Stephen Muggleton's Publications

     
    [1]
    L. Ai, J. Langer, S.H. Muggleton, and U. Schmid. Explanatory machine learning for sequential human teaching. Machine Learning, 112:3591-3632, 2023.

    [2]
    S.H. Muggleton. Hypothesising an algorithm from one example: the role of specificity. Philosophical Transaction of the Royal Society A, 381:20220046, 2023.

    [3]
    S. Patsantzis and S.H. Muggleton. Meta-interpretive learning as metarule specialisation. Machine Learning, 111:3703-3731, 2022.

    [4]
    L. Ai, S.H. Muggleton, C. Hocquette, M. Gromowski, and U. Schmid. Beneficial and harmful explanatory machine learning. Machine Learning, 110:695-721, 2021.

    [5]
    L.W. Cai, W.Z. Dai, Y.X. Huang, Y.F. Li7, S.H. Muggleton, and Y. Jiang. Abductive learning with ground knowledge base. In Proceedings of the 30th Conference on Artificial Intelligence (IJCAI 2021), pages 1815-1821, 2021.

    [6]
    A. Cropper, S. Dumancic, Richard Evans, and S.H. Muggleton. Inductive Logic Programming at 30. Machine Learning, 111:147-172, 2021.

    [7]
    W-Z. Dai and S.H. Muggleton. Abductive knowledge induction from raw data. In Proceedings of the 30th Conference on Artificial Intelligence (IJCAI 2021), pages 1845-1851. IJCAI, 2021.

    [8]
    Y.X. Huang, W.Z. Dai, L.W. Cai, S.H. Muggleton, and Y. Jiang. Fast abductive learning by similarity-based consistency optimization. In Advances in Neural Information Processing Systems, volume 34, pages 26574-26584, 2021.

    [9]
    S.H. Muggleton and N. Chater, editors. Human-Like Machine Intelligence. Oxford University Press, Oxford, 2021.

    [10]
    S.H. Muggleton and W-Z. Dai. Human-like computer vision. In S.H. Muggleton and N. Chater, editors, Human-Like Machine Intelligence, pages 199-217. Oxford University Press, 2021.

    [11]
    S. Patsantzis and S.H. Muggleton. Top program construction and reduction for polynomial time meta-interpretive learning. Machine Learning, 110:755-778, 2021.

    [12]
    A. Cropper, S. Dumancic, and S.H. Muggleton. Turning 30: New ideas in inductive logic programming. In Proceedings of the 29th International Joint Conference Artificial Intelligence (IJCAI 2020), pages 4833-4839. IJCAI, 2020.

    [13]
    A. Cropper, R. Morel, and S.H. Muggleton. Learning higher-order logic programs. Machine Learning, 109:1289-1322, 2020.

    [14]
    A. Cropper, R. Morel, and S.H. Muggleton. Learning higher-order programs through predicate invention. In Proceedings of the 34th Conference on Artificial Intelligence (AAAI 2020), pages 13655-13658. AAAI, 2020.

    [15]
    C. Hocquette and S.H. Muggleton. Complete bottom-up predicate invention in meta-interpretive learning. In Proceedings of the 29th International Joint Conference Artificial Intelligence (IJCAI 2020), pages 2312-2318. IJCAI, 2020.

    [16]
    A. Cropper and S.H. Muggleton. Learning efficient logic programs. Machine Learning, 108:1063-1083, 2019.

    [17]
    S.H. Muggleton and C. Hocquette. Machine discovery of comprehensible strategies for simple games using meta-interpretive learning. New Generation Computing, 37:203-217, 2019.

    [18]
    Celine Hocquette and S.H. Muggleton. How much can experimental cost be reduced in active learning of agent strategies?. In Fabrizio Riguzzi, Elena Bellodi, and Riccardo Zese, editors, Proceedings of the 28th International Conference on Inductive Logic Programming, pages 38-53, Berlin, 2018. Springer-Verlag.

    [19]
    S.H. Muggleton, W-Z. Dai, C. Sammut, A. Tamaddoni-Nezhad, J. Wen, and Z-H. Zhou. Meta-interpretive learning from noisy images. Machine Learning, 107:1097-1118, 2018.

    [20]
    S.H. Muggleton, U. Schmid, C. Zeller, A. Tamaddoni-Nezhad, and T. Besold. Ultra-strong machine learning - comprehensibility of programs learned with ILP. Machine Learning, 107:1119-1140, 2018.

    [21]
    S. Patsantzis and S.H. Muggleton. Which background knowledge is relevant?. In Late Breaking Paper Proceedings of the 28th International Conference on Inductive Logic Programming. CEUR, 2018.

    [22]
    H. Conn and S.H. Muggleton. The effect of predicate order on curriculum learning in ILP. In Late Breaking Paper Proceedings of the 27th International Conference on Inductive Logic Programming, pages 17-21. CEUR, 2017.

    [23]
    W-Z Dai, S.H. Muggleton, J. Wen, A. Tamaddoni-Nezhad, and Z-H. Zhou. Logical vision: One-shot meta-interpretive learning from real images. In Nicholas Lachiche and Christel Vrain, editors, Proceedings of the 27th International Conference on Inductive Logic Programming, pages 46-62, Berlin, 2017. Springer-Verlag.

    [24]
    S.H. Muggleton. Meta-interpretive learning: achievements and challenges. In Roman Kontchakov and Fariba Sadri, editors, Proceedings of the 11th International Symposium on Rule Technologies, RuleML+RR 2017, pages 1-7, Berlin, 2017. Springer-Verlag. LNCS 10364.

    [25]
    U. Schmid, C. Zeller, T. Besold, A. Tamaddoni-Nezhad, and S.H. Muggleton. How does predicate invention affect human comprehensibility?. In Alessandra Russo and James Cussens, editors, Proceedings of the 26th International Conference on Inductive Logic Programming, pages 52-67, Berlin, 2017. Springer-Verlag.

    [26]
    A. Cropper and S.H. Muggleton. Learning higher-order logic programs through abstraction and invention. In Proceedings of the 25th International Joint Conference Artificial Intelligence (IJCAI 2016), pages 1418-1424. IJCAI, 2016.

    [27]
    A. Cropper, A. Tamaddoni-Nezhad, , and S.H. Muggleton. Meta-interpretive learning of data transformation programs. In Proceedings of the 25th International Conference on Inductive Logic Programming, pages 46-59. Springer-Verlag, 2016.

    [28]
    A. Cropper and S.H. Muggleton. Can predicate invention compensate for incomplete background knowledge?. In Thirteenth Scandinavian Conference on Artificial Intelligence (SCAI 2015), pages 27-36. IOS Press, 2015.

    [29]
    A. Cropper and S.H. Muggleton. Learning efficient logical robot strategies involving composable objects. In Proceedings of the 24th International Joint Conference Artificial Intelligence (IJCAI 2015), pages 3423-3429. IJCAI, 2015.

    [30]
    A. Cropper and S.H. Muggleton. Logical minimisation of meta-rules within meta-interpretive learning. In Proceedings of the 24th International Conference on Inductive Logic Programming, pages 65-78. Springer-Verlag, 2015. LNAI 9046.

    [31]
    W-Z Dai, S.H. Muggleton, and Z-H Zhou. Logical Vision: Meta-interpretive learning for simple geometrical concepts. In Late Breaking Paper Proceedings of the 25th International Conference on Inductive Logic Programming, pages 1-16. CEUR, 2015.

    [32]
    C. Farquhar, G. Grov A. Cropper, S.H. Muggleton, and A. Bundy. Typed meta-interpretive learning for proof strategies. In Short Paper Proceedings of the 25th International Conference on Inductive Logic Programming. National Institute of Informatics, Tokyo, 2015.

    [33]
    A.K. Fidjeland, W. Luk, and S.H. Muggleton. Customisable multi-processor acceleration of inductive logic programming. In Stephen H. Muggleton and H. Watanabe, editors, Latest Advances in Inductive Logic Programming, pages 123-139. Imperial College Press, 2015.

    [34]
    S. Gulwani, J. Hernandez-Orallo, E. Kitzelmann, S.H. Muggleton, U. Schmid, and B. Zorn. Inductive programming meets the real world. Communications of the ACM, 58(11):90-99, 2015.

    [35]
    R.J. Henderson and S.H. Muggleton. Automatic invention of functional abstractions. In Stephen H. Muggleton and H. Watanabe, editors, Latest Advances in Inductive Logic Programming, pages 217-224. Imperial College Press, 2015.

    [36]
    S.H. Muggleton and H. Watanabe, editors. Latest Adavances in Inductive Logic Programming. Imperial College Press, London, 2015.

    [37]
    S.H. Muggleton and C. Xu. Can ILP learn complete and correct game strategies?. In Stephen H. Muggleton and H. Watanabe, editors, Latest Advances in Inductive Logic Programming, pages 3-10. Imperial College Press, 2015.

    [38]
    S.H. Muggleton, D. Lin, and A. Tamaddoni-Nezhad. Meta-interpretive learning of higher-order dyadic datalog: Predicate invention revisited. Machine Learning, 100(1):49-73, 2015.

    [39]
    N. Pahlavi and S.H. Muggleton. Towards efficient higher-order logic learning in a first-order datalog framework. In Stephen H. Muggleton and H. Watanabe, editors, Latest Advances in Inductive Logic Programming, pages 207-215. Imperial College Press, 2015.

    [40]
    C.R. Reynolds, S.H. Muggleton, and M.J.E. Sternberg. Incorporating virtual reactions into a logic-based ligand-based virtual screening method to discover new leads. Molecular Informatics, 2015. DOI: 10.1002/minf.201400162.

    [41]
    A. Tamaddoni-Nezhad, D. Bohan, A. Raybould, and S.H. Muggleton. Towards machine learning of predictive models from ecological data. In Proceedings of the 24th International Conference on Inductive Logic Programming, pages 159-173. Springer-Verlag, 2015. LNAI 9046.

    [42]
    D. Lin, E. Dechter, K. Ellis, J.B. Tenenbaum, and S.H. Muggleton. Bias reformulation for one-shot function induction. In Proceedings of the 23rd European Conference on Artificial Intelligence (ECAI 2014), pages 525-530, Amsterdam, 2014. IOS Press.

    [43]
    S. H. Muggleton. Alan Turing and the development of Artificial Intelligence. AI Communications, 27(1):3-10, 2014.

    [44]
    S.H. Muggleton, D. Lin, J. Chen, and A. Tamaddoni-Nezhad. Metabayes: Bayesian meta-interpretative learning using higher-order stochastic refinement. In Gerson Zaverucha, Vitor Santos Costa, and Aline Marins Paes, editors, Proceedings of the 23rd International Conference on Inductive Logic Programming (ILP 2013), pages 1-17, Berlin, 2014. Springer-Verlag. LNAI 8812.

    [45]
    S.H. Muggleton, D. Lin, N. Pahlavi, and A. Tamaddoni-Nezhad. Meta-interpretive learning: application to grammatical inference. Machine Learning, 94:25-49, 2014.

    [46]
    D. Bohan, A. Raybould, C. Mulder, G. Woodward, A. Tamaddoni-Nezhad, N. Bluthgen, M.J.O Pocock, S.H. Muggleton, D.M. Evans, J. Astegiano, F. Massol, N. Loeuille, S. Petit, and S. Macfadyen. Networking agroecology: Integrating the diversity of agroecosystem interactions. In G. Woodward and D.A. Bohan, editors, Advances in Ecological Research, Vol. 49, pages 2-67. Academic Press, Amsterdam, 2013.

    [47]
    S.H. Muggleton and D. Lin. Meta-interpretive learning of higher-order dyadic datalog: Predicate invention revisited. In Proceedings of the 23rd International Joint Conference Artificial Intelligence (IJCAI 2013), pages 1551-1557, 2013.

    [48]
    M.J.E. Sternberg, A. Tamaddoni-Nezhad, V.I.. Lesk, , E. Kay, P.G. Hitchen, A. Cootes, L.B. Alphen, M.P. Lamoureux, H.C. Jarrell, C.J. Rawlings, E.C. Soo, C.M. Szymanski, A. Dell, B.W. Wren, and S.H. Muggleton. Gene function hypotheses for the campylobacter jejuni glycome generated by a logic-based approach. Journal of Moleular Biology, 425(1):186-197, 2013.

    [49]
    A. Tamaddoni-Nezhad, G. Milani, A. Raybould, S.H. Muggleton, and D. Bohan. Construction and validation of food webs using logic-based machine learning and text mining. In G. Woodward and D.A. Bohan, editors, Advances in Ecological Research, Vol. 49, pages 224-290. Academic Press, Amsterdam, 2013.

    [50]
    A. Tamaddoni-Nezhad, G. Afroozi Milani, A. Raybould, S. Muggleton, and D.Bohan. Construction and validation of food-webs using logic-based machine learning and text-mining. Advances in Ecological Research, 49:225-289, 2013.

    [51]
    D. Lin, J. Chen, H. Watanabe, S.H. Muggleton, P. Jain, M. Sternberg, C. Baxter, R. Currie, S. Dunbar, M. Earll, and D. Salazar. Does multi-clause learning help in real-world applications?. In Stephen H. Muggleton, Alireza Tamaddoni-Nezhad, and Francesca A. Lisi, editors, Proceedings of the 21st International Conference on Inductive Logic Programming (ILP 2011), LNAI 7207, pages 221-237, Berlin, 2012. Springer-Verlag.

    [52]
    S.H. Muggleton, D. Lin, and A. Tamaddoni-Nezhad. MC-Toplog: Complete multi-clause learning guided by a top theory. In Stephen H. Muggleton, Alireza Tamaddoni-Nezhad, and Francesca A. Lisi, editors, Proceedings of the 21st International Conference on Inductive Logic Programming (ILP 2011), LNAI 7207, pages 238-254, Berlin, 2012. Springer-Verlag.

    [53]
    S.H. Muggleton, A. Tamaddoni-Nezhad, and F.A. Lisi, editors. Proceedings of the 21st International Conference on Inductive Logic Programming. LNAI 7207. Spring-Verlag, Berlin, 2012.

    [54]
    N. Pahlavi and S.H. Muggleton. Towards efficient higher-order logic learning in a first-order datalog framework. In Latest Advances in Inductive Logic Programming, pages 209-216. Imperial College Press, 2012.

    [55]
    C.R. Reynolds, S.H. Muggleton, and M.J.E. Sternberg. Assessment of a rule-based virtual screening technology (inddex) on a benchmark data set. The Journal of Physical Chemistry B, 116(23), 2012.

    [56]
    J.C.A. Santos, H. Nassif, and S.H. Muggleton M.J.E. Sternberg C.D. Page. Automated identification of protein-ligand interaction features using inductive logic programming: A hexose binding case study. BMC Bioinformatics, 13(162), 2012.

    [57]
    A. Tamaddoni-Nezhad, D. Bohan, A. Raybould, and S.H. Muggleton. Machine learning a probabilistic network of ecological interactions. In Stephen H. Muggleton, Alireza Tamaddoni-Nezhad, and Francesca A. Lisi, editors, Proceedings of the 21st International Conference on Inductive Logic Programming (ILP 2011), LNAI 7207, pages 332-346, Berlin, 2012. Springer-Verlag.

    [58]
    D.A. Bohan, G. Caron-Lormier, S.H. Muggleton, A. Raybould, and A. Tamaddoni-Nezhad. Automated discovery of food webs from ecological data using logic-based machine learning. PloS ONE, 6(12), 2011.

    [59]
    V. Lesk, J. Taubert, C. Rawlings, and S. Dunbarand S.H. Muggleton. WIBL: Workbench for integrative biological learning. Journal of Integrative Bioinformatics, 8(2), 2011.

    [60]
    S.H. Muggleton, J. Chen, H. Watanabe, S. Dunbar, C. Baxter, R. Currie, J.D. Salazar, J. Taubert, and M.J.E. Sternberg. Variation of background knowledge in an industrial application of ILP. In Paolo Frasconi and Francesca A. Lisi, editors, Proceedings of the 20th International Conference on Inductive Logic Programming (ILP 2011), LNAI 6489, pages 158-170, Berlin, 2011. Springer-Verlag.

    [61]
    S.H. Muggleton, L. De Raedt, D. Poole, I. Bratko, P. Flach, and K. Inoue. ILP turns 20: biography and future challenges. Machine Learning, 86(1):3-23, 2011.

    [62]
    N. Pahlavi and S.H. Muggleton. Can HOLL outperform FOLL? In Paolo Frasconi and Francesca A. Lisi, editors, Proceedings of the 20th International Conference on Inductive Logic Programming (ILP 2010), LNAI 6489, pages 198-205, Berlin, 2011. Springer-Verlag.

    [63]
    A. Tamaddoni-Nezhad and S.H. Muggleton. Stochastic refinement. In Paolo Frasconi and Francesca A. Lisi, editors, Proceedings of the 20th International Conference on Inductive Logic Programming (ILP 2010), LNAI 6489, pages 222-237, Berlin, 2011. Springer-Verlag.

    [64]
    H.M. Lodhi and S.H. Muggleton, editors. Elements of Computational Systems Biology. Wiley, New Jersey, 2010.

    [65]
    H. Lodhi, S.H. Muggleton, and M.J.E. Sternberg. Multi-class mode of action classification of toxic compounds using logic based kernel methods. Molecular Informatics, pages 655-664, 2010.

    [66]
    H. Lodhi, S.H. Muggleton, and M.J.E. Sternberg. Multi-class protein fold recognition using large margin logic based divide and conquer learning. SIGKDD Exploration, 11(2):117-122, 2010.

    [67]
    J. Chen S.H. Muggleton. Decision-theoretic logic programs. 2010. 19th International Conference on Inductive Logic Programming, Poster presentation.

    [68]
    S.H. Muggleton, A. Paes, V. Santos Costa, and G. Zaverucha. Chess revision: acquiring the rules of chess variants through FOL theory revision from examples. In Luc De Raedt, editor, Proceedings of the 19th International Conference on Inductive Logic Programming (ILP 2009), LNCS 5989, pages 123-130, Berlin, 2010. Springer-Verlag.

    [69]
    S.H. Muggleton, J. Santos, and A. Tamaddoni-Nezhad. ProGolem: a system based on relative minimal generalisation. In Proceedings of the 19th International Conference on Inductive Logic Programming, LNCS 5989, pages 131-148. Springer-Verlag, 2010.

    [70]
    S.H. Muggleton, J. Santos, and A. Tamaddoni-Nezhad. TopLog: ILP using a logic program declarative bias. In Proceedings of the International Conference on Logic Programming 2008, LNCS 5366, pages 687-692. Springer-Verlag, 2010.

    [71]
    H. Watanabe and S.H. Muggleton. Can ILP be applied to large datasets?. In Luc De Raedt, editor, Proceedings of the 19th International Conference on Inductive Logic Programming (ILP 2009), LNAI 5989, pages 249-256, Berlin, 2010. Springer-Verlag.

    [72]
    H. Lodhi, S.H. Muggleton, and M.J.E. Sternberg. Learning large margin first order decision lists for multi-class classification. In Proceedings of the 12th International Conference on Discovery Science, LNAI 5808, pages 163-183. Springer-Verlag, 2009.

    [73]
    J. Santos, A. Tamaddoni-Nezhad, and S.H. Muggleton. An ILP system for learning head output connected predicates. In Proceedings of the 14th Portuguese Conference on Artificial Intelligence, LNAI 5816, pages 150-159. Springer-Verlag, 2009.

    [74]
    A. Tamaddoni-Nezhad and S.H. Muggleton. The lattice structure and refinement operators for the hypothesis space bounded by a bottom clause. Machine Learning, 76(1):37-72, 2009. 10.1007/s10994-009-5117-7.

    [75]
    J-W. Bang, D.J. Crockford, E. Holmes, F. Pazos, M.J.E. Sternberg, S.H. Muggleton, and J.K. Nicholson. Integrative top-down system metabolic modeling in experimental disease states via data-driven bayesian methods. Journal of Proteome Research, 7(2):497-503, 2008.

    [76]
    J. Chen, S.H. Muggleton, and J. Santos. Learning probabilistic logic models from probabilistic examples. Machine Learning, 73(1):55-85, 2008.

    [77]
    T. Dietterich, P. Domingos, L. Getoor, S.H. Muggleton, and P. Tadepalli. Structured machine learning: the next ten years. Machine Learning, 73(1):3-23, 2008.

    [78]
    A. Fidjeland, W. Luk, and S.H. Muggleton. A customisable multiprocessor for application-optimised inductive logic programming. In Proceedings of Visions of Computer Science - BCS International Academic Conference, LNAI 5816. British Computer Society, 2008.

    [79]
    L. De Raedt, P. Frasconi, K. Kersting, and S.H. Muggleton, editors. Probabilistic Inductive Logic Programming. Springer-Verlag, Berlin, 2008. LNAI 4911.

    [80]
    A. Tamaddoni-Nezhad and S.H. Muggleton. A note on refinement operators for IE-based ILP systems. In Proceedings of the 18th International Conference on Inductive Logic Programming, LNAI 5194, pages 297-314. Springer-Verlag, 2008. DOI: 10.1007/978-3-540-85928-4_23.

    [81]
    K. Tsunoyama, A. Amini, M.J.E. Sternberg, and S.H. Muggleton. Scaffold hopping in drug discovery using inductive logic programming. Journal of Chemical Information and Modelling, 48(5):949-957, 2008.

    [82]
    A. Amini, H. Lodhi S.H. Muggleton, and M.J.E. Sternberg. A novel logic-based approach for quantitative toxicology prediction. Journal of Chemical Information and Modelling, 47(3):998-1006, 2007.

    [83]
    A. Amini, P.J. Shrimpton, S.H. Muggleton, and M.J.E. Sternberg. A general approach for developing system-specific functions to score protein-ligand docked complexes using support vector inductive logic programming. Proteins, 69(4):823-831, 2007. DOI: 10.1002/prot.21782.

    [84]
    E.O. Cannon, A. Amini, A. Bender, M. J. E. Sternberg, S.H. Muggleton, R.C. Glen, and J.B.O. Mitchell. Support vector inductive logic programming outperforms the naive Bayes classifier and inductive logic programming for the classification of bioactive chemical compounds. Journal of Computer Aided Molecular Design, 21:269-280, 2007.

    [85]
    J. Chen, L. Kelley, S.H. Muggleton, and M.J.E. Sternberg. Protein fold discovery using Stochastic Logic Programs. In L. De Raedt, P. Frasconi, K. Kersting, and S.H. Muggleton, editors, Probabilistic Inductive Logic Programming, pages 244-262. Springer-Verlag, 2007.

    [86]
    J. Chen, S.H. Muggleton, and J. Santos. Learning probabilistic learning models from examples (extended abstract). In Proceedings of the 17th International Conference on Inductive Logic Programming, LNAI 4894, pages 22-23. Springer-Verlag, 2007.

    [87]
    A.P. Cootes, S.H. Muggleton, and M.J.E. Sternberg. The identification of similarities between biological networks: Application to the metabolome and interactome prediction. Journal of Molecular Biology, 369(4):1126-1139, 2007. DOI: 10.1016/j.jmb.2007.03.013.

    [88]
    S.H. Muggleton and J. Chen. Comparison of some probabilistic logic models. In L. De Raedt, P. Frasconi, K. Kersting, and S.H. Muggleton, editors, Probabilistic Inductive Logic Programming, pages 305-324. Springer-Verlag, 2007.

    [89]
    S.H. Muggleton and N. Pahlavi. Stochastic logic programs: A tutorial. In L. Getoor and B. Taskar, editors, Introduction to Statistical Relational Learning, pages 323-338. MIT Press, 2007.

    [90]
    S.H. Muggleton and A. Tamaddoni-Nezhad. QG/GA: A stochastic search for Progol. Machine Learning, 70(2-3):123-133, 2007.

    [91]
    S.H. Muggleton, R. Otero, and A. Tamadonni-Nezhad, editors. Proceedings of the 16th International Workshop on Inductive Logic Programming. Springer-Verlag, Berlin, 2007. LNAI 4455.

    [92]
    A. Tamaddoni-Nezhad, R. Chaleil, A. Kakas, M.J.E. Sternberg, J. Nicholson, and S.H. Muggleton. Modeling the effects of toxins in metabolic networks. IEEE Engineering in Medicine and Biology, 26:37-46, 2007.

    [93]
    A. Arvanitis, S.H. Muggleton, J. Chen, and H. Watanabe. Abduction with stochastic logic programs based on a possible worlds semantics. In Short Paper Proceedings of the 16th International Conference on Inductive Logic Programming. University of Corunna, 2006.

    [94]
    J. Chen and S.H. Muggleton. Multi-class protein fold prediction using stochastic logic programs. In Short Paper Proceedings of the 16th International Conference on Inductive Logic Programming. University of Corunna, 2006.

    [95]
    J. Chen and S.H. Muggleton. A revised comparison of Bayesian logic programs and stochastic logic programs. In Short Paper Proceedings of the 16th International Conference on Inductive Logic Programming. University of Corunna, 2006.

    [96]
    S. Colton and S.H. Muggleton. Mathematical applications of inductive logic programming. Machine Learning, 64:25-64, 2006. DOI: 10.1007/s10994-006-8259-x.

    [97]
    S.H. Muggleton. Exceeding human limits. Nature, 440(7083):409-410, 2006.

    [98]
    S.H. Muggleton. Towards Universal Chemical Turing Machines. In Proceedings of the Twenty-First National Conference on Artificial Intelligence, AAAI-06, pages 1527-1529. AAAI Press, 2006.

    [99]
    S.H. Muggleton and N. Pahlavi. The complexity of translating blps to rmms. In Short Paper Proceedings of the 16th International Conference on Inductive Logic Programming. University of Corunna, 2006.

    [100]
    S.H. Muggleton and A. Tamaddoni-Nezhad. QG/GA: A stochastic search approach for Progol. In Proceedings of the 16th International Conference on Inductive Logic Programming, LNAI 4455, pages 37-39. Springer-Verlag, 2006.

    [101]
    S.H. Muggleton, H. Lodhi, A. Amini, and M.J.E. Sternberg. Support Vector Inductive Logic Programming. In D.E. Holmes and L.C. Jain, editors, Innovations in Machine Learning, pages 113-135. Springer-Verlag, 2006.

    [102]
    R. Otero and S.H. Muggleton. On McCarthy's appearance and reality problem. In Short Paper Proceedings of the 16th International Conference on Inductive Logic Programming. University of Corunna, 2006.

    [103]
    A. Tamaddoni-Nezhad, R. Chaleil, A. Kakas, and S.H. Muggleton. Application of abductive ILP to learning metabolic network inhibition from temporal data. Machine Learning, 64:209-230, 2006.

    [104]
    A. Tamaddoni-Nezhad, R. Greaves, and S.H. Muggleton. Large-scale online learning using analogical prediction. In Short Paper Proceedings of the 16th International Conference on Inductive Logic Programming. University of Corunna, 2006.

    [105]
    H. Watanabe, K. Inoue, and Stephen Muggleton. Complexity analysis of abductive action theory. In Short Paper Proceedings of the 16th International Conference on Inductive Logic Programming. University of Corunna, 2006.

    [106]
    S.H. Muggleton. Machine learning for systems biology. In Proceedings of the 15th International Conference on Inductive Logic Programming, LNAI 3625, pages 416-423. Springer-Verlag, 2005.

    [107]
    S.H. Muggleton, H. Lodhi, A. Amini, and M.J.E. Sternberg. Support Vector Inductive Logic Programming. In Proceedings of the 8th International Conference on Discovery Science, LNAI 3735, pages 163-175. Springer-Verlag, 2005.

    [108]
    A. Tamaddoni-Nezhad, R. Chaleil, A. Kakas, and S.H. Muggleton. Abduction and induction for learning models of inhibition in metabolic networks. In Proceedings of the Fourth International Conference on Machine Learning and Applications, ICMLA'05. IEEE Computer Society, 2005.

    [109]
    H. Watanabe and S.H. Muggleton. Learning Stochastic Logical Automaton. In Proceedings of the 19th Annual Conferences of JSAI, LNCS 4012, pages 201-211. Springer-Verlag, 2005.

    [110]
    R.D. King, K.E. Whelan, F.M. Jones, P.K.G. Reiser, C.H. Bryant, S.H. Muggleton, D.B. Kell, and S.G. Oliver. Functional genomic hypothesis generation and experimentation by a robot scientist. Nature, 427:247-252, 2004.

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    H. Lodhi and S.H. Muggleton. Modelling metabolic pathways using stochastic logic programs-based ensemble methods. In Proceedings of the 2nd International Conference on Computational Methods in System Biology. Springer-Verlag, 2004.

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    A. Tamaddoni-Nezhad, A. Kakas, S.H. Muggleton, and F. Pazos. Modelling inhibition in metabolic pathways through abduction and induction. In Proceedings of the 14th International Conference on Inductive Logic Programming, LNAI 3194, pages 305-322. Springer-Verlag, 2004.

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    S. Colton and S.H. Muggleton. ILP for mathematical discovery. In Proceedings of the 13th International Conference on Inductive Logic Programming, LNAI 2835, pages 93-111. Springer-Verlag, 2003.

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    A. Cootes, S.H. Muggleton, and M.J.E. Sternberg. The automatic discovery of structural principles describing protein fold space. Journal of Molecular Biology, 330(4):839-850, 2003.

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    A. Cootes, S.H. Muggleton, and M.J.E. Sternberg. The identification of similarities between biological networks: Application to the metabolome and interactome. Journal of Molecular Biology, 369:1126-1139, 2003.

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    S.H. Muggleton, A. Tamaddoni-Nezhad, and H. Watanabe. Induction of enzyme classes from biological databases. In Proceedings of the 13th International Conference on Inductive Logic Programming, LNAI 2835, pages 269-280. Springer-Verlag, 2003.

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    A. Puech and S.H. Muggleton. A comparison of stochastic logic programs and Bayesian logic programs. In IJCAI03 Workshop on Learning Statistical Models from Relational Data. IJCAI, 2003.

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    M.J.E. Sternberg and S.H. Muggleton. Structure activity relationships (SAR) and pharmacophore discovery using inductive logic programming (ILP). QSAR and Combinatorial Science, 22(5):527-532, 2003.

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    A. Tamaddoni-Nezhad, S.H. Muggleton, and J. Bang. A Bayesian model for metabolic pathways. In International Joint Conference on Artificial Intelligence (IJCAI03) Workshop on Learning Statistical Models from Relational Data, pages 50-57. IJCAI, 2003.

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    S.H. Muggleton A. Fidjeland, W. Luk. Scalable acceleration of inductive logic programs. In IEEE international conference on field-programmable technology, pages 252 -- 259. IEEE, 2002.

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    N. Angelopoulos and S.H. Muggleton. Machine learning metabolic pathway descriptions using a probabilistic relational representation. Electronic Transactions in Artificial Intelligence, 6, 2002.

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    S.H. Muggleton. Learning structure and parameters of stochastic logic programs. Electronic Transactions in Artificial Intelligence, 6, 2002.

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    S.H. Muggleton. Learning structure and parameters of stochastic logic programs. In Proceedings of the 12th International Conference on Inductive Logic Programming, pages 198-206. Springer-Verlag, 2002.

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    A Tamaddoni-Nezhad and S Muggleton. Closed loop machine learning: Complexity of ASE-progol. Technical Report 2002/8, Department of Computing, Imperial College London, 2002.

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    A Tamaddoni-Nezhad and S Muggleton. Closed loop machine learning: Reproduction and evaluation of phase A results. Technical Report 2002/7, Department of Computing, Imperial College London, 2002.

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    A. Tamaddoni-Nezhad and S.H. Muggleton. A genetic algorithms approach to ILP. In Proceedings of the 12th International Conference on Inductive Logic Programming, pages 285-300. Springer-Verlag, 2002.

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    H. Watanabe and S.H. Muggleton. First-order stochastic action languages. Electronic Transactions in Artificial Intelligence, 6, 2002.

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    C.H. Bryant, S.H. Muggleton, S.G. Oliver, D.B. Kell, P. Reiser, and R.D. King. Combining inductive logic programming, active learning and robotics to discover the function of genes. Electronic Transactions in Artificial Intelligence, 5-B1(012):1-36, November 2001.

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    A.P. Cootes, S.H. Muggleton, R.B. Greaves, and M.J. Sternberg. Automatic determination of protein fold signatures from structured superposition. Electronic Transactions in Artificial Intelligence, 6-B2(026):245-274, November 2001.

    [130]
    S.H. Muggleton. Statistical aspects of logic-based machine learning. ACM Transactions on Computational Logic, 2001. Under revision.

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    S.H. Muggleton. Stochastic logic programs. Journal of Logic Programming, 2001. Accepted subject to revision.

    [132]
    S.H. Muggleton and J. Firth. CProgol4.4: a tutorial introduction. In S. Dzeroski and N. Lavrac, editors, Relational Data Mining, pages 160-188. Springer-Verlag, 2001.

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    S.H. Muggleton, C.H. Bryant, A. Srinivasan, A. Whittaker, S. Topp, and C. Rawlings. Are grammatical representations useful for learning from biological sequence data? - a case study. Journal of Computational Biology, 8(5):493-521, 2001.

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    P.G.K. Reiser, R.D. King, D.B. Kell, S.H. Muggleton, C.H. Bryant, and S.G. Oliver. Developing a logical model of yeast metabolism. Electronic Transactions in Artificial Intelligence, 5-B2(024):223-244, November 2001.

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    A. Tamaddoni-Nezhad and S.H. Muggleton. Using genetic algorithms for learning clauses in first-order logic. In Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2001, pages 639-646, San Francisco, CA, 2001. Morgan Kaufmann Publishers.

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    M. Turcotte, S.H. Muggleton, and M.J. Sternberg. Generating protein three-dimensional fold signatures using inductive logic programming. Computational Chemistry, 26:57-64, 2001.

    [137]
    M. Turcotte, S.H. Muggleton, and M.J.E. Sternberg. Automated discovery of structural signatures of protein fold and function. Journal of Molecular Biology, 306:591-605, 2001.

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    M. Turcotte, S.H. Muggleton, and M.J.E. Sternberg. The effect of relational background knowledge on learning of protein three-dimensional fold signatures. Machine Learning, 1,2:81-96, April-May 2001.

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    C. H. Bryant and S. H. Muggleton. Closed loop machine learning. Technical Report YCS 330, University of York, Department of Computer Science, Heslington, York, YO10 5DD, UK., 2000.

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    S.H. Muggleton. Learning stochastic logic programs. Electronic Transactions in Artificial Intelligence, 4(041), 2000.

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    S.H. Muggleton. Learning stochastic logic programs. In Lise Getoor and David Jensen, editors, Proceedings of the AAAI2000 workshop on Learning Statistical Models from Relational Data. AAAI, 2000.

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    S.H. Muggleton. Semantics and derivation for stochastic logic programs. In Richard Dybowski, editor, Proceedings of the UAI2000 workshop on Knowledge-Data Fusion. UAI, 2000.

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    S.H. Muggleton and C.H. Bryant. Theory completion using inverse entailment. In Proc. of the 10th International Workshop on Inductive Logic Programming (ILP-00), pages 130-146, Berlin, 2000. Springer-Verlag.

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    S.H. Muggleton and F. Marginean. Logic-based machine learning. In J. Minker, editor, Logic-Based Artificial Intelligence, pages 315-330. Kluwer, 2000.

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    S.H. Muggleton, C.H. Bryant, A.Srinivasan, A. Whittaker, S. Topp, and C. Rawlings. Are grammatical representations useful for learning from biological sequence data? -- a case study. Technical Report YCS 328, University of York, Department of Computer Science, Heslington, York, YO10 5DD, UK., 2000.

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    S.H. Muggleton, C.H. Bryant, and A. Srinivasan. Learning Chomsky-like grammars for biological sequence families. In Proceedings of the Seventeenth International Conference on Machine Learning, pages 631-638, Stanford University, USA, 2000. San Francisco, CA: Morgan Kaufmann.

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    S.H. Muggleton, C.H.Bryant, and A.Srinivasan. Measuring performance when positives are rare: Relative advantage versus predictive accuracy - a biological case-study. In R.Lopez de Mantaras and E.Plaza, editors, Proceedings of the 11th European Conference on Machine Learning, Lecture Notes in Computer Science, http://www.springer.de/comp/lncs/index.html, 2000. copyright Springer Verlag.

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    A. Tamaddoni-Nezhad and S.H. Muggleton. Searching the subsumption lattice by a genetic algorithm. In J. Cussens and A. Frisch, editors, Proceedings of the 10th International Conference on Inductive Logic Programming, pages 243-252. Springer-Verlag, 2000.

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    C.H.Bryant, S.H. Muggleton, C.D.Page, and M.J.E.Sternberg. Combining Active Learning with Inductive Logic Programming to close the loop in Machine Learning. In S. Colton, editor, Proceedings of AISB'99 Symposium on AI and Scientific Creativity, pages 59-64, http://www.cogs.susx.ac.uk/aisb/, 1999. The Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB).

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    K. Furukawa, D. Michie, and S.H. Muggleton. Machine Intelligence 15: machine intelligence and inductive learning. Oxford University Press, Oxford, 1999.

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    S.H. Muggleton. Inductive Logic Programming. In Robert A. Wilson and Frank C. Keil, editors, The MIT Encyclopedia of the Cognitive Sciences (MITECS). MIT Press, 1999.

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    S.H. Muggleton. Inductive logic programming: issues, results and the LLL challenge. Artificial Intelligence, 114(1-2):283-296, December 1999.

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    S.H. Muggleton. Scientific knowledge discovery using Inductive Logic Programming. Communications of the ACM, 42(11):42-46, November 1999.

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    S.H. Muggleton and M. Bain. Analogical prediction. In Proc. of the 9th International Workshop on Inductive Logic Programming (ILP-99), pages 234-244, Berlin, 1999. Springer-Verlag.

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    S.H. Muggleton and D. Page. A learnability model for universal representations and its application to top-down induction of decision trees. In K. Furukawa, D. Michie, and S.H. Muggleton, editors, Machine Intelligence 15. Oxford University Press, 1999.

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    R. Parson, K. Khan, and S.H. Muggleton. Theory recovery. In Proc. of the 9th International Workshop on Inductive Logic Programming (ILP-99), Berlin, 1999. Springer-Verlag.

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    I. Bratko, S.H. Muggleton, and A. Karalic. Applications of Inductive Logic Programming. In R.S. Michalski, I. Bratko, and M. Kubat, editors, Machine Learning and Data Mining. John Wiley and Sons Ltd., Chichester, 1998.

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    S. Dzeroski, N. Jacobs, M. Molina, C. Moure, S.H. Muggleton, and W. Van Laer. Detecting traffic problems with ILP. In C.D. Page, editor, Proc. of the 8th International Workshop on Inductive Logic Programming (ILP-98), LNAI 1446, pages 281-290, Berlin, 1998. Springer-Verlag.

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    P. Finn, S.H. Muggleton, D. Page, and A. Srinivasan. Pharmacophore discovery using the Inductive Logic Programming system Progol. Machine Learning, 30:241-271, 1998.

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    K. Khan, S.H. Muggleton, and R. Parson. Repeat learning using predicate invention. In C.D. Page, editor, Proc. of the 8th International Workshop on Inductive Logic Programming (ILP-98), LNAI 1446, pages 165-174, Berlin, 1998. Springer-Verlag.

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    S.H. Muggleton. Advances in ILP theory and implementations. In C.D. Page, editor, Proc. of the 8th International Workshop on Inductive Logic Programming (ILP-98), LNAI 1446, page 9, Berlin, 1998. Springer-Verlag. Abstract of keynote presentation.

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    S.H. Muggleton. Completing inverse entailment. In C.D. Page, editor, Proceedings of the Eighth International Workshop on Inductive Logic Programming (ILP-98), LNAI 1446, pages 245-249. Springer-Verlag, Berlin, 1998.

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    S.H. Muggleton. Inductive logic programming: issues, results and the LLL challenge. In H. Prade, editor, Proceedings of ECAI98, page 697. John Wiley, 1998. Abstract of keynote talk.

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    S.H. Muggleton. Knowledge discovery in biological and chemical domains. In H. Motoda, editor, Proc. of the first Conference on Discovery Science, Berlin, 1998. Springer-Verlag. Abstract of keynote talk.

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    S.H. Muggleton, A. Srinivasan, R.D. King, and M.J.E. Sternberg. Biochemical knowledge discovery using Inductive Logic Programming. In H. Motoda, editor, Proc. of the first Conference on Discovery Science, Berlin, 1998. Springer-Verlag.

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    R. Parson and S.H. Muggleton. An experiment with browsers that learn. In K. Furukawa, D. Michie, and S.H. Muggleton, editors, Machine Intelligence 15. Oxford University Press, 1998.

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    S. Roberts, W. Van Laerand, N. Jacobs, S.H. Muggleton, and J. Broughton. A comparison of ILP and propositional systems on propositional data. In C.D. Page, editor, Proc. of the 8th International Workshop on Inductive Logic Programming (ILP-98), LNAI 1446, pages 291-299, Berlin, 1998. Springer-Verlag.

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    M. Turcotte, S.H. Muggleton, and M.J.E. Sternberg. Protein fold recognition. In C.D. Page, editor, Proc. of the 8th International Workshop on Inductive Logic Programming (ILP-98), LNAI 1446, pages 53-64, Berlin, 1998. Springer-Verlag.

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    S. Moyle and S.H. Muggleton. Learning programs in the event calculus. In N. Lavrac and S. Dzeroski, editors, Proceedings of the Seventh Inductive Logic Programming Workshop (ILP97), LNAI 1297, pages 205-212, Berlin, 1997. Springer-Verlag.

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    S.H. Muggleton. Declarative knowledge discovery in industrial databases. In H.F. Arner, editor, Proceedings of the First International Conference and Exhibition on The Practical Application of Knowledge Discovery and Data Mining (PADD-97), pages 9-24. Practical Application Company Ltd., 1997.

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    S.H. Muggleton, editor. Proceedings of the Sixth International Workshop on Inductive Logic Programming. Springer-Verlag, Berlin, 1997. LNAI 1314.

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    A. Srinivasan, , R.D. King S.H. Muggleton, and M.J.E. Sternberg. Carcinogenesis predictions using ILP. In N. Lavrac and S. Dzeroski, editors, Proceedings of the Seventh International Workshop on Inductive Logic Programming, pages 273-287. Springer-Verlag, Berlin, 1997. LNAI 1297.

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    A. Srinivasan, , R.D. King S.H. Muggleton, and M.J.E. Sternberg. The predictive toxicology evaluation challenge. In Proceedings of the Fifteenth International Joint Conference Artificial Intelligence (IJCAI-97), pages 1-6. Morgan-Kaufmann, 1997.

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    R.D. King, S.H. Muggleton, A. Srinivasan, and M.J.E. Sternberg. Structure-activity relationships derived by machine learning: the use of atoms and their bond connectives to predict mutagenicity by inductive logic programming. Proceedings of the National Academy of Sciences, 93:438-442, 1996.

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    S.H. Muggleton. Experimental acquisition of grammar from early reader books. PRG-TR 18-96, Oxford University Computing Laboratory, Oxford, 1996.

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    S.H. Muggleton. Learning from positive data. In S.H. Muggleton, editor, Proceedings of the Sixth International Workshop on Inductive Logic Programming (Workshop-96), LNAI 1314, pages 358-376, Berlin, 1996. Springer-Verlag.

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    S.H. Muggleton. Stochastic logic programs. In L. de Raedt, editor, Advances in Inductive Logic Programming, pages 254-264. IOS Press, 1996.

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    S.H. Muggleton and D. Michie. Machine intelligibility and the duality principle. British Telecom Technology Journal, 14(4):15-23, 1996.

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    S.H. Muggleton, C.D. Page, and A. Srinivasan. An initial experiment into stereochemistry-based drug design using ILP. In S.H. Muggleton, editor, Proceedings of the Sixth Inductive Logic Programming Workshop (ILP96), LNAI 1314, pages 25-40, Berlin, 1996. Springer-Verlag.

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    A. Srinivasan, S.H. Muggleton, R.D. King, and M.J.E. Sternberg. Theories for mutagenicity: a study of first-order and feature based induction. Artificial Intelligence, 85(1,2):277-299, 1996.

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    I. Bratko and S.H. Muggleton. Applications of Inductive Logic Programming. Communications of the ACM, 38(11):65-70, 1995.

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    K. Furukawa, D. Michie, and S.H. Muggleton. Machine Intelligence 14: machine intelligence and inductive learning. Oxford University Press, Oxford, 1995.

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    S.H. Muggleton. Inverse entailment and Progol. New Generation Computing, 13:245-286, 1995.

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    S.H. Muggleton. Inverting entailment and Progol. In K. Furukawa, D. Michie, and S.H. Muggleton, editors, Machine Intelligence 14. Oxford University Press, 1995.

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    A. Srinivasan, S.H. Muggleton, , and R.D. King. Comparing the use of background knowledge by inductive logic programming systems. In L. De Raedt, editor, Proceedings of the Fifth International Inductive Logic Programming Workshop. Katholieke Universteit Leuven, 1995.

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    A. Srinivasan, S.H. Muggleton, R.D. King, and M.J.E. Sternberg. The effect of background knowledge in inductive logic programming: a case study. Technical Report PRG-TR-9-95, Oxford University Computing Laboratory, Oxford, 1995.

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    K. Furukawa, D. Michie, and S.H. Muggleton. Machine Intelligence 13: machine intelligence and inductive learning. Oxford University Press, Oxford, 1994.

    [188]
    John R Koza and James P Rice. Genetic programming II: automatic discovery of reusable programs, volume 40. MIT press Cambridge, 1994.

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    D. Michie, S.H. Muggleton, C.D. Page, D. Page, and A. Srinivasan. To the international computing community: a new east-west challenge, 1994. Distributed email document available from http://www.doc.ic.ac.uk/~shm/Papers/ml-chall.pdf.

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    S.H. Muggleton. Bayesian Inductive Logic Programming. In W. Cohen and H. Hirsh, editors, Proceedings of the Eleventh International Machine Learning Conference, pages 371-379, San Mateo, CA, 1994. Morgan-Kaufmann. Keynote presentation.

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    S.H. Muggleton. Bayesian Inductive Logic Programming. In M. Warmuth, editor, Proceedings of the Seventh Annual ACM Conference on Computational Learning Theory, pages 3-11, New York, 1994. ACM Press. Keynote presentation.

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    S.H. Muggleton. Inductive logic programming: derivations, successes and shortcomings. SIGART Bulletin, 5(1):5-11, 1994.

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    S.H. Muggleton. Logic and learning: Turing's legacy. In K. Furukawa, D. Michie, and S.H. Muggleton, editors, Machine Intelligence 13, pages 37-56. Oxford University Press, 1994.

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    S.H. Muggleton. Predicate invention and utilisation. Journal of Experimental and Theoretical Artificial Intelligence, 6(1):127-130, 1994.

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    S.H. Muggleton and C.D. Page. A learnability model for universal representations. Technical Report PRG-TR-3-94, Oxford University Computing Laboratory, Oxford, 1994.

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    S.H. Muggleton and C.D. Page. Self-saturation of definite clauses. In S. Wrobel, editor, Proceedings of the Fourth International Inductive Logic Programming Workshop, pages 161-174. Gesellschaft fur Mathematik und Datenverarbeitung MBH, 1994. GMD-Studien Nr 237.

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    S.H. Muggleton and D. Page. Beyond first-order learning: inductive learning with higher-order logic. Technical Report PRG-TR-13-94, Oxford University Computing Laboratory, Oxford, 1994.

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    S.H. Muggleton and L. De Raedt. Inductive logic programming: Theory and methods. Journal of Logic Programming, 19,20:629-679, 1994.

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    A. Srinivasan, S.H. Muggleton, and M. Bain. The justification of logical theories based on data compression. In K. Furukawa, D. Michie, and S.H. Muggleton, editors, Machine Intelligence 13, pages 87-121. Oxford University Press, 1994.

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    A. Srinivasan, S.H. Muggleton, R.D. King, and M.J.E. Sternberg. Mutagenesis: ILP experiments in a non-determinate biological domain. In S. Wrobel, editor, Proceedings of the Fourth International Inductive Logic Programming Workshop. Gesellschaft fur Mathematik und Datenverarbeitung MBH, 1994. GMD-Studien Nr 237.

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    M.J.E. Sternberg, J. Hirst, R. Lewis, R.D. King, A. Srinivasan, and S.H. Muggleton. Application of machine learning to protein structure prediction and drug design. In S. Schulze-Kremer, editor, Advances in Molecular Bioinformatics, pages 1-8. IOS Press, 1994.

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    M.J.E. Sternberg, R.D. King, R. Lewis, and S.H. Muggleton. Application of machine learning to structural molecular biology. Philosophical Transactions of the Royal Society B, 344:365-371, 1994.

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    S. Dzeroski, S.H. Muggleton, and S. Russell. Learnability of constrained logic programs. In Proceedings of the European Conference on Machine Learning, pages 342-347, London, UK, 1993. Springer-Verlag.

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    S.H. Muggleton. Optimal layered learning: A PAC approach to incremental sampling. In K. Jantke, S. Kobayashi, E. Tomita, and T. Yokomori, editors, Proceedings of the 4th Conference on Algorithmic Learning Theory, LNAI 744, pages 37-44. Springer-Verlag, 1993.

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    S.H. Muggleton, editor. Proceedings of the Third International Workshop on Inductive Logic Programming. Jozef Stefan Institute, Bled, Slovenia, 1993.

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    B. Dolsak and S.H. Muggleton. The application of Inductive Logic Programming to finite element mesh design. In S.H. Muggleton, editor, Inductive Logic Programming, pages 453-472. Academic Press, London, 1992.

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    S. Dzeroski, S.H. Muggleton, and S. Russell. PAC-learnability of determinate logic programs. In Proceedings of the 5th ACM Workshop on Computational Learning Theory, pages 128-135, New York, NY, 1992. ACM Press.

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    C. Feng and S.H. Muggleton. Towards inductive generalisation in higher order logic. In D. Sleeman and P. Edwards, editors, Proceedings of the Ninth International Workshop on Machine Learning, pages 154-162, San Mateo, CA, 1992. Morgan Kaufmann.

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    R.D. King, S.H. Muggleton, R. Lewis, and M.J.E. Sternberg. Drug design by machine learning: The use of inductive logic programming to model the structure-activity relationships of trimethoprim analogues binding to dihydrofolate reductase. Proceedings of the National Academy of Sciences, 89(23):11322-11326, 1992.

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    S.H. Muggleton. Developments in Inductive Logic Programming. In Proceedings of the International Conference on Fifth Generation Computer Systems 1992, pages 1071-1073, Tokyo, 1992. Ohmsha.

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    S.H. Muggleton, editor. Inductive Logic Programming. Academic Press, 1992.

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    S.H. Muggleton. Inverting implication. In Proceedings of the Second Inductive Logic Programming Workshop, pages 19-39, Tokyo, 1992. ICOT (Technical report TM-1182).

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    S.H. Muggleton, editor. Proceedings of the Second International Workshop on Inductive Logic Programming. ICOT, Tokyo, Japan, 1992.

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    S.H. Muggleton, R.D. King, and M.J.E. Sternberg. Protein secondary structure prediction using logic-based machine learning. Protein Engineering, 5(7):647-657, 1992.

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    S.H. Muggleton, A. Srinivasan, and M. Bain. Compression, significance and accuracy. In D. Sleeman and P. Edwards, editors, Proceedings of the Ninth International Machine Learning Conference, pages 338-347, San Mateo, CA, 1992. Morgan-Kaufmann.

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    A. Srinivasan, S.H. Muggleton, and M. Bain. Distinguishing exceptions from noise in non-monotonic learning. In Proceedings of the Second Inductive Logic Programming Workshop, pages 97-107, Tokyo, 1992. ICOT (Technical report TM-1182).

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    M.J.E. Sternberg, R. Lewis, R.D. King, and S.H. Muggleton. Modelling the structure and function of enzymes by machine learning. Proceedings of the Royal Society of Chemistry: Faraday Discussions, 93:269-280, 1992.

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    M. Bain and S.H. Muggleton. Non-monotonic learning. In D. Michie, editor, Machine Intelligence 12, pages 105-120. Oxford University Press, 1991.

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    I. Bratko, S.H. Muggleton, and A. Varsek. Learning qualitative models of dynamic systems. In Proceedings of the Eighth International Machine Learning Workshop, San Mateo, Ca, 1991. Morgan-Kaufmann.

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    S.H. Muggleton. Inductive Logic Programming. New Generation Computing, 8(4):295-318, 1991.

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    S.H. Muggleton. Inverting the resolution principle. In Machine Intellience 12, pages 93-104. Oxford University Press, 1991.

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    S.H. Muggleton, editor. Proceedings of the First International Workshop on Inductive Logic Programming. University of Porto, Porto, Portugal, 1991.

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    S.H. Muggleton, A. Srinivasan, and M. Bain. Mdl codes for non-monotonic learning. TIRM 91-049, The Turing Institute, Glasgow, 1991.

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    P. Brazdil and S.H. Muggleton. Learning to relate terms in a multiple agent environment. Technical report, LIACC, Porto, Portugal, 1990.

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    S.H. Muggleton. Inductive Acquisition of Expert Knowledge. Addision-Wesley, Wokingham, England, 1990.

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    S.H. Muggleton and C. Feng. Efficient induction of logic programs. In Proceedings of the First Conference on Algorithmic Learning Theory, pages 368-381, Tokyo, 1990. Ohmsha.

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    S.H. Muggleton, M.E. Bain, J. Hayes-Michie, and D. Michie. An experimental comparison of human and machine learning formalisms. In Proceedings of the Sixth International Workshop on Machine Learning, Los Altos, CA, 1989. Kaufmann.

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    S.H. Muggleton. Inductive acquisition of chess strategies. In Machine Intellience 11, pages 375-390. Oxford University Press, 1988.

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    S.H. Muggleton. A strategy for constructing new predicates in first order logic. In Proceedings of the Third European Working Session on Learning, pages 123-130. Pitman, 1988.

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    S.H. Muggleton and W. Buntine. Machine invention of first-order predicates by inverting resolution. In Proceedings of the 5th International Conference on Machine Learning, pages 339-352. Kaufmann, 1988.

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    S.H. Muggleton and W. Buntine. Towards constructive induction in first-order predicate calculus. TIRM 88-03, The Turing Institute, Glasgow, 1988.

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    S.H. Muggleton. Duce, an oracle based approach to constructive induction. In IJCAI-87, pages 287-292. Kaufmann, 1987.

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    S.H. Muggleton. Inductive Acquisition of Expert Knowledge. University of Edinburgh, Edinburgh, 1986.

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    D. Michie, S.H. Muggleton, C. Riese, and S. Zubrick. Rulemaster: a second-generation knowledge-engineering facility. In Proceedings of the First Conference on Artificial Intelligence Applications, pages 591-597. IEEE Computer Soc., 1984.