote presentation", PUBLISHER = "ACM Press", ADDRESS = "New York"} @incollection{mugg:muta, AUTHOR = "A. Srinivasan and S.H. Muggleton and R.D. King and M.J.E. Sternberg", TITLE = "Mutagenesis: {ILP} experiments in a non-determinate biological domain", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ilp94.pdf", YEAR = 1994, EDITOR = "S. Wrobel", BOOKTITLE = "Proceedings of the Fourth International Inductive Logic Programming Workshop", PUBLISHER = "Gesellschaft fur Mathematik und Datenverarbeitung MBH", NOTE = "GMD-Studien Nr 237"} @incollection{mugg:slp0, AUTHOR = "S.H. Muggleton", TITLE = "Stochastic logic programs", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/slp.pdf", YEAR = 1996, EDITOR = "L. de Raedt", PAGES = "254--264", BOOKTITLE = "Advances in Inductive Logic Programming", PUBLISHER = "IOS Press" } @incollection{srin:carc, AUTHOR = "A. Srinivasan and and R.D. King S.H. Muggleton and M.J.E. Sternberg", TITLE = "Carcinogenesis predictions using {ILP}", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ilp97a.pdf", YEAR = 1997, EDITOR = "N. Lavra\v{c} and S. D\v{z}eroski", PAGES = "273--287", BOOKTITLE = "Proceedings of the Seventh International Workshop on Inductive Logic Programming", PUBLISHER = "Springer-Verlag", NOTE = "LNAI 1297", ADDRESS = "Berlin"} @incollection{mugg:ecai98, AUTHOR = "S.H. Muggleton", TITLE = "Inductive Logic Programming: issues, results and the {LLL} challenge", ABSTRACT = "Inductive Logic Programming (ILP) \cite{mugg:ilp,mugg:der} is the area of AI which deals with the induction of hypothesised predicate definitions from examples and background knowledge. Logic programs are used as a single representation for examples, background knowledge and hypotheses. ILP is differentiated from most other forms of Machine Learning (ML) both by its use of an expressive representation language and its ability to make use of logically encoded background knowledge. This has allowed successful applications of ILP \cite{bratmug:ilpapp} in areas such as molecular biology \cite{stern:roysoc,muggks:proteins,kmuggs:muta, Finn+Muggleton+Page+Srinivasan/98/Discovery} and natural language \cite{mooney:nlp,CusPagMugSri97:ECML97,Cus97-ILP97} which both have rich sources of background knowledge and both benefit from the use of an expressive concept representation languages. For instance, the ILP system Progol has recently been used to generate comprehensible descriptions of the 23 most populated fold classes of proteins \cite{turcotte:folds}, where no such descriptions had previously been formulated manually. In the natural language area ILP has not only been shown to have higher accuracies than various other ML approaches in learning the past tense of English \cite{mooney:foidl} but also shown to be capable of learning accurate grammars which translate sentences into deductive database queries \cite{zelle:semantics}. In both cases, follow up studies \cite{tho.pdfon:semantics,dzer:nominal} have shown that these ILP approaches to natural language problems extend with relative ease to various languages other than English. The area of Learning Language in Logic (LLL) is producing a number of challenges to existing ILP theory and implementations. In particular, language applications of ILP require revision and extension of a hierarchically defined set of predicates in which the examples are typically only provided for predicates at the top of the hierarchy. New predicates often need to be invented, and complex recursion is usually involved. Similarly the term structure of semantic objects is far more complex than in other applications of ILP. Advances in ILP theory and implementation related to the challenges of LLL are already producing beneficial advances in other sequence-oriented applications of ILP. In addition LLL is starting to develop its own character as a sub-discipline of AI involving the confluence of computational linguistics, machine learning and logic programming.", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ecai98.pdf", YEAR = 1998, PAGES = 697, EDITOR = "H. Prade", BOOKTITLE = "Proceedings of ECAI98", PUBLISHER = "John Wiley", NOTE = "Abstract of keynote talk"} @incollection{mugg:padd97, AUTHOR = "S.H. Muggleton", TITLE = "Declarative knowledge discovery in industrial databases", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/padd97.pdf", PAGES = "9--24", YEAR = 1997, EDITOR = "H.F. Arner", BOOKTITLE = "Proceedings of the First International Conference and Exhibition on The Practical Application of Knowledge Discovery and Data Mining (PADD-97)", PUBLISHER = "Practical Application Company Ltd."} @incollection{srin:pte, AUTHOR = "A. Srinivasan and and R.D. King S.H. Muggleton and M.J.E. Sternberg", TITLE = "The predictive toxicology evaluation challenge", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ijcai97.pdf", YEAR = 1997, PAGES = "1--6", BOOKTITLE = "Proceedings of the Fifteenth International Joint Conference Artificial Intelligence (IJCAI-97)", PUBLISHER = "Morgan-Kaufmann"} @inproceedings{mugg:stereo, TITLE = "An initial experiment into stereochemistry-based drug design using {ILP}", AUTHOR = "S.H. Muggleton and C.D. Page and A. Srinivasan", BOOKTITLE = "Proceedings of the Sixth Inductive Logic Programming Workshop (ILP96)", EDITOR = "S.H. Muggleton", PAGES = "25--40", YEAR = 1996, SERIES = "LNAI 1314", PUBLISHER = "Springer-Verlag", ADDRESS = "Berlin"} @inproceedings{moyle:eventc, TITLE = "Learning programs in the event calculus", AUTHOR = "S. Moyle and S.H. Muggleton", BOOKTITLE = "Proceedings of the Seventh Inductive Logic Programming Workshop (ILP97)", EDITOR = "N. Lavra\v{c} and S. D\v{z}eroski", YEAR = 1997, PAGES = "205--212", SERIES = "LNAI 1297", PUBLISHER = "Springer-Verlag", ADDRESS = "Berlin"} @incollection{muggpage:selfsat, AUTHOR = "S.H. Muggleton and C.D. Page", TITLE = "Self-saturation of definite clauses", YEAR = 1994, EDITOR = "S. Wrobel", BOOKTITLE = "Proceedings of the Fourth International Inductive Logic Programming Workshop", PUBLISHER = "Gesellschaft fur Mathematik und Datenverarbeitung MBH", PAGES = "161--174", NOTE = "GMD-Studien Nr 237"} @inproceedings{srin:noise, TITLE = "Distinguishing exceptions from noise in non-monotonic learning", AUTHOR = "A. Srinivasan and S.H. Muggleton and M. Bain", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/new_nil.pdf", BOOKTITLE = "Proceedings of the Second Inductive Logic Programming Workshop", YEAR = 1992, PUBLISHER = "ICOT (Technical report TM-1182)", PAGES = "97--107", ADDRESS = "Tokyo"} @inproceedings{mugg:poslearn, TITLE = "Learning from Positive data", ABSTRACT = " Gold showed in 1967 that not even regular grammars can be exactly identified from positive examples alone. Since it is known that children learn natural grammars almost exclusively from positives examples, Gold's result has been used as a theoretical support for Chomsky's theory of innate human linguistic abilities. In this paper new results are presented which show that within a Bayesian framework not only grammars, but also logic programs are learnable with arbitrarily low expected error from positive examples only. In addition, we show that the upper bound for expected error of a learner which maximises the Bayes' posterior probability when learning from positive examples is within a small additive term of one which does the same from a mixture of positive and negative examples. An Inductive Logic Programming implementation is described which avoids the pitfalls of greedy search by global optimisation of this function during the local construction of individual clauses of the hypothesis. Results of testing this implementation on artificially-generated data-sets are reported. These results are in agreement with the theoretical predictions.", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/poslearn.pdf", AUTHOR = "S.H. Muggleton", YEAR = "1996", PAGES = "358--376", EDITOR = "S.H. Muggleton", BOOKTITLE = "Proceedings of the Sixth International Workshop on Inductive Logic Programming (Workshop-96)", PUBLISHER = "Springer-Verlag", SERIES = "LNAI 1314", ADDRESS = "Berlin" } @incollection{mugg:ie, AUTHOR = "S.H. Muggleton", TITLE = "Completing inverse entailment", ABSTRACT = "Yamamoto has shown that the {\it Inverse Entailment} (IE) mechanism described previously by the author is complete for Plotkin's relative subsumption but incomplete for entailment. That is to say, an hypothesised clause $H$ can be derived from an example $E$ under a background theory $B$ using IE if and only if $H$ subsumes $E$ relative to $B$ in Plotkin's sense. Yamamoto gives examples of $H$ for which $B\cup H \models E$ but $H$ cannot be constructed using IE from $B$ and $E$. The main result of the present paper is a theorem to show that by enlarging the bottom set used within IE, it is possible to make a revised version of IE complete with respect to entailment for Horn theories. Furthermore, it is shown for function-free definite clauses that given a bound $k$ on the arity of predicates used in $B$ and $E$, the cardinality of the enlarged bottom set is bounded above by the polynomial function ${p(c+1)}^k$, where $p$ is the number of predicates in $B,E$ and $c$ is the number of constants in $B\cup\overline{E}$.", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ie.pdf", YEAR = 1998, PAGES = "245--249", EDITOR = "C.D. Page", BOOKTITLE = "Proceedings of the Eighth International Workshop on Inductive Logic Programming (ILP-98)", PUBLISHER = "Springer-Verlag", SERIES = "LNAI 1446", ADDRESS = "Berlin" } @inproceedings{turcotte:folds, AUTHOR = "M. Turcotte and S.H. Muggleton and M.J.E. Sternberg", TITLE = "Protein Fold Recognition", ABSTRACT = "Inductive Logic Programming (ILP) has been applied to discover rules governing the three-dimensional topology of protein structure. The data-set unifies two sources of information; SCOP and PROMOTIF. Cross-validation results for experiments using two background knowledge sets, global (attribute-valued) and constitutional (relational), are presented. The application makes use of a new feature of Progol4.4 for numeric parameter estimation. At this early stage of development, the rules produced can only be applied to proteins for which the secondary structure is known. However, since the rules are insightful, they should prove to be helpful in assisting the development of taxonomic schemes. The application of ILP to fold recognition represents a novel and promising approach to this problem.", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/folds.pdf", YEAR = 1998, EDITOR = "C.D. Page", BOOKTITLE = "Proc.\ of the 8th International Workshop on Inductive Logic Programming (ILP-98)", PAGES = "53--64", SERIES = "LNAI 1446", PUBLISHER = "Springer-Verlag", ADDRESS= "Berlin"} @inproceedings{khanmug:repeat, AUTHOR = "K. Khan and S.H. Muggleton and R. Parson", TITLE = "Repeat learning using predicate invention", ABSTRACT = "Most of machine learning is concerned with learning a single concept from a sequence of examples. In {\it repeat learning} the teacher chooses a series of related concepts randomly and independently from a distribution $\cal D$. A finite sequence of examples is provided for each concept in the series. The learner does not initially know $\cal D$, but progressively updates a posterior estimation of $\cal D$ as the series progresses. This papers considers {\it predicate invention} within Inductive Logic Programming as a mechanism for updating the learner's estimation of $\cal D$. A new predicate invention mechanism implemented in Progol4.4 is used in repeat learning experiments within a chess domain. The results indicate that significant performance increases can be achieved. The paper devel.pdf a Bayesian framework and demonstrates initial theoretical results for repeat learning.", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/repeat.pdf", YEAR = 1998, EDITOR = "C.D. Page", PAGES = "165--174", BOOKTITLE = "Proc.\ of the 8th International Workshop on Inductive Logic Programming (ILP-98)", PUBLISHER = "Springer-Verlag", SERIES = "LNAI 1446", ADDRESS= "Berlin"} @book{koza1994genetic, title={Genetic programming II: automatic discovery of reusable programs}, author={Koza, John R and Rice, James P}, volume={40}, year={1994}, publisher={MIT press Cambridge} } @inproceedings{roberts:traffic, AUTHOR = "S. Roberts and W. Van Laerand and N. Jacobs and S.H. Muggleton and J. Broughton", TITLE = " A comparison of {ILP} and propositional systems on propositional data", ABSTRACT = "This paper presents an experimental comparison of two Inductive Logic Programming algorithms, Progol and Tilde, with C4.5, a propositional learning algorithm, on a propositional dataset of road traffic accidents. Rebalancing methods are described for handling the skewed distribution of positive and negative examples in this dataset, and the relative cost of errors of commission and omission in this domain. It is noted that before the use of these methods all algorithms perform worse than majority class. On rebalancing, all did significantly better. The conclusion drawn from th experimental results is that on such a propositional data set ILP algorithms perform competitively in terms of predictive accuracy with propositional systems, but are significantly outperformed in terms of time taken for learning.", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/traffic.pdf", YEAR = 1998, EDITOR = "C.D. Page", PAGES = "291--299", BOOKTITLE = "Proc.\ of the 8th International Workshop on Inductive Logic Programming (ILP-98)", PUBLISHER = "Springer-Verlag", SERIES = "LNAI 1446", ADDRESS= "Berlin"} @inproceedings{dzer:spanish, AUTHOR = "S. D\v{z}eroski and N. Jacobs and M. Molina and C. Moure and S.H. Muggleton and W. Van Laer", TITLE = "Detecting traffic problems with {ILP}", ABSTRACT = "Expert systems for decision support have recently been successfully introduced in road transport management. These systems include knowledge on traffic problem detection and alleviation. The paper describes experiments in automated acquisition of knowledge on traffic problem detection. The task is to detect road sections where a problem has occurred (critical sections) from sensor data. It is necessary to use inductive logic programming (ILP) for this purpose as relational background knowledge on the road network is essential. In this paper, we apply three state-pf-the-art ILP systems to learn how to detect traffic problems.", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/spanish.pdf", YEAR = 1998, EDITOR = "C.D. Page", PAGES = "281-290", BOOKTITLE = "Proc.\ of the 8th International Workshop on Inductive Logic Programming (ILP-98)", PUBLISHER = "Springer-Verlag", SERIES = "LNAI 1446", ADDRESS= "Berlin"} @inproceedings{mugg:ilp98invited, AUTHOR = "S.H. Muggleton", TITLE = "Advances in {ILP} theory and implementations", ABSTRACT = "A strong linkage exists between advances in applications, implementations and theory within Inductive Logic Programming (ILP). Early ILP systems, such as FOIL, Golem and LINUS learned single predicate definitions from positive and negative examples and extensional background knowledge. They also employed strong learning biases such as ij-determinacy. Although these systems found a number of applications, they had problems in areas such as molecular biology and natural language learning. General mechanisms for inverting entailment have now been developed which support the use-of non-ground background knowledge, and the revision of multiple inter-related predicates. ILP theory results concerning complete refinement graph operators now allow efficient admissible searches. The absolute requirement for negative examples (rare within natural language domains) has been eased by Bayesian analysis of learning from positive-only examples. Bayesian approaches have also supported sample complexity analysis of predicate invention within the framework of repeat learning. In this framework it is assumed that the learner's prior is not equivalent to the distribution from which the teacher is sampling targets. By providing a series of sessions the learner is able to update the initial prior by adding and deleting background predicates. Within the Bayesian framework stochastic logic program representations have been used to estimate the distribution of examples over the instance space. Stochastic logic programs are a generalisation of hidden Markov models and stochastic grammars. Apart from a few special cases PAC-learning results have been largely negative for ILP. This is in large part due to the fact that testing satisfiability is intractrable for most interesting subsets of first-order Horn logic. The development of Bayesian approaches to ILP supported the development of U-learnability, which allows classes of distributions over the hypotheses. Here it was shown that for any exponential-decay distribution the class of time-bounded logic-programs is polynomially U-learnable. The use of such bounds on proof depth is common within ILP systems. Although logically impure, this approach allows general-purpose flexible representations, while maintaining termination guarantees.", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ilp98:invited.pdf", YEAR = 1998, EDITOR = "C.D. Page", NOTE = "Abstract of keynote presentation", PAGES = "9", BOOKTITLE = "Proc.\ of the 8th International Workshop on Inductive Logic Programming (ILP-98)", PUBLISHER = "Springer-Verlag", SERIES = "LNAI 1446", ADDRESS= "Berlin"} @inproceedings{mugg:biochem:ds98, AUTHOR = "S.H. Muggleton and A. Srinivasan and R.D. King and M.J.E. Sternberg", TITLE = "Biochemical knowledge discovery using {I}nductive {L}ogic {P}rogramming", ABSTRACT = "Machine Learning algorithms are being increasingly used for knowledge discovery tasks. Approaches can be broadly divided by distinguishing discovery of procedural from that of declarative knowledge. Client requirements determine which of these is appropriate. This paper discusses an experimental application of machine learning in an area related to drug design. The bottleneck here is in finding appropriate constraints to reduce the large number of candidate molecules to be synthesised and tested. Such constraints can be viewed as declarative specifications of the structural elements necessary for high medicinal activity and low toxicity. The first-order representation used within Inductive Logic Programming (ILP) provides an appropriate description language for such constraints. Within this application area knowledge accreditation requires not only a demonstration of predictive accuracy but also, and crucially, a certification of novel insight into the structural chemistry. This paper describes an experiment in which the ILP system Progol was used to obtain structural constraints associated with mutagenicity of molecules. In doing so Progol found a new indicator of mutagenicity within a subset of previously published data. This subset was already known not to be amenable to statistical regression, though its complement was adequately explained by a linear model. According to the combined accuracy/explanation criterion provided in this paper, on both subsets comparative trials show that Progol's structurally-oriented hypotheses are preferable to those of other machine learning algorithms.", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ds98jnt.pdf", YEAR = 1998, EDITOR = "H. Motoda", BOOKTITLE = "Proc.\ of the first Conference on Discovery Science", PUBLISHER = "Springer-Verlag", ADDRESS= "Berlin"} @inproceedings{mugg:invited:ds98, AUTHOR = "S.H. Muggleton", TITLE = "Knowledge discovery in biological and chemical domains", NOTE = "Abstract of keynote talk", ABSTRACT = "This talk will review the results of the last few years' academic pilot studies involving the application of ILP to the prediction of protein secondary structure, mutagenicity, structure activity , pharmacophore discovery and protein fold analysis. While predictive accuracy is the central performance measure of data analytical techniques which generate procedural knowledge (neural nets, decision trees, etc.), the performance of an ILP system is determined both by accuracy and degree of stereo-chemical insight provided. ILP hypotheses can be easily stated in English and exemplified diagrammatically. This allows cross-checking with the relevant biological and chemical literature. Most importantly it allows for expert involvement in human background knowledge refinement and for final dissemination of discoveries to the wider scientific community. In several of the comparative trials presented ILP systems provided significant chemical and biological insights where other data analysis techniques did not. In his statement of the importance of this line of research to the Royal Society Sternberg emphasised the aspect of joint human-computer collaboration in scientific discoveries. Science is an activity of human societies. It is our belief that computer-based scientific discovery must support strong integration into existing the social environment of human scientific communities. The discovered knowledge must add to and build on existing science. The author believes that the ability to incorporate background knowledge and re-use learned knowledge together with the comprehensibility of the hypotheses, have marked out ILP as a particularly effective approach for scientific knowledge discovery.", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ds98inv.pdf", YEAR = 1998, EDITOR = "H. Motoda", BOOKTITLE = "Proc.\ of the first Conference on Discovery Science", PUBLISHER = "Springer-Verlag", ADDRESS= "Berlin"} @inproceedings{muggbain:ap, AUTHOR = "S.H. Muggleton and M. Bain", TITLE = "Analogical Prediction", YEAR = 1999, BOOKTITLE = "Proc.\ of the 9th International Workshop on Inductive Logic Programming (ILP-99)", ABSTRACT = "Inductive Logic Programming (ILP) involves constructing an hypothesis $H$ on the basis of background knowledge $B$ and training examples $E$. An independent test set is used to evaluate the accuracy of $H$. This paper concerns an alternative approach called Analogical Prediction (AP). AP takes $B,E$ and then for each test example $\langle x,y\rangle$ forms an hypothesis $H_x$ from $B,E,x$. Evaluation of AP is based on estimating the probability that $H_{x}(x)=y$ for a randomly chosen $\langle x,y\rangle$. AP has been implemented within CProgol4.4. Experiments in the paper show that on English past tense data AP has significantly higher predictive accuracy on this data than both previously reported results and CProgol in inductive mode. However, on KRK illegal AP does not outperform CProgol in inductive mode. We conjecture that AP has advantages for domains in which a large proportion of the examples must be treated as exceptions with respect to the hypothesis vocabulary. The relationship of AP to analogy and instance-based learning is discussed. Limitations of the given implementation of AP are discussed and improvements suggested.", PAGES = "234-244", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ap.pdf", PUBLISHER = "Springer-Verlag", ADDRESS= "Berlin"} @inproceedings{parskhanmug:trec, AUTHOR = "R. Parson and K. Khan and S.H. Muggleton", TITLE = "Theory recovery", ABSTRACT = "In this paper we examine the problem of repairing incomplete background knowledge using Theory Recovery. Repeat Learning under ILP considers the problem of updating background knowledge in order to progressively increase the performance of an ILP algorithm as it tackles a sequence of related learning problems. Theory recovery is suggested as a suitable mechanism. A bound is derived for the performance of theory recovery in terms of the information content of the missing predicate definitions. Experiments are described that use the logical back-propagation ability of Progol 5.0 to perform theory recovery. The experimental results are consistent with the derived bound.", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/trec.pdf", YEAR = 1999, BOOKTITLE = "Proc.\ of the 9th International Workshop on Inductive Logic Programming (ILP-99)", PUBLISHER = "Springer-Verlag", ADDRESS= "Berlin"} @inproceedings{mugg:slplearn, AUTHOR = "S.H. Muggleton", TITLE = "Learning Stochastic Logic Programs", BOOKTITLE = "Proceedings of the AAAI2000 workshop on Learning Statistical Models from Relational Data", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/slplearn.pdf", EDITOR = "Lise Getoor and David Jensen", PUBLISHER = "AAAI", YEAR = 2000} @inproceedings{mugg:s.pdfem, AUTHOR = "S.H. Muggleton", TITLE = "Semantics and derivation for Stochastic Logic Programs", BOOKTITLE = "Proceedings of the UAI2000 workshop on Knowledge-Data Fusion", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/slpsem.pdf", EDITOR = "Richard Dybowski", PUBLISHER = "UAI", YEAR = 2000} @inproceedings{mugg:icml2k, AUTHOR = "S.H. Muggleton and C.H. Bryant and A. Srinivasan", TITLE = "Learning {C}homsky-like Grammars for Biological Sequence Families", URL = "ftp://ftp.cs.york.ac.uk/pub/aig/Papers/bryant/bryant_icml2k.pdf", BOOKTITLE = "Proceedings of the Seventeenth International Conference on Machine Learning", PAGES = "631--638", PUBLISHER = "San Francisco, CA: Morgan Kaufmann", ADDRESS = "Stanford University, USA", YEAR = 2000, ABSTRACT = "This paper presents a new method of measuring performance when positives are rare and investigates whether Chomsky-like grammar representations are useful for learning accurate comprehensible predictors of members of biological sequence families. The positive-only learning framework of the Inductive Logic Programming (ILP) system CProgol is used to generate a grammar for recognising a class of proteins known as human neuropeptide precursors (NPPs). As far as these authors are aware, this is both the first biological grammar learnt using ILP and the first real-world scientific application of the positive-only learning framework of CProgol. Performance is measured using both predictive accuracy and a new cost function, {\em Relative Advantage} ($RA$). The $RA$ results show that searching for NPPs by using our best NPP predictor as a filter is more than 100 times more efficient than randomly selecting proteins for synthesis and testing them for biological activity. The highest $RA$ was achieved by a model which includes grammar-derived features. This $RA$ is significantly higher than the best $RA$ achieved without the use of the grammar-derived features."} @inproceedings{mugg:ecml2k, AUTHOR = "S.H. Muggleton and C.H.Bryant and A.Srinivasan", TITLE = "Measuring Performance when Positives are Rare: Relative Advantage versus Predictive Accuracy - a Biological Case-study", URL = "ftp://ftp.cs.york.ac.uk/pub/aig/Papers/bryant/bryant_ecml2k.pdf", BOOKTITLE = "Proceedings of the 11th European Conference on Machine Learning", EDITOR = "R.Lopez de Mantaras and E.Plaza", SERIES = "Lecture Notes in Computer Science", PUBLISHER = "\copyright Springer Verlag", ADDRESS = "http://www.springer.de/comp/lncs/index.html", YEAR = 2000, ABSTRACT = "This paper presents a new method of measuring performance when positives are rare and investigates whether Chomsky-like grammar representations are useful for learning accurate comprehensible predictors of members of biological sequence families. The positive-only learning framework of the Inductive Logic Programming (ILP) system CProgol is used to generate a grammar for recognising a class of proteins known as human neuropeptide precursors (NPPs). Performance is measured using both predictive accuracy and a new cost function, {\em Relative Advantage} ($RA$). The $RA$ results show that searching for NPPs by using our best NPP predictor as a filter is more than 100 times more efficient than randomly selecting proteins for synthesis and testing them for biological activity. Predictive accuracy is not a good measure of performance for this domain because it does not discriminate well between NPP recognition models: despite covering varying numbers of (the rare) positives, all the models are awarded a similar (high) score by predictive accuracy because they all exclude most of the abundant negatives."} @inproceedings{bryant:aisb99, AUTHOR = "C.H.Bryant and S.H. Muggleton and C.D.Page and M.J.E.Sternberg", TITLE = "Combining {A}ctive {L}earning with {I}nductive {L}ogic {P}rogramming to close the loop in {M}achine {L}earning", URL = "ftp://ftp.cs.york.ac.uk/pub/aig/Papers/bryant/bryant_aisb99.pdf", BOOKTITLE = "Proceedings of AISB'99 Symposium on AI and Scientific Creativity", EDITOR = "S. Colton", PAGES = "59--64", PUBLISHER = "The Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB)", ADDRESS = "http://www.cogs.susx.ac.uk/aisb/", YEAR = 1999, ABSTRACT = "Machine Learning (ML) systems that produce human-comprehensible hypotheses from data are typically open loop, with no direct link between the ML system and the collection of data. This paper describes the alternative, {\it Closed Loop Machine Learning}. This is related to the area of Active Learning in which the ML system actively selects experiments to discriminate between contending hypotheses. In Closed Loop Machine Learning the system not only selects but also carries out the experiments in the learning domain. ASE-Progol, a Closed Loop Machine Learning system, is proposed. ASE-Progol will use the ILP system Progol to form the initial hypothesis set. It will then devise experiments to select between competing hypotheses, direct a robot to perform the experiments, and finally analyse the experimental results. ASE-Progol will then revise its hypotheses and repeat the cycle until a unique hypothesis remains. This will be, to our knowledge, the first attempt to use a robot to carry out experiments selected by Active Learning within a real world application."} @inproceedings{muggbryant:logprop, AUTHOR = "S.H. Muggleton and C.H. Bryant", TITLE = "Theory completion using Inverse Entailment", ABSTRACT = "The main real-world applications of Inductive Logic Programming (ILP) to date involve the ``Observation Predicate Learning'' (OPL) assumption, in which both the examples and hypotheses define the same predicate. However, in both scientific discovery and language learning potential applications exist in which OPL does not hold. OPL is ingrained within the theory and performance testing of Machine Learning. A general ILP technique called ``Theory Completion using Inverse Entailment'' (TCIE) is introduced which is applicable to non-OPL applications. TCIE is based on inverse entailment and is closely allied to abductive inference. The implementation of TCIE within Progol5.0 is described. The implementation uses contra-positives in a similar way to Stickel's Prolog Technology Theorem Prover. Progol5.0 is tested on two different data-sets. The first dataset involves a grammar which translates numbers to their representation in English. The second dataset involves hypothesising the function of unknown genes within a network of metabolic pathways. On both datasets near complete recovery of performance is achieved after relearning when randomly chosen portions of background knowledge are removed. Progol5.0's running times for experiments in this paper were typically under 6 seconds on a standard laptop PC.", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/logprop.pdf", YEAR = 2000, BOOKTITLE = "Proc.\ of the 10th International Workshop on Inductive Logic Programming (ILP-00)", PUBLISHER = "Springer-Verlag", PAGES = "130--146", ADDRESS= "Berlin"} @inproceedings{alimugg:ilp2000, Author = {A. Tamaddoni-Nezhad and S.H. Muggleton}, Title = {Searching the Subsumption Lattice by a Genetic Algorithm}, Booktitle = {Proceedings of the 10th International Conference on Inductive Logic Programming}, Editor = {J. Cussens and A. Frisch}, Publisher = {Springer-Verlag}, URL = "http://www.doc.ic.ac.uk/\~shm/Papers/gailp00.pdf", Year = 2000, ISBN = {3-540-67795-X}, Pages = {243--252} } @inproceedings{alimugg:ga_ilp, AUTHOR = "A. Tamaddoni-Nezhad and S.H. Muggleton", TITLE = "Using Genetic Algorithms for Learning Clauses in First-Order Logic", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/gailp.pdf", YEAR = 2001, EDITORS = "Spector and L. and E. Goodman and A. Wu and W.B. Langdon and H.-M. Voigt and M. Gen and S. Sen and M. Dorigo and S. Pezeshk and M. Garzon and E. Burke", BOOKTITLE = "Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2001", PAGES = "639--646", PUBLISHER = "Morgan Kaufmann Publishers", ADDRESS= "San Francisco, CA"} @inproceedings{diffslp:mugg, AUTHOR = "S.H. Muggleton", TITLE = "Learning structure and parameters of Stochastic Logic Programs", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/diffslp.pdf", YEAR = 2002, PAGES = "198--206", Booktitle = {Proceedings of the 12th International Conference on Inductive Logic Programming}, PUBLISHER = "Springer-Verlag" } @inproceedings{alimugg:ga_ilp02, AUTHOR = "A. Tamaddoni-Nezhad and S.H. Muggleton", TITLE = "A genetic algorithms approach to {ILP}", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/gailp02.pdf", YEAR = 2002, PAGES = "285--300", Booktitle = {Proceedings of the 12th International Conference on Inductive Logic Programming}, PUBLISHER = "Springer-Verlag" } @inproceedings{puechmuggl:comparison, AUTHOR = "A. Puech and S.H. Muggleton", TITLE = "A Comparison of Stochastic Logic Programs and {B}ayesian Logic Programs", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/puech-paper2.pdf", YEAR = 2003, Booktitle = {IJCAI03 Workshop on Learning Statistical Models from Relational Data}, PUBLISHER = "IJCAI" } @inproceedings{muggaliwat:enzymes, AUTHOR = "S.H. Muggleton and A. Tamaddoni-Nezhad and H. Watanabe", TITLE = "Induction of enzyme classes from biological databases", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ilp03_eclass.pdf", YEAR = 2003, PAGES = "269-280", Booktitle = {Proceedings of the 13th International Conference on Inductive Logic Programming}, SERIES = "LNAI 2835", PUBLISHER = "Springer-Verlag" } @inproceedings{coltonmugg:ILPmaths, AUTHOR = "S. Colton and S.H. Muggleton", TITLE = "{ILP} for Mathematical Discovery", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ILPmaths.pdf", PAGES = "93--111", YEAR = 2003, SERIES = "LNAI 2835", Booktitle = {Proceedings of the 13th International Conference on Inductive Logic Programming}, PUBLISHER = "Springer-Verlag" } inproceedings{lau2003learning, title={Learning programs from traces using version space algebra}, author={Lau, Tessa and Domingos, Pedro and Weld, Daniel S}, booktitle={Proceedings of the 2nd international conference on Knowledge capture}, pages={36--43}, year={2003}, organization={ACM} } @inproceedings{lodhimugg:ensemble, AUTHOR = "H. Lodhi and S.H. Muggleton", TITLE = "Modelling Metabolic Pathways Using Stochastic Logic Programs-Based Ensemble Methods", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ensemble.pdf", YEAR = 2004, Booktitle = {Proceedings of the 2nd International Conference on Computational Methods in System Biology}, PUBLISHER = "Springer-Verlag" } @inproceedings{alimugg:metabduce, AUTHOR = "A. Tamaddoni-Nezhad and A. Kakas and S.H. Muggleton and F. Pazos", TITLE = "Modelling inhibition in metabolic pathways through Abduction and Induction", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/metabduce.pdf", PAGES = "305--322", YEAR = 2004, Booktitle = {Proceedings of the 14th International Conference on Inductive Logic Programming}, SERIES = "LNAI 3194", PUBLISHER = "Springer-Verlag" } @inproceedings{alimuggl:ijcai03, AUTHOR = "A. Tamaddoni-Nezhad and S.H. Muggleton and J. Bang", TITLE = " A {B}ayesian Model for Metabolic Pathways", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/metabayes.pdf", YEAR = 2003, Booktitle = {International Joint Conference on Artificial Intelligence (IJCAI03) Workshop on Learning Statistical Models from Relational Data}, pages = {50-57}, PUBLISHER = "IJCAI" } @inproceedings{mugg:mlsysbio, AUTHOR = "S.H. Muggleton", TITLE = "Machine Learning for Systems Biology", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/mlsysbio.pdf", YEAR = 2005, PAGES = "416--423", Booktitle = {Proceedings of the 15th International Conference on Inductive Logic Programming}, SERIES = "LNAI 3625", PUBLISHER = "Springer-Verlag" } @inproceedings{mugg:svilp, AUTHOR = "S.H. Muggleton and H. Lodhi and A. Amini and M.J.E. Sternberg", TITLE = "Support {V}ector {I}nductive {L}ogic {P}rogramming", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/svilp.pdf", YEAR = 2005, PAGES = "163--175", Booktitle = {Proceedings of the 8th International Conference on Discovery Science}, SERIES = "LNAI 3735", PUBLISHER = "Springer-Verlag" } @inproceedings{mugg:learnstoch, AUTHOR = "H. Watanabe and S.H. Muggleton", TITLE = "Learning {S}tochastic {L}ogical {A}utomaton", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/stochlog.pdf", YEAR = 2005, Booktitle = {Proceedings of the 19th Annual Conferences of JSAI}, PAGES = "201--211", SERIES = "LNCS 4012", PUBLISHER = "Springer-Verlag" } @inproceedings{mugg:accelfidj, AUTHOR = "A. Fidjeland, W. Luk, S.H. Muggleton", TITLE = "Scalable acceleration of inductive logic programs", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/accelfidj.pdf", YEAR = 2002, PAGES = "252 - 259", Booktitle = {IEEE international conference on field-programmable technology}, PUBLISHER = "IEEE"} @inproceedings{alimug:metabduceieee, AUTHOR = "A. Tamaddoni-Nezhad and R. Chaleil and A. Kakas and S.H. Muggleton", TITLE = "Abduction and induction for learning models of inhibition in metabolic networks", YEAR = 2005, Booktitle = {Proceedings of the Fourth International Conference on Machine Learning and Applications, ICMLA'05}, URL = "http://www.doc.ic.ac.uk/\~shm/Papers/metabduceieee.pdf", PUBLISHER = "IEEE Computer Society" } @inproceedings{mugg:chemturing, AUTHOR = "S.H. Muggleton", TITLE = "Towards {U}niversal {C}hemical {T}uring {M}achines", YEAR = 2006, PAGES = "1527--1529", Booktitle = {Proceedings of the Twenty-First National Conference on Artificial Intelligence, AAAI-06}, URL = "http://www.doc.ic.ac.uk/\~shm/Papers/chemturing.pdf", PUBLISHER = "AAAI Press" } @inproceedings{alimugg:largescale, AUTHOR = "A. Tamaddoni-Nezhad and R. Greaves and S.H. Muggleton", TITLE = "Large-scale online learning using Analogical Prediction", YEAR = 2006, Booktitle = {Short Paper Proceedings of the 16th International Conference on Inductive Logic Programming}, PUBLISHER = "University of Corunna" } @inproceedings{alimugg:qg-ga, AUTHOR = "S.H. Muggleton and A. Tamaddoni-Nezhad", TITLE = "{QG/GA}: A Stochastic Search Approach for {P}rogol", YEAR = 2006, Booktitle = {Proceedings of the 16th International Conference on Inductive Logic Programming}, SERIES = "LNAI 4455", PAGES = "37--39", PUBLISHER = "Springer-Verlag" } @inproceedings{arvmugg:slpabduce, AUTHOR = "A. Arvanitis and S.H. Muggleton and J. Chen and H. Watanabe", TITLE = "Abduction with Stochastic Logic Programs based on a Possible Worlds Semantics", YEAR = 2006, Booktitle = {Short Paper Proceedings of the 16th International Conference on Inductive Logic Programming}, PUBLISHER = "University of Corunna" } @inproceedings{chenmugg:blpvslp, AUTHOR = "J. Chen and S.H. Muggleton", TITLE = "A Revised Comparison of {B}ayesian Logic Programs and Stochastic Logic Programs", YEAR = 2006, Booktitle = {Short Paper Proceedings of the 16th International Conference on Inductive Logic Programming}, PUBLISHER = "University of Corunna" } @inproceedings{chenmugg:protslp, AUTHOR = "J. Chen and S.H. Muggleton", TITLE = "Multi-Class Protein Fold Prediction Using Stochastic Logic Programs", YEAR = 2006, Booktitle = {Short Paper Proceedings of the 16th International Conference on Inductive Logic Programming}, PUBLISHER = "University of Corunna" } @inproceedings{oteromugg:mccarthy, AUTHOR = "R. Otero and S.H. Muggleton", TITLE = "On {McCarthy}'s Appearance and Reality Problem", YEAR = 2006, Booktitle = {Short Paper Proceedings of the 16th International Conference on Inductive Logic Programming}, PUBLISHER = "University of Corunna" } @inproceedings{muggpahl:blprmm, AUTHOR = "S.H. Muggleton and N. Pahlavi", TITLE = "The Complexity of Translating BLPs to RMMs", YEAR = 2006, Booktitle = {Short Paper Proceedings of the 16th International Conference on Inductive Logic Programming}, PUBLISHER = "University of Corunna" } @inproceedings{muggpahl:hodatalog, AUTHOR = "N. Pahlavi and S.H. Muggleton", TITLE = "Towards Efficient Higher-order Logic Learning in a First-order Datalog Framework", YEAR = 2012, Booktitle = {Latest Advances in Inductive Logic Programming}, URL = "http://ilp11.doc.ic.ac.uk/short_papers/ilp2011_submission_46.pdf", PUBLISHER = "Imperial College Press", PAGES = "209--216"} @inproceedings{watmugg:compactheory, AUTHOR = "H. Watanabe and K. Inoue and Stephen Muggleton", TITLE = "Complexity Analysis of Abductive Action Theory", YEAR = 2006, Booktitle = {Short Paper Proceedings of the 16th International Conference on Inductive Logic Programming}, PUBLISHER = "University of Corunna" } @inproceedings{chenmugg:problabels, AUTHOR = "J. Chen and S.H. Muggleton and J. Santos", TITLE = "Learning Probabilistic Learning Models from Examples (extended abstract)", YEAR = 2007, Booktitle = {Proceedings of the 17th International Conference on Inductive Logic Programming}, SERIES = "LNAI 4894", PAGES = "22--23", PUBLISHER = "Springer-Verlag" } @inproceedings{alimug:ierefinement, AUTHOR = "A. Tamaddoni-Nezhad and S.H. Muggleton", TITLE = " A Note on Refinement Operators for {IE}-Based {ILP} Systems", YEAR = 2008, Booktitle = {Proceedings of the 18th International Conference on Inductive Logic Programming}, SERIES = "LNAI 5194", PAGES = "297--314", NOTE = " DOI: 10.1007/978-3-540-85928-4_23", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ierefinement.pdf", PUBLISHER = "Springer-Verlag" } @inproceedings{lodhimug:msvilp, AUTHOR = "H. Lodhi and S.H. Muggleton and M.J.E. Sternberg", TITLE = "Learning Large Margin First Order Decision Lists for Multi-Class Classification", YEAR = 2009, Booktitle = {Proceedings of the 12th International Conference on Discovery Science}, SERIES = "LNAI 5808", PAGES = "163--183", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/msvilp.pdf", PUBLISHER = "Springer-Verlag" } @inproceedings{santosmug:head, AUTHOR = "J. Santos and A. Tamaddoni-Nezhad and S.H. Muggleton", TITLE = "An {ILP} System for Learning Head Output Connected Predicates", YEAR = 2009, Booktitle = {Proceedings of the 14th Portuguese Conference on Artificial Intelligence}, SERIES = "LNAI 5816", PAGES = "150--159", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/EPIA09.pdf", PUBLISHER = "Springer-Verlag" } @inproceedings{fidjmug:fpga08, AUTHOR = "A. Fidjeland and W. Luk and S.H. Muggleton", TITLE = "A Customisable Multiprocessor for Application-Optimised Inductive Logic Programming", YEAR = 2008, Booktitle = {Proceedings of Visions of Computer Science - BCS International Academic Conference}, SERIES = "LNAI 5816", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/fpga08.pdf", PUBLISHER = "British Computer Society" } @inproceedings{mugsantos:progolem, AUTHOR = "S.H. Muggleton and J. Santos and A. Tamaddoni-Nezhad", TITLE = "{ProGolem}: a system based on relative minimal generalisation", YEAR = 2010, Booktitle = {Proceedings of the 19th International Conference on Inductive Logic Programming}, SERIES = "LNCS 5989", PAGES = "131--148", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/progolem.pdf", PUBLISHER = "Springer-Verlag" } @inproceedings{mugsantos:toplog, AUTHOR = "S.H. Muggleton and J. Santos and A. Tamaddoni-Nezhad", TITLE = "{TopLog}: {ILP} Using a Logic Program Declarative Bias", YEAR = 2010, Booktitle = {Proceedings of the International Conference on Logic Programming 2008}, SERIES = "LNCS 5366", PAGES = "687--692", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/toplog.pdf", PUBLISHER = "Springer-Verlag" } @inproceedings{mugpaes:chess, AUTHOR = "S.H. Muggleton and A. Paes and V. Santos Costa and G. Zaverucha", TITLE = "Chess revision: acquiring the rules of chess variants through {FOL} theory revision from examples", YEAR = 2010, Booktitle = {Proceedings of the 19th International Conference on Inductive Logic Programming (ILP 2009)}, EDITOR = "Luc De Raedt", SERIES = "LNCS 5989", PAGES = "123--130", PUBLISHER = "Springer-Verlag", ADDRESS = "Berlin", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/paeschess.pdf", PUBLISHER = "Springer-Verlag" } @inproceedings{watmugg:large, AUTHOR = "H. Watanabe and S.H. Muggleton", TITLE = "Can {ILP} be applied to large datasets?", YEAR = 2010, Booktitle = {Proceedings of the 19th International Conference on Inductive Logic Programming (ILP 2009)}, EDITOR = "Luc De Raedt", SERIES = "LNAI 5989", PAGES = "249--256", PUBLISHER = "Springer-Verlag", ADDRESS = "Berlin", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/watlarge.pdf", PUBLISHER = "Springer-Verlag" } @inproceedings{chen:dtlp, AUTHOR = "J. Chen S.H. Muggleton", TITLE = "Decision-Theoretic Logic Programs", YEAR = 2010, NOTE = {19th International Conference on Inductive Logic Programming, Poster presentation}, URL = "http://www.doc.ic.ac.uk/\~shm/Papers/dtlp.pdf" } @inproceedings{pahlmug:hollfoll, AUTHOR = "N. Pahlavi and S.H. Muggleton", TITLE = "Can {HOLL} outperform {FOLL}?", YEAR = 2011, SERIES = "LNAI 6489", Booktitle = {Proceedings of the 20th International Conference on Inductive Logic Programming (ILP 2010)}, EDITOR = "Paolo Frasconi and Francesca A. Lisi", PUBLISHER = "Springer-Verlag", ADDRESS = "Berlin", PAGES = "198--205" } @inproceedings{alimugg:stochrefine, AUTHOR = "A. Tamaddoni-Nezhad and S.H. Muggleton", TITLE = "Stochastic Refinement", YEAR = 2011, SERIES = "LNAI 6489", Booktitle = {Proceedings of the 20th International Conference on Inductive Logic Programming (ILP 2010)}, PAGES = "222--237", EDITOR = "Paolo Frasconi and Francesca A. Lisi", PUBLISHER = "Springer-Verlag", ADDRESS = "Berlin", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/Stochastic_refinement.pdf" } @inproceedings{mugg:uic, AUTHOR = "S.H. Muggleton and J. Chen and H. Watanabe and S. Dunbar and C. Baxter and R. Currie and J.D. Salazar and J. Taubert and M.J.E. Sternberg", TITLE = "Variation of background knowledge in an industrial application of {ILP}", YEAR = 2011, SERIES = "LNAI 6489", Booktitle = "Proceedings of the 20th International Conference on Inductive Logic Programming (ILP 2011)", PAGES = "158--170", EDITOR = "Paolo Frasconi and Francesca A. Lisi", PUBLISHER = "Springer-Verlag", ADDRESS = "Berlin", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/uic.pdf" } @inproceedings{mugg:mctoplog, AUTHOR = "S.H. Muggleton and D. Lin and A. Tamaddoni-Nezhad", TITLE = "{MC}-{T}opLog: Complete Multi-clause Learning Guided by A Top Theory", YEAR = 2012, SERIES = "LNAI 7207", BOOKTITLE = "Proceedings of the 21st International Conference on Inductive Logic Programming (ILP 2011)", EDITOR = "Stephen H. Muggleton and Alireza Tamaddoni-Nezhad and Francesca A. Lisi", PAGES = "238--254", PUBLISHER = "Springer-Verlag", ADDRESS = "Berlin", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/mctoplog.pdf" } @inproceedings{mugg:eco, AUTHOR = "A. Tamaddoni-Nezhad and D. Bohan and A. Raybould and S.H. Muggleton", TITLE = "Machine Learning a Probabilistic Network of Ecological Interactions", YEAR = 2012, SERIES = "LNAI 7207", BOOKTITLE = "Proceedings of the 21st International Conference on Inductive Logic Programming (ILP 2011)", EDITOR = "Stephen H. Muggleton and Alireza Tamaddoni-Nezhad and Francesca A. Lisi", PUBLISHER = "Springer-Verlag", ADDRESS = "Berlin", PAGES = "332--346"} @inproceedings{uic2, AUTHOR = "D. Lin and J. Chen and H. Watanabe and S.H. Muggleton and P. Jain and M. Sternberg and C. Baxter and R. Currie and S. Dunbar and M. Earll and D. Salazar", TITLE = "Does Multi-clause Learning Help in Real-world Applications?", YEAR = 2012, SERIES = "LNAI 7207", BOOKTITLE = "Proceedings of the 21st International Conference on Inductive Logic Programming (ILP 2011)", EDITOR = "Stephen H. Muggleton and Alireza Tamaddoni-Nezhad and Francesca A. Lisi", PAGES = "221--237", PUBLISHER = "Springer-Verlag", ADDRESS = "Berlin", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/uicmulticlause.pdf" } @inproceedings{mugg:metagolD, AUTHOR = "S.H. Muggleton and D. Lin", TITLE = "Meta-Interpretive Learning of Higher-Order Dyadic Datalog: Predicate Invention revisited", YEAR = 2013, BOOKTITLE = "Proceedings of the 23rd International Joint Conference Artificial Intelligence (IJCAI 2013)", PAGES = "1551--1557", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/metagold.pdf" } @inproceedings{mugg:metabias, AUTHOR = "D. Lin and E. Dechter and K. Ellis and J.B. Tenenbaum and S.H. Muggleton", TITLE = "Bias reformulation for one-shot function induction", YEAR = 2014, BOOKTITLE = "Proceedings of the 23rd European Conference on Artificial Intelligence (ECAI 2014)", PUBLISHER = "IOS Press", ADDRESS = "Amsterdam", PAGES = "525--530", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/metabias.pdf" } @inproceedings{mugg:metabayes, AUTHOR = "S.H. Muggleton and D. Lin and J. Chen and A. Tamaddoni-Nezhad ", TITLE = "MetaBayes: Bayesian Meta-Interpretative Learning using Higher-Order Stochastic Refinement", YEAR = 2014, BOOKTITLE = "Proceedings of the 23rd International Conference on Inductive Logic Programming (ILP 2013)", PAGES = "1--17", PUBLISHER = "Springer-Verlag", EDITOR = "Gerson Zaverucha and Vitor Santos Costa and Aline Marins Paes", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/metabayeslong07.pdf", NOTE = "LNAI 8812", ADDRESS = "Berlin"} @inproceedings{mugg:minmeta, AUTHOR = "A. Cropper and S.H. Muggleton", TITLE = "Logical minimisation of meta-rules within Meta-Interpretive Learning", YEAR = 2015, BOOKTITLE = "Proceedings of the 24th International Conference on Inductive Logic Programming", PAGES = "65--78", PUBLISHER = "Springer-Verlag", NOTE = "LNAI 9046", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/minmeta.pdf" } @inproceedings{atn:predeco, AUTHOR = "A. Tamaddoni-Nezhad and D. Bohan and A. Raybould and S.H. Muggleton", TITLE = "Towards machine learning of predictive models from ecological data", YEAR = 2015, BOOKTITLE = "Proceedings of the 24th International Conference on Inductive Logic Programming", PAGES = "159--173", PUBLISHER = "Springer-Verlag", NOTE = "LNAI 9046" , URL = "http://www.doc.ic.ac.uk/\~shm/Papers/predeco.pdf" } @inproceedings{mugg:metagolO, AUTHOR = "A. Cropper and S.H. Muggleton", TITLE = "Learning Efficient Logical Robot Strategies Involving Composable Objects", YEAR = 2015, BOOKTITLE = "Proceedings of the 24th International Joint Conference Artificial Intelligence (IJCAI 2015)", PAGES = "3423--3429", PUBLISHER = "IJCAI", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/metagolo.pdf" } @inproceedings{cropmugg:datacurate, AUTHOR = "A. Cropper and A. Tamaddoni-Nezhad and and S.H. Muggleton", TITLE = "Meta-Interpretive Learning of Data Transformation Programs", YEAR = 2016, Booktitle = {Proceedings of the 25th International Conference on Inductive Logic Programming}, PAGES = "46--59", PUBLISHER = "Springer-Verlag", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/datacurate.pdf" } @inproceedings{farqmugg:typedmil, AUTHOR = "C. Farquhar and G. Grov A. Cropper and S.H. Muggleton and A. Bundy", TITLE = "Typed meta-interpretive learning for proof strategies", YEAR = 2015, Booktitle = {Short Paper Proceedings of the 25th International Conference on Inductive Logic Programming}, PUBLISHER = "National Institute of Informatics, Tokyo", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/typemilproof.pdf" } @inproceedings{croppmugg:picomp, AUTHOR = "A. Cropper and S.H. Muggleton", TITLE = "Can predicate invention compensate for incomplete background knowledge?", YEAR = 2015, Booktitle = {Thirteenth Scandinavian Conference on Artificial Intelligence (SCAI 2015)}, PUBLISHER = "IOS Press", PAGES = {27--36}, URL = "http://www.doc.ic.ac.uk/\~shm/Papers/scai2015.pdf" } @inproceedings{daimugg:logvis, AUTHOR = "W-Z Dai and S.H. Muggleton and Z-H Zhou", TITLE = "Logical {V}ision: Meta-Interpretive Learning for Simple Geometrical Concepts", YEAR = 2015, Booktitle = {Late Breaking Paper Proceedings of the 25th International Conference on Inductive Logic Programming}, PUBLISHER = "CEUR", PAGES = {1--16}, URL = "http://ceur-ws.org/Vol-1636"} @inproceedings{mugg:metagolAI, AUTHOR = "A. Cropper and S.H. Muggleton", TITLE = "Learning Higher-Order Logic Programs Through Abstraction and Invention", YEAR = 2016, BOOKTITLE = "Proceedings of the 25th International Joint Conference Artificial Intelligence (IJCAI 2016)", PAGES = "1418--1424", PUBLISHER = "IJCAI", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/metafunc.pdf" } @inproceedings{mugg:CompInv, AUTHOR = {U. Schmid and C. Zeller and T. Besold and A. Tamaddoni-Nezhad and S.H. Muggleton}, TITLE = "How does Predicate Invention affect Human Comprehensibility?", YEAR = 2017, BOOKTITLE = "Proceedings of the 26th International Conference on Inductive Logic Programming", PUBLISHER = "Springer-Verlag", EDITOR = "Alessandra Russo and James Cussens", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/compinv.pdf", PAGES = "52--67", ADDRESS = "Berlin"} @inproceedings{mugg:milachallenges, AUTHOR = {S.H. Muggleton}, TITLE = "Meta-Interpretive Learning: achievements and challenges", YEAR = 2017, BOOKTITLE = "Proceedings of the 11th International Symposium on Rule Technologies, RuleML+RR 2017", PUBLISHER = "Springer-Verlag", EDITOR = "Roman Kontchakov and Fariba Sadri", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/rulemlabs.pdf", PAGES = "1--7", NOTE = "LNCS 10364" , ADDRESS = "Berlin"} @inproceedings{mugg:logvismoon, AUTHOR = {W-Z Dai and S.H. Muggleton and J. Wen and A. Tamaddoni-Nezhad and Z-H. Zhou}, TITLE = "Logical Vision: One-Shot Meta-Interpretive Learning from Real Images ", YEAR = 2017, BOOKTITLE = "Proceedings of the 27th International Conference on Inductive Logic Programming", PUBLISHER = "Springer-Verlag", EDITOR = "Nicholas Lachiche and Christel Vrain", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/logvismoon.pdf", PAGES = "46--62", ADDRESS = "Berlin"} @inproceedings{connmugg:predorder, AUTHOR = "H. Conn and S.H. Muggleton", TITLE = "The Effect of Predicate Order on Curriculum Learning in {ILP}", YEAR = 2017, Booktitle = {Late Breaking Paper Proceedings of the 27th International Conference on Inductive Logic Programming}, PUBLISHER = "CEUR", PAGES = {17--21}, URL = "http://ceur-ws.org/Vol-2085"} @inproceedings{mugg:ActiveAgentMIL, AUTHOR = {Celine Hocquette and S.H. Muggleton}, TITLE = "How Much Can Experimental Cost Be Reduced in Active Learning of Agent Strategies?", YEAR = 2018, BOOKTITLE = "Proceedings of the 28th International Conference on Inductive Logic Programming", PUBLISHER = "Springer-Verlag", EDITOR = "Fabrizio Riguzzi and Elena Bellodi and Riccardo Zese", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/activemil.pdf", PAGES = "38--53", ADDRESS = "Berlin"} @inproceedings{patsantzismugg:relevance18, AUTHOR = "S. Patsantzis and S.H. Muggleton", TITLE = "Which background knowledge is relevant?", YEAR = 2018, Booktitle = {Late Breaking Paper Proceedings of the 28th International Conference on Inductive Logic Programming}, PUBLISHER = "CEUR", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/relevance18.pdf"} @inproceedings{mugghoc:botinv, AUTHOR = "C. Hocquette and S.H. Muggleton", TITLE = "Complete Bottom-Up Predicate Invention in Meta-Interpretive Learning", YEAR = 2020, BOOKTITLE = "Proceedings of the 29th International Joint Conference Artificial Intelligence (IJCAI 2020)", PAGES = "2312--2318", PUBLISHER = "IJCAI", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/botinv.pdf" } @inproceedings{muggcrop:ILP30, AUTHOR = "A. Cropper and S. Dumancic and S.H. Muggleton", TITLE = "Turning 30: New Ideas in Inductive Logic Programming", YEAR = 2020, BOOKTITLE = "Proceedings of the 29th International Joint Conference Artificial Intelligence (IJCAI 2020)", PAGES = "4833--4839", PUBLISHER = "IJCAI", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ILP30.pdf" } @inproceedings{muggcrop:holearnpredinv, AUTHOR = "A. Cropper and R. Morel and S.H. Muggleton", TITLE = "Learning Higher-Order Programs through Predicate Invention", YEAR = 2020, BOOKTITLE = "Proceedings of the 34th Conference on Artificial Intelligence (AAAI 2020)", PAGES = "13655--13658", PUBLISHER = "AAAI", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/holearnpredinv.pdf" } @inproceedings{muggdai:neuralmil, AUTHOR = "W-Z. Dai and S.H. Muggleton", TITLE = "Abductive Knowledge Induction From Raw Data", YEAR = 2021, BOOKTITLE = "Proceedings of the 30th Conference on Artificial Intelligence (IJCAI 2021)", PUBLISHER = "IJCAI", PAGES = "1845--1851", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/abdmetarawIJCAI.pdf" } @inproceedings{muggdai:abdoptim, AUTHOR = "Y.X. Huang and W.Z. Dai and L.W. Cai and S.H. Muggleton and Y. Jiang", TITLE = "Fast abductive learning by similarity-based consistency optimization", YEAR = 2021, BOOKTITLE = "Advances in Neural Information Processing Systems", VOLUME = "34", PAGES = "26574--26584", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/NIPSabdopt.pdf"} @inproceedings{muggdai:abdground, AUTHOR = "L.W. Cai and W.Z. Dai and Y.X. Huang and Y.F. Li7 and S.H. Muggleton and Y. Jiang", TITLE = "Abductive Learning with Ground Knowledge Base", YEAR = 2021, BOOKTITLE = "Proceedings of the 30th Conference on Artificial Intelligence (IJCAI 2021)", PAGES = "1815-1821", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/IJCAIabdgrnd.pdf"} @inproceedings{mugg:reduce1, AUTHOR = "S.H. Muggleton", TITLE = "ReDuce: Linear-time Inductive Compression using Greedy Folding", YEAR = 2025, BOOKTITLE = "Proceedings of the 5th International Joint Conference on Learning and Reasoning, IJCLR 2025", NOTE = "In Press", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/Reduce.pdf"} @inproceedings{chess:mug, AUTHOR = "S.H. Muggleton", TITLE = "Inductive acquisition of chess strategies", BOOKTITLE = "Machine Intellience 11", YEAR = 1988, PAGES = "375--390", PUBLISHER = "Oxford University Press"} @inproceedings{ducerev:mug, AUTHOR = "S.H. Muggleton", TITLE = "Inverting the resolution principle", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/mi12.pdf", BOOKTITLE = "Machine Intellience 12", YEAR = 1991, PAGES = "93--104", PUBLISHER = "Oxford University Press"} @incollection{dolmugg:fem, AUTHOR = "B. Dolsak and S.H. Muggleton", TITLE = "The application of {I}nductive {L}ogic {P}rogramming to finite element mesh design", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/fem1.pdf", YEAR = 1992, BOOKTITLE = "Inductive Logic Programming", EDITOR= "S.H. Muggleton", PAGES = "453--472", PUBLISHER = "Academic Press", ADDRESS = "London"} @incollection{muggbain:nonmon, AUTHOR = "M. Bain and S.H. Muggleton", TITLE = "Non-monotonic Learning", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/nonmon.pdf", YEAR = "1991", BOOKTITLE = "Machine Intelligence 12", EDITOR = "D. Michie", PAGES = "105--120", PUBLISHER = "Oxford University Press"} @InCollection(Muggleton93:turing, AUTHOR = "S.H. Muggleton", TITLE = "Logic and learning: {T}uring's legacy", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/tll.pdf", EDITOR = "K. Furukawa and D. Michie and S.H. Muggleton", BOOKTITLE = "Machine Intelligence 13", YEAR = 1994, PUBLISHER = "Oxford University Press", PAGES = "37--56" ) @InCollection(Srin:justify, AUTHOR = "A. Srinivasan and S.H. Muggleton and M. Bain", TITLE = "The justification of logical theories based on data compression", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/mi13just.pdf", EDITOR = "K. Furukawa and D. Michie and S.H. Muggleton", BOOKTITLE = "Machine Intelligence 13", YEAR = 1994, PUBLISHER = "Oxford University Press", PAGES = "87--121" ) @incollection{sternmugg:molbiol, AUTHOR = "M.J.E. Sternberg and J. Hirst and R. Lewis and R.D. King and A. Srinivasan and S.H. Muggleton", TITLE = "Application of Machine Learning to Protein Structure Prediction and Drug Design", YEAR = "1994", PAGES = "1--8", BOOKTITLE = "Advances in Molecular Bioinformatics", EDITOR = "S. Schulze-Kremer", PUBLISHER = "IOS Press"} @incollection{muggsrin:progol, AUTHOR = "S.H. Muggleton", TITLE = "Inverting entailment and {Progol}", YEAR = "1995", BOOKTITLE = "Machine Intelligence 14", EDITOR = "K. Furukawa and D. Michie and S.H. Muggleton", PUBLISHER = "Oxford University Press"} @incollection{bratmugg:appl, AUTHOR = "I. Bratko and S.H. Muggleton and A. Karali\v{c}", TITLE = "Applications of {Inductive Logic Programming}", YEAR = "1998", BOOKTITLE = "Machine Learning and Data Mining", EDITOR = "R.S. Michalski and I. Bratko and M. Kubat", PUBLISHER = "John Wiley and Sons Ltd.", ADDRESS = "Chichester"} @incollection{parsmugg:agents, AUTHOR = "R. Parson and S.H. Muggleton", TITLE = "An experiment with browsers that learn", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/agents.pdf", YEAR = "1998", BOOKTITLE = "Machine Intelligence 15", EDITOR = "K. Furukawa and D. Michie and S.H. Muggleton", PUBLISHER = "Oxford University Press"} @incollection{muggpage:utrees, AUTHOR = "S.H. Muggleton and D. Page", TITLE = "A learnability model for universal representations and its application to top-down induction of decision trees", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/utrees.pdf", YEAR = "1999", BOOKTITLE = "Machine Intelligence 15", EDITOR = "K. Furukawa and D. Michie and S.H. Muggleton", PUBLISHER = "Oxford University Press"} @incollection{mitecs:mug, AUTHOR = "S.H. Muggleton", TITLE = "Inductive {L}ogic {P}rogramming", BOOKTITLE = "The MIT Encyclopedia of the Cognitive Sciences (MITECS)", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/mitecs.pdf", YEAR = "1999", EDITOR = "Robert A. Wilson and Frank C. Keil", PUBLISHER = "MIT Press"} @incollection{mugfirth:progol4.4, AUTHOR = "S.H. Muggleton and J. Firth", TITLE = "{CP}rogol4.4: a tutorial introduction", BOOKTITLE = "Relational Data Mining", EDITOR = "S. Dzeroski and N. Lavrac", PAGES = "160--188", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/progtuttheo.pdf", YEAR = "2001", PUBLISHER = "Springer-Verlag"} @incollection{mugmarginean:lbml, AUTHOR = "S.H. Muggleton and F. Marginean", TITLE = "Logic-based Machine Learning", BOOKTITLE = "Logic-Based Artificial Intelligence", EDITOR = "J. Minker", PAGES = "315--330", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/lbml.pdf", YEAR = "2000", PUBLISHER = "Kluwer"} @incollection{svilp:holmes, AUTHOR = "S.H. Muggleton and H. Lodhi and A. Amini and M.J.E. Sternberg", TITLE = "Support {V}ector {I}nductive {L}ogic {P}rogramming", BOOKTITLE = "Innovations in Machine Learning", EDITOR = "D.E. Holmes and L.C. Jain", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/svilp.pdf", YEAR = "2006", PAGES = "113--135", PUBLISHER = "Springer-Verlag" } @incollection{slptut:getoor, AUTHOR = "S.H. Muggleton and N. Pahlavi", TITLE = "Stochastic Logic Programs: A Tutorial", BOOKTITLE = "Introduction to Statistical Relational Learning", EDITOR = "L. Getoor and B. Taskar", YEAR = "2007", PAGES = "323--338", PUBLISHER = "MIT Press" } @incollection{protslp:deraedt, AUTHOR = "J. Chen and L. Kelley and S.H. Muggleton and M.J.E. Sternberg", TITLE = "Protein Fold Discovery using {S}tochastic {L}ogic {P}rograms", BOOKTITLE = "Probabilistic Inductive Logic Programming", EDITOR = "L. De Raedt and P. Frasconi and K. Kersting and S.H. Muggleton", YEAR = "2007", PAGES = "244--262", PUBLISHER = "Springer-Verlag" } @incollection{slpbehav:deraedt, AUTHOR = "S.H. Muggleton and J. Chen", TITLE = "Comparison of some Probabilistic Logic Models", BOOKTITLE = "Probabilistic Inductive Logic Programming", EDITOR = "L. De Raedt and P. Frasconi and K. Kersting and S.H. Muggleton", YEAR = "2007", PAGES = "305--324", PUBLISHER = "Springer-Verlag" } @incollection{foodwebs:tamaddoni, AUTHOR = "A. Tamaddoni-Nezhad and G. Milani and A. Raybould and S.H. Muggleton and D. Bohan", TITLE = "Construction and Validation of Food Webs Using Logic-Based Machine Learning and Text Mining", BOOKTITLE = "Advances in Ecological Research, Vol. 49", EDITOR = "G. Woodward and D.A. Bohan", YEAR = "2013", PAGES = "224--290", PUBLISHER = "Academic Press", ADDRESS = "Amsterdam", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/foodwebchapter.pdf" } @incollection{agroeco:bohan, AUTHOR = "D. Bohan and A. Raybould and C. Mulder and G. Woodward and A. Tamaddoni-Nezhad and N. Bluthgen and M.J.O Pocock and S.H. Muggleton and D.M. Evans and J. Astegiano and F. Massol and N. Loeuille and S. Petit and S. Macfadyen", TITLE = "Networking Agroecology: Integrating the Diversity of Agroecosystem Interactions", BOOKTITLE = "Advances in Ecological Research, Vol. 49", EDITOR = "G. Woodward and D.A. Bohan", YEAR = "2013", PAGES = "2--67", PUBLISHER = "Academic Press", ADDRESS = "Amsterdam", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/agroeco.pdf" } @incollection{LogBioAbd:atn, AUTHOR = "Alireza Tamaddoni‐Nezhad and Dianhuan Lin and Hiroaki Watanabe and Jianzhong Chen and Stephen Muggleton", TITLE = "Machine Learning of Biological Networks using Abductive {ILP}", BOOKTITLE = "Logical Modeling of Biological Systems", EDITOR = "Luis Fariñas del Cerro, Katsumi Inoue", YEAR = "2014", PAGES = "363--401", PUBLISHER = "Wiley", ADDRESS = "Bognor Regis", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/agroeco.pdf" } @incollection{muggXu:gamestrat, AUTHOR = "S.H. Muggleton and C. Xu", TITLE = "Can {ILP} Learn Complete And Correct Game Strategies?", BOOKTITLE = "Latest Advances in Inductive Logic Programming", EDITOR = "Stephen H. Muggleton and H. Watanabe", YEAR = "2015", PAGES = "3--10", PUBLISHER = "Imperial College Press", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/gamestrat.pdf"} @incollection{muggFidjeland:multiproc, AUTHOR = "A.K. Fidjeland and W. Luk and S.H. Muggleton", TITLE = "Customisable Multi-Processor Acceleration of Inductive Logic Programming", BOOKTITLE = "Latest Advances in Inductive Logic Programming", EDITOR = "Stephen H. Muggleton and H. Watanabe", YEAR = "2015", PAGES = "123--139", PUBLISHER = "Imperial College Press", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/multiproc.pdf"} @incollection{muggPahlavi:hodatalog, AUTHOR = "N. Pahlavi and S.H. Muggleton", TITLE = "Towards Efficient Higher-order Logic Learning in a First-order Datalog Framework", BOOKTITLE = "Latest Advances in Inductive Logic Programming", EDITOR = "Stephen H. Muggleton and H. Watanabe", YEAR = "2015", PAGES = "207--215", PUBLISHER = "Imperial College Press", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/hodatalog.pdf"} @incollection{muggHenderson:funcabstract, AUTHOR = "R.J. Henderson and S.H. Muggleton", TITLE = "Automatic Invention of Functional Abstractions", BOOKTITLE = "Latest Advances in Inductive Logic Programming", EDITOR = "Stephen H. Muggleton and H. Watanabe", YEAR = "2015", PAGES = "217--224", PUBLISHER = "Imperial College Press", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/funcabstract.pdf"} @incollection{muggdai:hlvis, AUTHOR = "S.H. Muggleton and W-Z. Dai", TITLE = "Human-like Computer Vision", BOOKTITLE = "Human-Like Machine Intelligence", EDITOR = "S.H. Muggleton and N. Chater", YEAR = "2021", PAGES = "199--217", PUBLISHER = "Oxford University Press"} @book{mi13, AUTHOR = {K. Furukawa and D. Michie and S.H. Muggleton}, TITLE = {Machine Intelligence 13: machine intelligence and inductive learning}, YEAR = 1994, PUBLISHER = "Oxford University Press", ADDRESS = "Oxford"} @book{mi14, AUTHOR = {K. Furukawa and D. Michie and S.H. Muggleton}, TITLE = {Machine Intelligence 14: machine intelligence and inductive learning}, YEAR = 1995, PUBLISHER = "Oxford University Press", ADDRESS = "Oxford"} @book{mi15, AUTHOR = {K. Furukawa and D. Michie and S.H. Muggleton}, TITLE = {Machine Intelligence 15: machine intelligence and inductive learning}, YEAR = 1999, PUBLISHER = "Oxford University Press", ADDRESS = "Oxford"} @book{mug:thesisbook, TITLE = "Inductive Acquisition of Expert Knowledge", AUTHOR = "S.H. Muggleton", YEAR = 1990, PUBLISHER = "Addision-Wesley", ADDRESS = "Wokingham, England"} @book{mug:thesis, TITLE = "Inductive Acquisition of Expert Knowledge", AUTHOR = "S.H. Muggleton", YEAR = 1986, URL = "http://www.doc.ic.ac.uk/\~shm/Papers/Thesis.pdf", PUBLISHER = "University of Edinburgh", ADDRESS = "Edinburgh"} @book{mugg:ilpbook, TITLE = "Inductive Logic Programming", EDITOR = "S.H. Muggleton", YEAR = 1992, PUBLISHER = "Academic Press"} @book{mugg:ilpw1, TITLE = "Proceedings of the First International Workshop on Inductive Logic Programming", EDITOR = "S.H. Muggleton", YEAR = 1991, PUBLISHER = "University of Porto", ADDRESS = "Porto, Portugal"} @book{mugg:ilpw2, TITLE = "Proceedings of the Second International Workshop on Inductive Logic Programming", EDITOR = "S.H. Muggleton", YEAR = 1992, PUBLISHER = "ICOT", ADDRESS = "Tokyo, Japan"} @book{mugg:ilpw3, TITLE = "Proceedings of the Third International Workshop on Inductive Logic Programming", EDITOR = "S.H. Muggleton", YEAR = 1993, PUBLISHER = "Jozef Stefan Institute", ADDRESS = "Bled, Slovenia"} @book{mugg:ilpw6, TITLE = "Proceedings of the Sixth International Workshop on Inductive Logic Programming", EDITOR = "S.H. Muggleton", YEAR = 1997, PUBLISHER = "Springer-Verlag", NOTE = "LNAI 1314", ADDRESS = "Berlin"} @book{mugg:ilp06, TITLE = "Proceedings of the 16th International Workshop on Inductive Logic Programming", EDITOR = "S.H. Muggleton and R. Otero and A. Tamadonni-Nezhad", YEAR = 2007, PUBLISHER = "Springer-Verlag", NOTE = "LNAI 4455", ADDRESS = "Berlin"} @book{deraedt:pilp, TITLE = "Probabilistic Inductive Logic Programming", EDITOR = "L. De Raedt and P. Frasconi and K. Kersting and S.H. Muggleton", YEAR = 2008, PUBLISHER = "Springer-Verlag", NOTE = "LNAI 4911", ADDRESS = "Berlin"} @book{LodhiMug:sysbio, TITLE = "Elements of Computational Systems Biology", EDITOR = "H.M. Lodhi and S.H. Muggleton", YEAR = 2010, PUBLISHER = "Wiley", ADDRESS = "New Jersey"} @book{mugg:ilp2011, TITLE = "Proceedings of the 21st International Conference on Inductive Logic Programming", EDITOR = "S.H. Muggleton and A. Tamaddoni-Nezhad and F.A. Lisi", YEAR = 2012, PUBLISHER = "Spring-Verlag", SERIES = "LNAI 7207", ADDRESS= "Berlin"} @book{mugg:advilp, TITLE = "Latest Adavances in Inductive Logic Programming", EDITOR = "S.H. Muggleton and H. Watanabe", YEAR = 2015, PUBLISHER = "Imperial College Press", ADDRESS= "London", URL = "http://www.worldscientific.com/worldscibooks/10.1142/p954"} @book{mugg:hlmi, TITLE = "Human-Like Machine Intelligence", EDITOR = "S.H. Muggleton and N. Chater", YEAR = 2021, PUBLISHER = "Oxford University Press", ADDRESS= "Oxford", URL = "https://global.oup.com/academic/product/human-like-machine-intelligence-9780198862536?cc=gb&lang=en&"} @techreport{brazmugg:multiagent, AUTHOR = "P. Brazdil and S.H. Muggleton", TITLE = "Learning to Relate Terms in a Multiple Agent Environment", YEAR = 1990, INSTITUTION = "LIACC", ADDRESS = "Porto, Portugal"} @techreport{mug:cigol1, AUTHOR = "S.H. Muggleton and W. Buntine", TITLE = "Towards constructive induction in first-order predicate calculus", YEAR = 1988, TYPE = "TIRM", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/towards.pdf", INSTITUTION = "The Turing Institute", NUMBER = "88-03", ADDRESS = "Glasgow"} @techreport{mugg:mdl, AUTHOR = "S.H. Muggleton and A. Srinivasan and M. Bain", TITLE = "MDL codes for non-monotonic learning", YEAR = 1991, TYPE = "{TIRM}", NUMBER = "91-049", INSTITUTION = "The Turing Institute", ADDRESS = "Glasgow"} @techreport{mugg:janet, AUTHOR = "S.H. Muggleton", TITLE = "Experimental acquisition of grammar from early reader books", YEAR = 1996, TYPE = "{PRG-TR}", NUMBER = "18--96", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/janet.pdf", INSTITUTION = "Oxford University Computing Laboratory", ADDRESS = "Oxford"} @techreport{srinmugg:backstrep, AUTHOR = "A. Srinivasan and S.H. Muggleton and R.D. King and M.J.E. Sternberg", TITLE = "The effect of background knowledge in Inductive Logic Programming: a case study", YEAR = "1995", NUMBER = "PRG-TR-9-95", INSTITUTION = "Oxford University Computing Laboratory", ADDRESS = "Oxford"} @techreport{muggpage:meta, AUTHOR = "S.H. Muggleton and D. Page", TITLE = "Beyond first-order learning: inductive learning with higher-order logic", YEAR = "1994", NUMBER = "PRG-TR-13-94", INSTITUTION = "Oxford University Computing Laboratory", ADDRESS = "Oxford"} @techreport{muggpage:ulearn, AUTHOR = "S.H. Muggleton and C.D. Page", TITLE = "A Learnability Model for Universal Representations", YEAR = "1994", NUMBER = "PRG-TR-3-94", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ulearn10.pdf", INSTITUTION = "Oxford University Computing Laboratory", ADDRESS = "Oxford"} @misc{muggmichie:eastwest, AUTHOR = "D. Michie and S.H. Muggleton and C.D. Page and D. Page and A. Srinivasan", TITLE = "To the International Computing Community: a new East-West Challenge", YEAR = "1994", URL = "http://www.doc.ic.ac.uk/\~shm/Papers/ml-chall.pdf", INSTITUTION = "Oxford University Computing Laboratory", ADDRESS = "Oxford", NOTE = "Distributed email document available from http://www.doc.ic.ac.uk/\~shm/Papers/ml-chall.pdf"} @techreport{brymug:closedloop, AUTHOR = "C. H. Bryant and S. H. Muggleton", TITLE = "Closed Loop Machine Learning", URL = "ftp://ftp.cs.york.ac.uk/reports/YCS-2000-330.pdf", INSTITUTION = "University of York", YEAR = 2000, NUMBER = "YCS 330", ADDRESS = "Department of Computer Science, Heslington, York, YO10 5DD, UK."} @techreport{brymug:gram:tech, AUTHOR = "S.H. Muggleton and C.H. Bryant and A.Srinivasan and A. Whittaker and S. Topp and C. Rawlings", TITLE = "Are grammatical representations useful for learning from biological sequence data? -- a case study", URL = "ftp://ftp.cs.york.ac.uk/reports/YCS-2000-328.pdf", INSTITUTION = "University of York", YEAR = 2000, NUMBER = "YCS 328", ADDRESS = "Department of Computer Science, Heslington, York, YO10 5DD, UK."} @TechReport{techreport02_7, author = {A Tamaddoni-Nezhad and S Muggleton}, title = {Closed Loop Machine Learning: Reproduction and Evaluation of Phase {A} Results}, institution = {Department of Computing, Imperial College London}, year = {2002}, number = {2002/7} } @TechReport{techreport02_8, author = {A Tamaddoni-Nezhad and S Muggleton}, title = {Closed Loop Machine Learning: Complexity of {ASE}-Progol}, institution = {Department of Computing, Imperial College London}, year = {2002}, number = {2002/8} }