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Cited by 126Logic, Reasoning, and KnowledgeData Mining Algorithms and ApplicationsMachine Learning and Algorithms

ILP turns 20: Biography and future challenges

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Muggleton, Stephen, De Raedt, Luc, Poole, David, Bratko, Ivan, Flach, Peter, Inoue, Katsumi, Srinivasan, Ashwin·2012·Machine Learning
DOI 10.1007/s10994-011-5259-2
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Abstract

Inductive Logic Programming (ILP) is an area of Machine Learning which has now reached its twentieth year. Using the analogy of a human biography this paper recalls the development of the subject from its infancy through childhood and teenage years. We show how in each phase ILP has been characterised by an attempt to extend theory and implementations in tandem with the development of novel and challenging real-world applications. Lastly, by projection we suggest directions for research which will help the subject coming of age.

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  • Most ILP and program synthesis systems cannot learn recursive programs.

    “and most ILP and program synthesis systems cannot learn recursive programs.”

    From Learning programs by learning from failures (2021) · cites this paper as Muggleton et al. 2012

    Section 3.3, “The role of logic programming” (page not available), says ILP's emphasis shifted toward learning typically non-recursive classification rules and describes program synthesis as much harder. It does not establish that most ILP and program synthesis systems cannot learn recursive programs; a typical research focus is not the same as an inability. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Predicate invention has been under-explored within inductive logic programming since the late 1990s due to difficulties in formulating efficient search mechanisms.

    “Since the late 1990s predicate invention has been under-explored within inductive logic programming due to difficulties in formulating efficient search mechanisms.”

    From Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited (2015) · cites this paper as Muggleton et al. 2011

  • Failure to address these issues has led to limited progress being made in predicate invention over a protracted period.

    “Failure to address these issues has led to limited progress being made in this important topic over a protracted period (Muggleton et al. 2011).”

    From Meta-interpretive learning of higher-order dyadic datalog: predicate invention revisited (2015) · cites this paper as Muggleton et al. 2011

  • Learning recursive programs has long been considered a difficult problem in ILP.

    “Learning recursive programs has long been considered a difficult problem in ILP (Muggleton et al. 2012).”

    From Learning programs by learning from failures (2021) · cites this paper as Muggleton et al. 2012

  • Predicate invention has been repeatedly stated as an important challenge in ILP.

    “Predicate invention has been repeatedly stated as an important challenge in ILP (Muggleton and Buntine 1988; Stahl 1995; Muggleton et al. 2012).”

    From Learning higher-order logic programs (2020) · cites this paper as Muggleton et al. 2012

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References 132 · Cited by 126

Research works printed but not linked: 95

  1. Bain, M., & Muggleton, S. H. (1991). Non-monotonic learning. In D. Michie (Ed.), Machine intelligence (Vol. 12, pp. 105-120). London: Oxford University Press.
  2. Blockeel, H., & De Raedt, L. (1997). Lookahead and discretisation in ILP. In N. Lavrač & S. Džeroski (Eds.), LNAI: Vol. 1297. Proceedings of the seventh international workshop on inductive logic programming (pp. 77-84). Berlin: Springer.
  3. Blockeel, H., De Raedt, L., Jacobs, N., & Demoen, B. (1999). Scaling up inductive logic programming by learning from interpretations. Data Mining and Knowledge Discovery, 3(1), 59-93.
  4. Bratko, I. (2010). Discovery of abstract concepts by a robot. In LNAI: Vol. 6332. Proceedings of discovery science 2010 (pp. 372-379). Berlin: Springer.
  5. Bratko, I., Muggleton, S. H., & Varsek, A. (1991). Learning qualitative models of dynamic systems. In Pro- ceedings of the eighth international machine learning workshop, San Mateo, CA. San Mateo: Morgan- Kaufmann.
  6. Bratko, I., Leban, G., & Žabkar, J. (2008). An experiment in robot discovery with ilp. In Proceedings of the 18th international conference on inductive logic programming (ILP 2008). Berlin: Springer.
  7. Buntine, W. L. (1994). Operations for learning with graphical models. Journal of Artificial Intelligence Re- search, 2, 159-225.
  8. Chen, J., Muggleton, S. H., & Santos, J. (2008). Learning probabilistic logic models from probabilistic ex- amples. Machine Learning, 73(1), 55-85. doi:10.1007/s10994-008-5076-4. · DOI
  9. Cohen, W. (1993). PAC-learning a restricted class of logic programs. In S. Muggleton (Ed.), Proceedings of the 3rd international workshop on inductive logic programming (pp. 41-72).
  10. Cussens, J. (2001). Parameter estimation in stochastic logic programs. Machine Learning, 44(3), 245-271.
  11. Davis, J., & Domingo, P. (2009). Deep transfer via second-order markov logic. In Proceedings of the twenty- sixth international workshop on machine learning (pp. 217-224). San Mateo: Morgan Kaufmann.
  12. De Raedt, L. (1997). Logical settings for concept-learning. Artificial Intelligence, 95(1), 197-201.
  13. De Raedt, L. (2008). Logical and relational learning. Berlin: Springer.
  14. De Raedt, L., & Bruynooghe, M. (1991). Clint: a multistrategy interactive concept-learner and theory revision system. In Proceedings of the 1st international workshop on multistrategy learning (pp. 175-191). San Mateo: Morgan Kaufmann.
  15. De Raedt, L., & Kersting, K. (2004). Probabilistic inductive logic programming. In S. Ben-David, J. Case, & A. Maruoka (Eds.), Lecture notes in computer science: Vol. 3244. Proceedings of the 15th international conference on algorithmic learning theory. Berlin: Springer.
  16. De Raedt, L., & Lavrač, N. (1996). Multiple predicate learning in two inductive logic programming settings. Journal on Pure and Applied Logic, 4(2), 227-254.
  17. De Raedt, L., Kimmig, A., & Toivonen, H. (2007). ProbLog: a probabilistic Prolog and its application in link discovery. In R. Lopez de Mantaras & M.M. Veloso (Eds.), Proceedings of the 20th international joint conference on artificial intelligence (IJCAI-2007) (pp. 2462-2467).
  18. De Raedt, L., Frasconi, P., Kersting, K., & Muggleton, S. H. (Eds.) (2008). LNAI: Vol. 4911. Probabilistic inductive logic programming. Berlin: Springer.
  19. Dehaspe, L., & Toivonen, H. (2001). Discovery of relational association rules. In Džeroski, S., & Lavrač, N. (Eds.), Relational data mining (pp. 189-212). Berlin: Springer.
  20. Dietterich, T., Domingos, P., Getoor, L., Muggleton, S. H., & Tadepalli, P. (2008). Structured machine learn- ing: the next ten years. Machine Learning, 73(1), 3-23. doi:10.1007/s10994-008-5079-1. · DOI
  21. Dolsak, B., & Muggleton, S. H. (1992). The application of Inductive Logic Programming to finite element mesh design. In S. H. Muggleton (Ed.), Inductive logic programming (pp. 453-472). London: Academic Press.
  22. Domingos, P. S., Kok, S., Poon, H., Richardson, M., & Singla, P. (2006). Unifying logical and statistical ai. In Proceedings of the twenty-first national conference on artificial intelligence, AAAI06 (pp. 2-7). Menlo Park/Cambridge: AAAI Press/MIT Press.
  23. Džeroski, S., & Lavrač, N. (Eds.) (2001). Relational data mining. Berlin: Springer.
  24. Džeroski, S., Muggleton, S. H., & Russell, S. (1993). Learnability of constrained logic programs. In Proceed- ings of the European conference on machine learning (pp. 342-347). London: Springer.
  25. Emde, W., & Wettschereck, D. (1996). Relational instance-based learning. In Proceedings of the 13th inter- national machine learning conference (pp. 122-130).
  26. Esposito, F., Laterza, A., Malerba, D., & Semeraro, G. (1996). Refinement of Datalog programs. In Proceed- ings of the MLnet familiarization workshop on data mining with inductive logic programming (pp. 73- 94).
  27. Feng, C. (1992). Inducing temporal fault diagnostic rules from a qualitative model. In S. H. Muggleton (Ed.), Inductive logic programming. London: Academic Press.
  28. Flach, P. (1993). Predicate invention in inductive data engineering. In P. B. Brazdil (Ed.), Lecture notes in artificial intelligence: Vol. 667. Machine learning: ECML-93 (pp. 83-94). Berlin: Springer.
  29. Flach, P. A., & Kakas, A. C. (Eds.) (2000). Abduction and induction: essays on their relation and integration. Dordrecht: Kluwer Academic.
  30. Getoor, L., Friedman, N., Koller, D., & Pfeffer, A. (2001). Learning probabilistic relational models. In Džeroski, S., & Lavrač, N. (Eds.), Relational data mining (pp. 307-335). Berlin: Springer.
  31. Graham, J. H., Page, C. D., & Kamal, A. H. (2003). Accelerating the drug design process through parallel inductive logic programming data mining. In Proceedings of the IEEE computer society bioinformatics conference-CSB (pp. 400-402). New York: IEEE Press.
  32. Horsch, M., & Poole, D. L. (1990). A dynamic approach to probabilistic inference using Bayesian networks. In Proc. sixth conference on uncertainty in AI, Boston, July 1990 (pp. 155-161).
  33. Inoue, K., Furukawa, K., Kobayashiand, I., & Nabeshima, H. (2010). Discovering rules by meta-level abduc- tion. In L. De Raedt (Ed.), LNAI: Vol. 5989. Proceedings of the nineteenth international conference on inductive logic programming (ILP09) (pp. 49-64). Berlin: Springer.
  34. Kersting, K., & De Raedt, L. (2001). Towards combining inductive logic programming with bayesian net- works. In LNAI: Vol. 2157. Proceedings of the eleventh international conference on inductive logic programming (pp. 118-131). Berlin: Springer.
  35. Khardon, R. (1998). Learning first order universal Horn expressions. In Proceedings of the eleventh annual ACM conference on computational learning theory (pp. 154-165). New York: ACM.
  36. Kietz, J. U. (1993). Some lower bounds on the computational complexity of inductive logic programming. In P. Brazdil (Ed.), Lecture notes in artificial intelligence: Vol. 667. Proceedings of the 6th European conference on machine learning (pp. 115-123). Berlin: Springer.
  37. King, R. D., Muggleton, S. H., Srinivasan, A., & Sternberg, M. J. E. (1996). 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.
  38. Knobbe, A. J., Siebes, A., & Marseille, B. (2002). Involving aggregate functions in multi-relational search. In Proceedings of the 6th European conference on data mining principles and practice of knowledge discovery in databases (p. 1).
  39. Kramer, S., Lavrač, N., & Flach, P. (2001). Propositionalization approaches to relational data mining. In S. Džeroski & N. Lavrač (Eds.), Relational data mining (pp. 262-291). Berlin: Springer.
  40. Krogel, M.-A., & Wrobel, S. (2001). Transformation-based learning using multirelational aggregation. In LNCS: Vol. 2157. Inductive logic programming (pp. 142-155).
  41. Landwehr, N., Kersting, K., & De Raedt, L. (2007). Integrating naive Bayes and Foil. Journal of Machine Learning Research, 8, 481-507.
  42. Lavrač, N., Džeroski, S., & Grobelnik, M. (1991). Learning non-recursive definitions of relations with LI- NUS. In Y. Kodratoff (Ed.), Lecture notes in artificial intelligence: Vol. 482. Proceedings of the 5th European working session on learning. Berlin: Springer.
  43. Lisi, F. A., & Malerba, D. (2003). Bridging the gap between horn clausal logic and description logics in in- ductive learning. In LNCS: Vol. 2829. AI*IA 2003: Advances in artificial intelligence. Berlin: Springer.
  44. Mihalkova, L., & Mooney, R. J. (2009). Transfer learning from minimal target data by mapping across re- lational domains. In IJCAI-09: Proceedings of the twentieth international joint conference on artificial intelligence (pp. 1163-1168). San Mateo: Morgan-Kaufmann.
  45. Milch, B., Zettlemoyer, L. S., Kersting, K., Haimes, M., & Kaelbling, L. P. (2008). Lifted probabilistic in- ference with counting formulas. In Proceedings of the twenty third conference on artificial intelligence (AAAI).
  46. Morik, K., Wrobel, S., Kietz, J., & Emde, W. (1993). Knowledge acquisition and machine learning: theory, methods and applications. London: Academic Press.
  47. Moyle, S., & Muggleton, S. H. (1997). Learning programs in the event calculus. In N. Lavrač & S. Džeroski (Eds.), LNAI: Vol. 1297. Proceedings of the seventh inductive logic programming workshop (ILP97) (pp. 205-212). Berlin: Springer.
  48. Muggleton, S. H. (1987). Duce, an oracle based approach to constructive induction. In IJCAI-87 (pp. 287- 292). Los Altos: Kaufmann.
  49. Muggleton, S. H. (1991). Inductive logic programming. New Generation Computing, 8(4), 295-318.
  50. Muggleton, S. H. (Ed.) (1992). Inductive logic programming. San Diego: Academic Press.
  51. Muggleton, S. H. (1996). Stochastic logic programs. In L. de Raedt (Ed.), Advances in inductive logic pro- gramming (pp. 254-264). Amsterdam: IOS Press.
  52. Muggleton, S. H. (2002). Learning structure and parameters of stochastic logic programs. In Proceedings of the 12th international conference on inductive logic programming (pp. 198-206). Berlin: Springer.
  53. Muggleton, S. H., & Bryant, C. H. (2000). Theory completion using inverse entailment. In Proc. of the 10th international workshop on inductive logic programming (ILP-00) (pp. 130-146). Berlin: Springer.
  54. Muggleton, S. H., & Buntine, W. (1988). Machine invention of first-order predicates by inverting resolu- tion. In Proceedings of the 5th international conference on machine learning (pp. 339-352). Los Altos: Kaufmann.
  55. Muggleton, S. H., & De Raedt, L. (1994). Inductive logic programming: Theory and methods. Journal of Logic Programming, 19-20, 629-679.
  56. Muggleton, S. H., & Feng, C. (1990). Efficient induction of logic programs. In Proceedings of the first con- ference on algorithmic learning theory (pp. 368-381). Tokyo: Ohmsha.
  57. Muggleton, S. H., & Feng, C. (1992). Efficient induction of logic programs. In S. H. Muggleton (Ed.), Induc- tive logic programming (pp. 281-298). London: Academic Press.
  58. Muggleton, S. H., King, R. D., & Sternberg, M. J. E. (1992). Protein secondary structure prediction using logic-based machine learning. Protein Engineering, 5(7), 647-657.
  59. Muggleton, S. H., Fidjeland, A., & Luk, W. (2002). Scalable acceleration of inductive logic programs. In IEEE international conference on field-programmable technology (pp. 252-259). New York: IEEE Press.
  60. Nienhuys-Cheng, S.-H., & de Wolf, R. (1997). LNAI: Vol. 1228. Foundations of inductive logic programming. Berlin: Springer.
  61. Otero, R. (2005). Induction of the indirect effects of actions by monotonic methods. In Proceedings of the fifteenth international conference on inductive logic programming (ILP05) (Vol. 3625, pp. 279-294). Berlin: Springer.
  62. Passerini, A., Frasconi, P., & De Raedt, L. (2006). Kernels on Prolog proof trees: statistical learning in the ILP setting. Journal of Machine Learning Research, 7, 307-342.
  63. Plotkin, G. D. (1969). A note on inductive generalisation. In B. Meltzer & D. Michie (Eds.), Machine intelli- gence (Vol. 5, pp. 153-163). Edinburgh: Edinburgh University Press.
  64. Plotkin, G. D. (1971a). Automatic methods of inductive inference. Ph.D. thesis, Edinburgh University, August 1971.
  65. Plotkin, G. D. (1971b). A further note on inductive generalization. In Machine intelligence (Vol. 6). Edin- burgh: Edinburgh University Press.
  66. Poole, D. L. (1991). Representing diagnostic knowledge for probabilistic Horn abduction (pp. 1129-1135).
  67. Poole, D. L. (1993). Probabilistic Horn abduction and Bayesian networks. Artificial Intelligence, 64(1), 81-129.
  68. Poole, D. L. (2000). Abducing through negation as failure: stable models within the independent choice logic. Journal of Logic Programming, 44(1-3), 5-35.
  69. Poole, D. L. (2003). First-order probabilistic inference. In Proc. eighteenth international joint conference on artificial intelligence (IJCAI-03), Acapulco, Mexico (pp. 985-991).
  70. Poole, D. L. (2008). The independent choice logic and beyond. In L. De Raedt, P. Frasconi, K. Kersting, & S. Muggleton (Eds.), LNCS: Vol. 4911. Probabilistic inductive logic programming: theory and applica- tion. Berlin: Springer.
  71. Poole, D. L., & Mackworth, A. K. (2010). Artificial intelligence: foundations of computational agents. New York: Cambridge University Press.
  72. Quinlan, J. R. (1987). Generating production rules from decision trees. In Proceedings of the tenth interna- tional conference on artificial intelligence (pp. 304-307). Los Altos: Kaufmann.
  73. Quinlan, J. R., & Cameron-Jones, R.M. (1993). FOIL: a midterm report. In P. Brazdil (Ed.), Lecture notes in artificial intelligence: Vol. 667. Proceedings of the 6th European conference on machine learning (pp. 3-20). Berlin: Springer.
  74. Ray, O., Broda, K., & Russo, A. (2003). Hybrid abductive inductive learning: a generalisation of Progol. In Lecture notes in artificial intelligence: Vol. 2835. Proceedings of the 13th international conference on inductive logic programming (ILP'03) (pp. 311-328). Berlin: Springer.
  75. Rouveirol, C., & Puget, J.-F. (1989). A simple and general solution for inverting resolution. In EWSL-89 (pp. 201-210). London: Pitman.
  76. Russell, S. J., & Norvig, P. (2010). Artificial intelligence: a modern approach (3rd ed.). New Jersey: Pearson.
  77. Sammut, C., & Banerji, R.B. (1986). Learning concepts by asking questions. In R. Michalski, J. Carbonnel, & T. Mitchell (Eds.), Machine learning: an artificial intelligence approach (Vol. 2, pp. 167-192). Los Altos: Kaufmann.
  78. Sammut, C., & Webb, G. (Eds.) (2010). Encyclopedia of machine learning. Berlin: Springer.
  79. Sanner, S., & Kersting, K. (2010). Symbolic dynamic programming. In C. Sammut & G. Webb (Eds.), Ency- clopedia of machine learning. Berlin: Springer.
  80. Santos Costa, V., Page, D., Qazi, M., & Cussens, J. (2003). CLP(BN): Constraint logic programming for probabilistic knowledge. In Proceedings of the 19th conference on uncertainty in artificial intelligence (pp. 517-524).
  81. Sato, T. (2005). Generative modeling with failure in prism. In International joint conference on artificial intelligence (pp. 847-852). San Mateo: Morgan Kaufmann.
  82. Sato, T., & Kameya, Y. (1997). PRISM: a symbolic-statistical modeling language. In Proceedings of the 15th international joint conference on artificial intelligence (IJCAI-97) (pp. 1330-1335).
  83. Sato, T., & Kameya, Y. (2008). New advances in logic-based probabilistic modeling by PRISM. In L. De Raedt, P. Frasconi, K. Kersting, & S. Muggleton (Eds.), LNCS: Vol. 4911. Probabilistic inductive logic programming (pp. 118-155). Berlin: Springer.
  84. Shapiro, E. Y. (1983). Algorithmic program debugging. Cambridge: MIT Press.
  85. Stahl, I. (1996). Predicate invention in inductive logic programming. In L. De Raedt (Ed.), Advances in inductive logic programming (pp. 34-47). Amsterdam: IOS Press.
  86. Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: an introduction. Cambridge: MIT Press.
  87. Synnaeve, G., Inoue, K., Doncescu, A., Kameya, Y., Sato, T., Ishihata, M., & Nabeshima, H. (2011). Kinetic models and qualitative abstraction for relational learning in systems biology. In Proceedings of the international conference on bioinformatics models, methods and algorithms.
  88. Tamaddoni-Nezhad, A., Chaleil, R., Kakas, A., & Muggleton, S. H. (2006). Application of abductive ILP to learning metabolic network inhibition from temporal data. Machine Learning, 64, 209-230. doi:10.1007/s10994-006-8988-x. · DOI · Paper page
  89. Tamaddoni-Nezhad, A., Chaleil, R., Kakas, A., Sternberg, M. J. E., Nicholson, J., & Muggleton, S. H. (2007). Modeling the effects of toxins in metabolic networks. IEEE Engineering in Medicine and Biology, 26, 37-46. doi:10.1109/MEMB.2007.335590. · DOI
  90. Torrey, L., & Shavlik, J. W. (2010). Policy transfer via Markov logic networks. In L. De Raedt (Ed.), LNAI: Vol. 5989. Proceedings of the nineteenth international conference on inductive logic programming (ILP09) (pp. 234-248). Berlin: Springer.
  91. Van den Broeck, G., Thon, I., van Otterlo, M., & De Raedt, L. (2010). DTProbLog: A decision-theoretic probabilistic prolog. In Proceedings of the AAAI conference on artificial intelligence (AAAI 2010).
  92. van Otterlo, M. (2009). The logic of adaptive behavior-knowledge representation and algorithms for adap- tive sequential decision making under uncertainty in first-order and relational domains. Amsterdam: IOS Press.
  93. Vens, C., Ramon, J., & Blockeel, H. (2006). Refining aggregate conditions in relational learning. In J. Fürnkranz, T. Scheffer, & M. Spiliopoulou (Eds.), Lecture notes in computer science: Vol. 4213. Proceedings of the 10th European conference on principles and practice of knowledge discovery in databases (pp. 383-394). Berlin: Springer.
  94. Vere, S. A. (1975). Induction of concepts in the predicate calculus. In Proceedings of the 4th international joint conference on artificial intelligence (pp. 282-287). San Mateo: Morgan Kaufmann.
  95. Yamamoto, Y., Inoue, K., & Iwanuma, K. (2010). From inverese entailment to inverese subsumption. In Pro- ceedings of the 20th international conference on inductive logic programming (ILP'10).

Authors
Muggleton, S., De Raedt, L., Poole, D., Bratko, I., Flach, P., Inoue, K., & Srinivasan, A.
Affiliations
  • Imperial College London (GB)
  • University of British Columbia (CA)
  • University of Bristol (GB)
  • National Institute of Informatics (JP)
  • South Asian University (IN)
Corresponding author
Muggleton, Stephen
ORCID
Venue
Machine Learning
Publisher
Springer Science and Business Media LLC
Published
2011-09-05
Volume
86
Issue
1
Pages
3-23
ISSN
  • 0885-6125 (ISSN-L)
  • 1573-0565
DOI
10.1007/s10994-011-5259-2
OpenAlex
W1499053674
Cited by
126
References
132
Citations by year
2026: 2 · 2025: 7 · 2024: 4 · 2023: 8 · 2022: 7 · 2021: 12 · 2020: 11 · 2019: 15 · 2018: 14 · 2017: 10 · 2016: 4 · 2015: 8 · 2014: 7 · 2013: 12 · 2012: 4
FWCI
7.07
Type
journal article
Language
en
Open access
Licence
Locations
  • Machine Learning (publishedVersion, cc-by-nc)
  • Lirias (submittedVersion, other-oa)
Funders
  • Microsoft Research
  • Jawaharlal Nehru University
  • Royal Academy of Engineering
  • Imperial College London
  • University of Oxford
  • University of New South Wales
  • Biotechnology and Biological Sciences Research Council
  • Japan Society for the Promotion of Science (20240016)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Logic, Reasoning, and Knowledge
  • Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
Keywords
inductive logic programming, research directions, real-world applications

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