All paper's cited in Stephen Muggleton's Publications

[1]: 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.
[2]: M. A-Razzak, T. Hassan, and R. Pettipher. Extran-7: A Fortran-based software package for building expert systems. In M.A. Bramer, editor, Research and Development in Expert Systems, pages 23-30. Cambridge University Press, Cambridge, 1984.
[3]: H. Abramson and V. Dahl. Logic Grammars. Springer-Verlag, Berlin, 1989.
[4]: Harvey Abramson. Definite clause translation grammars. Technical report, Vancouver, BC, Canada, Canada, 1984.
[5]: H. Ade, L. De Raedt, and M. Bruynooghe. Theory revision. In S. Muggleton, editor, Proceedings of the 3rd International Workshop on Inductive Logic Programming, pages 179-192, 1993.
[6]: R. Agrawal. Sample mean based index policies with o(log n) regret for the multi-armed bandit problem. Advanced Applied Probability, 27:1054-1078, 1995.
[7]: L. Ai, S.H. Muggleton, C. Hocquette, M. Gromowski, and U. Schmid. Beneficial and harmful explanatory machine learning. Machine Learning, 110:695-721, 2021.
[8]: L. Ai, J. Langer, S.H. Muggleton, and U. Schmid. Explanatory machine learning for sequential human teaching. Machine Learning, 112:3591-3632, 2023.
[9]: James S Aitken. Learning information extraction rules: An inductive logic programming approach. In ECAI, pages 355-359, 2002.
[10]: K. Ali and M. Pazzani. Hydra : a noise tolerant relational concept-learning algorithm. In Proceedings of the 13th International Joint Conference on Artificial Intelligence. Morgan Kaufmann, 1993.
[11]: B.P. Allen. Case-based reasoning: business applications. Communications of the ACM, 37(3):40-44, 1994.
[12]: J.F. Allen. Natural language understanding. Benjamin/Cummings, Menlo Park, CA, 1995.
[13]: E. Alpaydin. Intoduction to Machine Learning. MIT Press, 2004.
[14]: H. Alshawi. The Core Language Engine. M.I.T.Press, 1992.
[15]: A.P. Ambler, H.G. Barrow, C.M. Brown, R.M. Burstall, and R. J. Popplestone. A versatile system for computer controlled assembly. Artificial Intelligence, 6(2):129-156, 1975.
[16]: 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.
[17]: 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.
[18]: B. Andres, B. Kaufmann, O. Matheis, and T. Schaub. Unsatisfiability-based optimization in clasp. In Proceedings of the 28th International Conference on Logic Programming, 2012.
[19]: E. Andrianantoandro, S. Basu, D. Karig, and R. Weiss. Synthetic biology: new engineering rules for an emerging discipline. Molecular Systems Biology, 2(2006:0028), 2006.
[20]: N. Angelopoulos and J. Cussens. Markov chain Monte Carlo using tree-based priors on model structure. In UAI-2001, Los Altos, CA, 2001. Kaufmann.
[21]: N. Angelopoulos and S.H. Muggleton. Machine learning metabolic pathway descriptions using a probabilistic relational representation. Electronic Transactions in Artificial Intelligence, 6, 2002.
[22]: D. Angluin and C.H. Smith. A survey of inductive inference: theory and methods. Computing Surveys, 15(3):237-269, 1983.
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[25]: D. Angluin. A note on the number of queries needed to identify regular languages. Information and Control, 51(1):76-87, 1982.
[26]: D. Angluin. Queries and concept learning. Machine Learning, 2(4):319-342, 1988.
[27]: Martin HG Anthony and Norman Biggs. Computational learning theory. 1997.
[28]: C. Apté, F.J. Damerau, and S.M. Weiss. Automated learning of decision rules for text categorization. ACM Trans on Information Systems, 12:233-251, 1994.
[29]: B. Arbab and D. Michie. Generating rules from examples. In IJCAI-85, pages 631-633, Los Altos, CA, 1985. Kaufmann.
[30]: Brenna D Argall, Sonia Chernova, Manuela Veloso, and Brett Browning. A survey of robot learning from demonstration. Robotics and autonomous systems, 57(5):469-483, 2009.
[31]: J. Arima. Preduction: a common form of induction and analogy. In IJCAI-97, pages 23-29. Morgan Kaufmann, 1997.
[32]: J. Arima. Logical Foundations of Induction and Analogy. PhD thesis, Kyoto University, 1998.
[33]: M. Arita and T. Nishioka. Hierarchical classification of chemical reactions. Bio Industry, 17(7):45-50, 2000.
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[35]: 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.
[36]: F. Baganz, A. Hayes, D. Marren, D.C.J. Gardner, and S.G. Oliver. Evaluation of replacement markers for functional analysis studies in Saccharomyces cerevisiae. Yeast, 13:1563-1573, 1997.
[37]: M. Bain and S.H. Muggleton. Non-monotonic learning. In D. Michie, editor, Machine Intelligence 12, pages 105-120. Oxford University Press, 1991.
[38]: M. Bain. Specification of attributes for computer induction. TIRM, The Turing Institute, Glasgow, 1987.
[39]: M. Bain. Experiments in non-monotonic first-order induction. TIRM, The Turing Institute, Glasgow, 1990.
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[42]: R.B. Banerji. Learning in the limit in a growing language. In IJCAI-87, pages 280-282, Los Angeles, CA, 1987. Kaufmann.
[43]: R.B. Banerji. Learning theoretical terms. In S.H. Muggleton, editor, Inductive Logic Programming. Academic Press, London, 1992.
[44]: 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.
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[83]: D.A. Bohan, C.W.H. Boffey, D.R. Brooks, S.J. Clark, A.M. Dewar, L.G. Firbank, A.J. Haughton, C. Hawes, M.S. Heard, M.J. May, et al. Effects on weed and invertebrate abundance and diversity of herbicide management in genetically modified herbicide-tolerant winter-sown oilseed rape. Proceedings of the Royal Society B: Biological Sciences, 272(1562):463, 2005.
[84]: 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.
[85]: 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.
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[96]: 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.
[97]: 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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[119]: 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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[148]: 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.
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