Skip to main content
Springer Nature Link
Log in

Engineering applications of ILP

  • Special Issue
  • Published:
New Generation Computing Aims and scope Submit manuscript

Abstract

Several applications of Inductive Logic Programming (ILP) are presented. These belong to various areas of engineering, including mechanical, environmental, software, and dynamical systems engineering. The particular applications are finite element mesh design, biological classification of river water quality, data reification, inducing program invariants, learning qualitative models of dynamic systems, and learning control rules for dynamic systems. A number of other applications are briefly mentioned. Finally, a discussion of the advantages and disadvantages of ILP as compared to other approaches to machine learning is given.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Amin, A., Chen, C. P., Sammut, C., and Sum, K. C., “Learning to Recognize Hand-Printed Chinese Characters Using Inductive Logic Programming,”Proc. Fourth International Workshop on Inductive Logic Programming, Gesellschaft für Mathematik und Datenverarbeitung MBH, pp. 263–271, 1994.

  2. Bratko, I., “Applications of Machine Learning: Towards Knowledge Synthesis,”New Generation Computing, 11, pp. 343–360, 1993.

    Article  Google Scholar 

  3. Bratko, I., “Innovative Design as Learning from Examples,”Proc. International Conference on Design to Manufacture in Modern Industry, University of Maribor, pp. 674–681, 1993.

  4. Bratko, I. and Grobelnik, M., “Inductive Learning Applied to Program Construction and Verification,”AI Techniques for Information Processing (J. Cuena, ed.), North-Holland, pp. 169–182, 1993.

  5. Bratko, I. and Grobelnik, M., “Inductive Learning Applied to Program Construction and Verification,”Working Paper, University of Ljubljana, 1994.

  6. Bratko, I. and King, R., “Applications of Inductive Logic Programming,”SIGART Bulletin, 5, 1, pp. 43–49, 1994.

    Article  Google Scholar 

  7. Bratko, I., Muggleton, S., and Varšek, A., “Learning Qualitative Models of Dynamic Systems,”Inductive Logic Programming (S. Muggleton, ed.), Academic Press, pp. 437–452, 1992.

  8. Breiman, L., Friedman, J. H., Olshen, R. A., and Stone C. J.,Classification and Regression Trees, Wadsworth, 1984.

  9. Camacho, R., “Learning Stage Transition Rules with INDLOG,”Proc. Fourth International Workshop on Inductive Logic Programming, Gesellschaft für Mathematik und Datenverarbeitung MBH, pp. 273–290, 1994.

  10. Clark, P. and Boswell, R., “Rule Induction with CN2: Some Recent Improvements,”Proc. Fifth European Working Session on Learning, Springer-Verlag, pp. 151–163, 1991.

  11. Clark, P. and Niblett, T., “The CN2 Induction Algorithm,”Machine Learning, 3, 4, pp. 261–283, 1989.

    Google Scholar 

  12. Dehaspe, L., Van Laer, W., and De Raedt, L., “Applications of a Logical Discovery Engine,”Proc. Fourth International Workshop on Inductive Logic Programming, Gesellschaft für Mathematik und Datenverarbeitung MBH, pp. 273–290, 1994.

  13. De Pauw, N. and Hawkes, H. A., “Biological Monitoring of River Water Quality,”Proc. Freshwater Europe Symposium on River Water Quality Monitoring and Control, Aston University, pp. 87–111, 1993.

  14. De Raedt, L. and Bruynooghe, M., “A Theory of Clausal Discovery,”Proc. Thirteenth International Joint Conference on Artificial Intelligence, Morgan Kaufmann, pp. 1058–1063, 1993.

  15. Dolšak, B., “Determining the Geometric Model of Finite Element Meshes Using AI Methods,”MSc thesis, University of Maribor, 1991.

  16. Dolšak, B., Jezernik, K., and Bratko, I., “A Knowledge Base for Finite Element Mesh Design,”Proc. Sixth Workshop of the International School for the Synthesis of Expert Knowledge, Jožef Stefan Institute, 1992.

  17. Dolšak, B., Bratko, I., and Jezernik, A., “Finite-Element Mesh Design: An Engineering Domain for ILP Application,”Proc. Fourth International Workshop on Inductive Logic Programming, Gesellschaft für Mathematik und Datenverarbeitung MBH, pp. 305–320, 1994.

  18. Dolšak, B. and Muggleton, S., “The Application of Inductive Logic Programming to Finite Element Mesh Design,”Inductive Logic Programming (S. Muggleton, ed.), Academic Press, pp. 453–472, 1992.

  19. Džeroski, S., Cestnik, B., and Petrovski, I., “Using them-Estimate in Rule Induction,”Journal of Computing and Information Technology, 1, 1, pp. 37–46, 1993.

    Google Scholar 

  20. Džeroski, S. and Todorovski, L., “Handling Real Numbers in Inductive Logic Programming,”Proc. Third Electrotechnical and Computer Science Conference, Volume B, Slovenia Section IEEE, pp. 143–146, 1994.

  21. Džeroski, S., Dehaspe, L., Ruck, B. M., and Walley, W. J., “Classification of River Water Quality Using Machine Learning,”Proc. Fifth International Conference on the Development and Application of Computer Techniques to Environmental Studies, Volume I, Computational Mechanics Publications, pp. 129–137, 1994.

  22. Džeroski, S. and Bratko, I., “Handling Noise in Inductive Logic Programming,”Proc. Second International Workshop on Inductive Logic Programming, Institute for New Generation Computer Technologies, 1992.

  23. Emde, W., “Inductive Learning of Characteristic Concept Descriptions,”Proc. Fourth International Workshop on Inductive Logic Programming, Gesellschaft für Mathematik und Datenverarbeitung MBH, pp. 305–320, 1994.

  24. Feng, C., “Inducing Temporal Diagnostic Rules from a Qualitative Model,”Inductive Logic Programming (S. Muggleton, ed.), Academic press, pp. 471–493, 1992.

  25. Flach, P., “Predicate Invention in Inductive Data Engineering,”Proc. Sixth European Conference on Machine Learning, Springer-Verlag, pp. 83–94, 1993.

  26. Grobelnik, M., “MARKUS: An Optimized Model Inference System,”Proc. Workshop on Logical Approaches to Machine Learning, Technical University Vienna, 1992.

  27. Hirst, J. D., King, R. D., and Sternberg, M. J. E., “Quantitative Structure-Activity Relationships: Neural Networks and Inductive Logic Programming Compared against Statistical Methods—I) The Inhibition of Dihydrofolate Reductase by Pyrimidines,”Journal of Medical Chemistry, 1995. Submitted.

  28. Hirst, J. D., King, R. D., and Sternberg, M. J. E., “Quantitative Structure-Activity Relationships: Neural Networks and Inductive Logic Programming Compared against Statistical Methods—II) The Inhibition of Dihydrofolate Reductase by Triazines,”Journal of Medical Chemistry, 1995. Submitted.

  29. Karalič, A., “Employing Linear Regression in Regression Tree Leaves,”Proc. Tenth European Conference on Artificial Intelligence, John Wiley & Sons, pp. 410–411, 1992.

  30. Karalič, A., “Relational Regression: First Steps,”Technical Report, IJS-DP-7001, Jožef Stefan Institute, 1992.

  31. Kietz, J. U. and Wrobel, S., “Controlling the Complexity of Learning in Logic through Syntactic and Task-Oriented Models,”Inductive Logic Programming (S. Muggleton, ed.), Academic Press, pp. 335–359, 1992.

  32. King, R. D., Muggleton, S., Lewis, R. A., and Sternberg, M. J. E., “Drug Design by Machine Learning: The Use of Inductive Logic Progamming to Model the Structure-Activity Relationship of Trimethoprim Analogues Binding to Dihydrofolate Reductase”,Proc. National Academy of Sciences USA, 89, pp. 11322–11326, 1992.

  33. King, R. D., Muggleton, S., Lewis, R. A., Srinivasan, A., Feng, C., and Sternberg, M. J. E., “Drug Design Using Inductive Logic Programming,”Proc. 26th Annual Hawaii International Conference on System Sciences, Volume I, IEEE Computer Society Press, pp. 646–655, 1992.

  34. Klingspor, V., “GRDT: Enhancing Model-Based Learning for Its Application in Robot Navigation,”Proc. Fourth International Workshop on Inductive Logic Programming, Gesellschaft für Mathematik und Datenverarbeitung MBH, pp. 305–320, 1994.

  35. Kodratoff, Y. and Langley, P. (eds.),Proc. Workshop on Real-World Applications of Machine Learning, Technical University Vienna, 1993.

  36. Kovačič, M., “MILP: A Stochastic Approach to Inductive Logic Programming,”Proc. Fourth International Workshop on Inductive Logic Programming, Gesellschaft für Mathematik und Datenverarbeitung MBH, pp. 123–138, 1994.

  37. Kuipers, B., “Qualitative Simulation,”Artificial Intelligence, 29, 3, pp. 289–338, 1986.

    Article  MATH  MathSciNet  Google Scholar 

  38. Lavrač, N., Džeroski, S., and Grobelnik, M., “Learning Nonrecursive Definitions of Relations with LINUS,”Proc. Fifth European Working Session on Learning, Springer-Verlag, pp. 265–281, 1991.

  39. Lavrač, N., Džeroski, S., Pirnat, V., and Križman, V., “The Utility of Background Knowledge in Learning Medical Diagnostic Rules,”Applied Artificial Intelligence, 7, pp. 273–293, 1993.

    Article  Google Scholar 

  40. Lavrač, N. and Džeroski, S.,Inductive Logic Programming: Techniques and Applications, Ellis Horwood, 1994.

  41. Michalski, R. S., “A Theory and Methodology of Inductive Learning,”Machine Learning: An Artificial Intelligence Approach (R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, eds.), Tioga, pp. 83–134, 1983.

  42. Michie, D. and Camacho, R., “Building Symbolic Representations of Intuitive Real-Time Skills from Performance Data,”Machine Intelligence 13 (K. Furukawa, D. Michie, and S. Muggleton, eds.), Oxford University Press, pp. 385–418.

  43. Morales, E., “Learning Chess Patterns,”Inductive Logic Programming (S. Muggleton, ed.), Academic Press, pp. 517–537, 1992.

  44. Morik, K., Wrobel, S., Kietz, J. U., and Emde, W.,Knowledge Acquisition and Machine Learning: Theory, Methods and Applications, Academic Press, 1993.

  45. Muggleton, S. and Feng, C., “Efficient Induction of Logic Programs,”Proc. First Conference on Algorithmic Learning Theory, Ohmsha, pp. 368–381, 1990.

  46. Muggleton, S. and De Raedt, L., “Inductive Logic Programming: Theory and Methods,”Journal of Logic Programming, 19/20, pp. 629–679, 1994.

    Article  Google Scholar 

  47. Muggleton, S., King, R. D., and Sternberg, M. J. E., “Protein Secondary Structure Prediction Using Logic,”Protein Engineering, 5, pp. 647–657, 1992.

    Article  Google Scholar 

  48. Quinlan, J. R., “Induction of Decision Trees,”Machine Learning, 1, 1, pp. 81–106, 1986.

    Google Scholar 

  49. Quinlan, J. R., “Learning Logical Definitions from Relations,”Machine Learning, 5, 3, pp. 239–266, 1990.

    Google Scholar 

  50. Ruck, B. M., Walley, W. J., and Hawkes, H. A., “Biological Classification of River Water Quality Using Neural Networks,”Proc. Eighth International Conference on Artificial Intelligence in Engineering, Elsevier, pp. 361–372, 1993.

  51. Sammut, C., Hurst, S., Kedzier, D., and Michie, D., “Learning to Fly,”Proc. Ninth International Conference on Machine Learning, Morgan Kaufmann, pp. 385–393, 1993.

  52. Srinivasan, A., Muggleton, S., King, R. D., and Sternberg, M. J. E., “Mutagenesis: ILP Experiments in a Non-Determinate Biological Domain”,Proc. Fourth International Workshop on Inductive Logic Programming, Gesellschaft für Mathematik und Datenverarbeitung MBH, pp. 217–232, 1994.

  53. Urbančič, T. and Bratko, I., “Learning to Control Dynamic Systems,”Machine Learning, Neural and Statistical Classification (D. Michie, D. Spiegelhalter, and C. Taylor, eds.), Ellis Horwood, pp. 246–261, 1994.

  54. Urbančič, T. and Bratko, I., “Reconstructing Human Skill with Machine Learning,”Proc. Eleventh European Conference on Artificial Intelligence, John Wiley & Sons, pp. 498–502, 1994.

  55. Walley, W. J., Boyd, M., and Hawkes, H. A., “An Expert System for the Biological Monitoring of River Pollution,”Proc. Fourth International Conference on the Development and Application of Computer Techniques to Environmental Studies, Elsevier, pp. 1030–1047, 1992.

  56. Zupanc, R., “An Implementation of FOIL in Pascal,”MSc thesis, University of Ljubljana, 1991. [in Slovenian]

Download references

Author information

Authors and Affiliations

  1. Jožef Stefan Institute, Jamova 39, 61111, Ljubljana, Slovenia

    Ivan Bratko & Sašo Džeroski

Authors
  1. Ivan Bratko
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Sašo Džeroski
    View author publications

    You can also search for this author inPubMed Google Scholar

Additional information

Ivan Bratko, Ph. D.: He is professor at the Faculty of Electrical Eng. and Computer Science, Ljubljana University, Slovenia. He heads the Al laboratories at J. Stefan Institute and the University. He is also the chairman of the International School for the Synthesis of Expert Knowledge (ISSEK, based in Udine, Italy). He has conducted research in machine learning, knowledge-based systems, qualitative modelling, intelligent robotics, heuristic programming and computer chess. Currently he is the principal researcher of the project Automated Knowledge Synthesis, and is collaborating in the European Esprit III Basic Research Project Inductive Logic Programming. Ivan Bratko received his B. Sc. and Ph. D degrees from Ljubljana University, Slovenia.

Sašo Džeroski, Ph.D.: He is a research assistant at the Jožef Stefan Institute, Ljubljana, Slovenia since 1989. He has been a visiting researcher at the Turing Institute, Glasgow, Scotland and the Computer Science Department at the Katholieke Universiteit Leuven, Belgium. He has conducted research in various areas of machine learning, including applications of machine learning to environmental problems, automated modelling, computational learning theory, genetic programming, and machine discovery of empirical laws. His main research interest is in the area of inductive logic programming. He received his B. Sc., M. Sc., and Ph. D. degrees from the Faculty of Electrical engineering and Computer Science of Ljubljana University.

About this article

Cite this article

Bratko, I., Džeroski, S. Engineering applications of ILP. NGCO 13, 313–333 (1995). https://doi.org/10.1007/BF03037229

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF03037229

Keywords