I. Bratko and S.H. Muggleton and A. Varsek. Learning Qualitative Models of Dynamic Systems. 1991. in Proceedings of the Eighth International Machine Learning Workshop. Morgan-Kaufmann, San Mateo, Ca.

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

[1]

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.

[2]

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.

[3]

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.

[4]

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.

[5]

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.

[6]

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.

[7]

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.

[8]

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.

[9]

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.

[10]

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.

[11]

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.

[12]

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.

[13]

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.

[14]

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.

[15]

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.

[16]

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.

[17]

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.

[18]

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.

[19]

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.

[20]

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.

[21]

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.

[22]

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.

[23]

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.

[24]

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.

[25]

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.

[26]

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.

[27]

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.

[28]

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.

[29]

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.

[30]

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.

[31]

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

[32]

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.

[33]

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.

[34]

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.

[35]

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.

[36]

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.

[37]

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.

[38]

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.

[39]

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.

[40]

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.

[41]

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.

[42]

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.

[43]

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.

[44]

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.

[45]

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.

[46]

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.

[47]

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.

[48]

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.

[49]

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.

[50]

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.

[51]

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.

[52]

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.

[53]

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.

[54]

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.

[55]

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.

[56]

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.

[57]

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.

[58]

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.

[59]

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.

[60]

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.

[61]

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.

[62]

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.

[63]

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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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.

[65]

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.

[66]

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.

[67]

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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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.

[72]

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.

[73]

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.

[74]

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.

[75]

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.

[76]

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.

[77]

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.

[78]

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.

[79]

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.

[80]

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.

[81]

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.

[82]

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.

[83]

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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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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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.

[88]

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, , 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.

[90]

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

[91]

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 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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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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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.

[96]

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. 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.

[99]

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.

[100]

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

[101]

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).

[103]

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.

[104]

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.

[105]

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.

[106]

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.

[107]

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.

[108]

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

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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.