ILP turns 20: Biography and future challenges

Machine Learning

Volumn 86, Issue 1, 2012, Pages 3-23

  Muggleton, Stephen ^a   De Raedt, Luc ^b   Poole, David ^c   Bratko, Ivan ^d   Flach, Peter ^e   Inoue, Katsumi ^f   Srinivasan, Ashwin ^g  

^a IMPERIAL COLLEGE LONDON   (United Kingdom)

^b UNIVERSITY OF LEUVEN   (Belgium)

^c UNIVERSITY OF BRITISH COLUMBIA   (Canada)

^d UNIVERSITY OF LJUBLJANA   (Slovenia)

^e UNIVERSITY OF BRISTOL   (United Kingdom)

^f NATIONAL INSTITUTE OF INFORMATICS   (Japan)

^g SOUTH ASIAN UNIVERSITY   (India)

Author keywords

(Statistical) relational learning; Inductive Logic Programming; Structured data in Machine Learning

Indexed keywords

FUTURE CHALLENGES; INDUCTIVE LOGIC; REAL-WORLD APPLICATION; RELATIONAL LEARNING; STRUCTURED DATA;

LEARNING SYSTEMS;

INDUCTIVE LOGIC PROGRAMMING (ILP);

EID: 84855664051     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-011-5259-2     Document Type: Conference Paper

References (111)

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. H. Blockeel L. De Raedt N. Jacobs B. Demoen 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 Proceedings 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. W. L. Buntine 1994 Operations for learning with graphical models Journal of Artificial Intelligence Research 2 159 225
8. J. Chen S. H. Muggleton J. Santos 2008 Learning probabilistic logic models from probabilistic examples Machine Learning 73 1 55 85 10.1007/s10994-008-5076-4
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. Corapi, D.; Russo, A.; & Lupu, E. (2010). Inductive logic programming as abductive search. In Technical communications of ICLP'10 (pp. 54-63).
11. M. Craven S. Slattery 2001 Relational learning with statistical predicate invention: Better models for hypertext Machine Learning 43 1/2 97 119 0988.68818 (Pubitemid 32286617)
12. J. Cussens 2001 Parameter estimation in stochastic logic programs Machine Learning 44 3 245 271 0986.68010 (Pubitemid 32761112)
13. 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.
14. L. De Raedt 1997 Logical settings for concept-learning Artificial Intelligence 95 1 197 201
15. De Raedt, L. (2008). Logical and relational learning. Berlin: Springer.
16. 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.
17. 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.
18. L. De Raedt N. Lavrač 1996 Multiple predicate learning in two inductive logic programming settings Journal on Pure and Applied Logic 4 2 227 254 0846.68087
19. 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).
20. De Raedt, L.; Frasconi, P.; Kersting, K.; & Muggleton, S. H. (Eds.) (2008). LNAI: Vol. 4911. Probabilistic inductive logic programming. Berlin: Springer.
21. Dehaspe, L.; & Toivonen, H. (2001). Discovery of relational association rules. In Džeroski, S.; & Lavrač, N. (Eds.), Relational data mining (pp. 189-212). Berlin: Springer.
22. T. Dietterich P. Domingos L. Getoor S. H. Muggleton P. Tadepalli 2008 Structured machine learning: the next ten years Machine Learning 73 1 3 23 10.1007/s10994-008-5079-1
23. 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.
24. 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. (Pubitemid 44705255)
25. Džeroski, S.; & Lavrač, N. (Eds.) (2001). Relational data mining. Berlin: Springer.
26. Džeroski, S.; Muggleton, S. H.; & Russell, S. (1993). Learnability of constrained logic programs. In Proceedings of the European conference on machine learning (pp. 342-347). London: Springer.
27. S. Džeroski L. De Raedt K. Driessens 2001 Relational reinforcement learning Machine Learning 43 1/2 5 52
28. Emde, W.; & Wettschereck, D. (1996). Relational instance-based learning. In Proceedings of the 13th international machine learning conference (pp. 122-130).
29. Esposito, F.; Laterza, A.; Malerba, D.; & Semeraro, G. (1996). Refinement of Datalog programs. In Proceedings of the MLnet familiarization workshop on data mining with inductive logic programming (pp. 73-94).
30. Feng, C. (1992). Inducing temporal fault diagnostic rules from a qualitative model. In S. H. Muggleton (Ed.), Inductive logic programming. London: Academic Press.
31. 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.
32. Flach, P. A.; & Kakas, A. C. (Eds.) (2000). Abduction and induction: essays on their relation and integration. Dordrecht: Kluwer Academic.
33. Getoor, L.; & Taskar, B. (Eds.) (2007). An introduction to statistical relational learning. Cambridge: MIT Press.
34. 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.
35. 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.
36. 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).
37. K. Inoue 2004 Induction as consequence finding Machine Learning 55 109 135 1101.68078
38. Inoue, K.; Furukawa, K.; Kobayashiand, I.; & Nabeshima, H. (2010). Discovering rules by meta-level abduction. In L. De Raedt (Ed.), LNAI: Vol. 5989. Proceedings of the nineteenth international conference on inductive logic programming (ILP09) (pp. 49-64). Berlin: Springer.
39. Kersting, K.; & De Raedt, L. (2001). Towards combining inductive logic programming with bayesian networks. In LNAI: Vol. 2157. Proceedings of the eleventh international conference on inductive logic programming (pp. 118-131). Berlin: Springer. (Pubitemid 33332602)
40. K. Kersting L. De Raedt T. Raiko 2006 Logical Hidden Markov Models 25 425 456 1182.68353
41. 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.
42. 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.
43. R. D. King S. H. Muggleton A. Srinivasan M. J. E. Sternberg 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 (Pubitemid 26038172)
44. R. D. King K. E. Whelan F. M. Jones P. K. G. Reiser C. H. Bryant S. H. Muggleton D. B. Kell S. G. Oliver 2004 Functional genomic hypothesis generation and experimentation by a robot scientist Nature 427 247 252 (Pubitemid 38112040)
45. R. D. King J. Rowland S. G. Oliver M. Young W. Aubrey E. Byrne M. Liakata M. Markham P. Pir L. N. Soldatova A. Aparkes K. E. Whelan A. Clare 2009 The automation of science Science 324 5923 85 89
46. 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).
47. 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.
48. Krogel, M.-A.; & Wrobel, S. (2001). Transformation-based learning using multirelational aggregation. In LNCS: Vol. 2157. Inductive logic programming (pp. 142-155). (Pubitemid 33332604)
49. N. Landwehr K. Kersting L. De Raedt 2007 Integrating naive Bayes and Foil Journal of Machine Learning Research 8 481 507 1222.68242
50. Lavrač, N.; & Džeroski, S. (1993). Inductive logic programming: techniques and applications. Chichester: Ellis Horwood.
51. Lavrač, N.; Džeroski, S.; & Grobelnik, M. (1991). Learning non-recursive definitions of relations with LINUS. In Y. Kodratoff (Ed.), Lecture notes in artificial intelligence: Vol. 482. Proceedings of the 5th European working session on learning. Berlin: Springer.
52. Lisi, F. A.; & Malerba, D. (2003). Bridging the gap between horn clausal logic and description logics in inductive learning. In LNCS: Vol. 2829. AI IA 2003: Advances in artificial intelligence. Berlin: Springer.
53. Lloyd, J. W. (2003). Logic for learning. Berlin: Springer.
54. Mihalkova, L.; & Mooney, R. J. (2009). Transfer learning from minimal target data by mapping across relational domains. In IJCAI-09: Proceedings of the twentieth international joint conference on artificial intelligence (pp. 1163-1168). San Mateo: Morgan-Kaufmann.
55. Milch, B.; Zettlemoyer, L. S.; Kersting, K.; Haimes, M.; & Kaelbling, L. P. (2008). Lifted probabilistic inference with counting formulas. In Proceedings of the twenty third conference on artificial intelligence (AAAI).
56. Morik, K.; Wrobel, S.; Kietz, J.; & Emde, W. (1993). Knowledge acquisition and machine learning: theory, methods and applications. London: Academic Press.
57. 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. (Pubitemid 127124392)
58. Muggleton, S. H. (1987). Duce, an oracle based approach to constructive induction. In IJCAI-87 (pp. 287-292). Los Altos: Kaufmann.
59. S. H. Muggleton 1991 Inductive logic programming New Generation Computing 8 4 295 318 0712.68022
60. Muggleton, S. H. (Ed.) (1992). Inductive logic programming. San Diego: Academic Press.
61. S. H. Muggleton 1995 Inverse entailment and Progol New Generation Computing 13 245 286
62. Muggleton, S. H. (1996). Stochastic logic programs. In L. de Raedt (Ed.), Advances in inductive logic programming (pp. 254-264). Amsterdam: IOS Press.
63. 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.
64. 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.
65. Muggleton, S. H.; & Buntine, W. (1988). Machine invention of first-order predicates by inverting resolution. In Proceedings of the 5th international conference on machine learning (pp. 339-352). Los Altos: Kaufmann.
66. S. H. Muggleton L. De Raedt 1994 Inductive logic programming: Theory and methods Journal of Logic Programming 19-20 629 679
67. Muggleton, S. H.; & Feng, C. (1990). Efficient induction of logic programs. In Proceedings of the first conference on algorithmic learning theory (pp. 368-381). Tokyo: Ohmsha.
68. Muggleton, S. H.; & Feng, C. (1992). Efficient induction of logic programs. In S. H. Muggleton (Ed.), Inductive logic programming (pp. 281-298). London: Academic Press.
69. S. H. Muggleton R. D. King M. J. E. Sternberg 1992 Protein secondary structure prediction using logic-based machine learning Protein Engineering 5 7 647 657
70. 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.
71. Nienhuys-Cheng, S.-H.; & de Wolf, R. (1997). LNAI: Vol. 1228. Foundations of inductive logic programming. Berlin: Springer.
72. 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. (Pubitemid 41479998)
73. A. Passerini P. Frasconi L. De Raedt 2006 Kernels on Prolog proof trees: statistical learning in the ILP setting Journal of Machine Learning Research 7 307 342 1222.68280 (Pubitemid 43668123)
74. Plotkin, G. D. (1969). A note on inductive generalisation. In B. Meltzer & D. Michie (Eds.), Machine intelligence (Vol. 5, pp. 153-163). Edinburgh: Edinburgh University Press.
75. Plotkin, G. D. (1971a). Automatic methods of inductive inference. Ph.D. thesis, Edinburgh University, August 1971.
76. Plotkin, G. D. (1971b). A further note on inductive generalization. In Machine intelligence (Vol. 6). Edinburgh: Edinburgh University Press.
77. Poole, D. L. (1991). Representing diagnostic knowledge for probabilistic Horn abduction (pp. 1129-1135). Sydney.
78. D. L. Poole 1993 Probabilistic Horn abduction and Bayesian networks Artificial Intelligence 64 1 81 129 0792.68176
79. D. L. Poole 1997 The independent choice logic for modelling multiple agents under uncertainty Artificial Intelligence 94 7 56 0902.03017 1464312 Special issue on economic principles of multi-agent systems
80. D. L. Poole 2000 Abducing through negation as failure: stable models within the independent choice logic Journal of Logic Programming 44 1-3 5 35 0957.68013 1768376
81. Poole, D. L. (2003). First-order probabilistic inference. In Proc. eighteenth international joint conference on artificial intelligence (IJCAI-03), Acapulco, Mexico (pp. 985-991).
82. 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 application. Berlin: Springer.
83. Poole, D. L.; & Mackworth, A. K. (2010). Artificial intelligence: foundations of computational agents. New York: Cambridge University Press.
84. Quinlan, J. R. (1987). Generating production rules from decision trees. In Proceedings of the tenth international conference on artificial intelligence (pp. 304-307). Los Altos: Kaufmann.
85. J. R. Quinlan 1990 Learning logical definitions from relations Machine Learning 5 239 266
86. 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.
87. 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. (Pubitemid 37273941)
88. M. Richardson P. Domingos 2006 Markov logic networks Machine Learning 62 107 136 (Pubitemid 43202307)
89. Rouveirol, C.; & Puget, J.-F. (1989). A simple and general solution for inverting resolution. In EWSL-89 (pp. 201-210). London: Pitman.
90. Russell, S. J.; & Norvig, P. (2010). Artificial intelligence: a modern approach (3rd ed.). New Jersey: Pearson.
91. 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.
92. Sammut, C.; & Webb, G. (Eds.) (2010). Encyclopedia of machine learning. Berlin: Springer.
93. Sanner, S.; & Kersting, K. (2010). Symbolic dynamic programming. In C. Sammut & G. Webb (Eds.), Encyclopedia of machine learning. Berlin: Springer.
94. 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).
95. Sato, T. (2005). Generative modeling with failure in prism. In International joint conference on artificial intelligence (pp. 847-852). San Mateo: Morgan Kaufmann.
96. 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).
97. 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.
98. Shapiro, E. Y. (1983). Algorithmic program debugging. Cambridge: MIT Press.
99. Stahl, I. (1992). Constructive induction in inductive logic programming: an overview (Technical report). Fakultat Informatik, Universitat Stuttgart.
100. 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.
101. Sutton, R. S.; & Barto, A. G. (1998). Reinforcement learning: an introduction. Cambridge: MIT Press.
102. 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.
103. A. Tamaddoni-Nezhad R. Chaleil A. Kakas S. H. Muggleton 2006 Application of abductive ILP to learning metabolic network inhibition from temporal data Machine Learning 64 209 230 10.1007/s10994-006-8988-x 1103.68443 (Pubitemid 44320252)
104. A. Tamaddoni-Nezhad R. Chaleil A. Kakas M. J. E. Sternberg J. Nicholson S. H. Muggleton 2007 Modeling the effects of toxins in metabolic networks IEEE Engineering in Medicine and Biology 26 37 46 10.1109/MEMB.2007.335590 (Pubitemid 46486578)
105. 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.
106. 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).
107. van Otterlo, M. (2009). The logic of adaptive behavior-knowledge representation and algorithms for adaptive sequential decision making under uncertainty in first-order and relational domains. Amsterdam: IOS Press.
108. 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.
109. 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.
110. S. Wrobel 1994 Concept formation during iterative theory revision Machine Learning 14 169 191 0804.68126 1334354
111. Yamamoto, Y.; Inoue, K.; & Iwanuma, K. (2010). From inverese entailment to inverese subsumption. In Proceedings of the 20th international conference on inductive logic programming (ILP'10).