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Asia-Pacific Conference on Simulated Evolution and Learning

SEAL 2008: Simulated Evolution and Learning pp 503–513Cite as

Horn Query Learning with Multiple Refinement

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5361))

Abstract

In this paper we try to understand the heuristics that underlie the decisions made by the Horn query learning algorithm proposed in [1]. We take advantage of our explicit representation of such heuristics in order to present an alternative termination proof for the algorithm, as well as to justify its decisions by showing that they always guarantee that the negative examples in the sequence maintained by the algorithm violate different clauses in the target formula. Finally, we propose a new algorithm that allows multiple refinement when we can prove that such a refinement does not affect the independence of the negative examples in the sequence maintained by the algorithm.

This work is partially funded by the DGICYT TIN2005-08832-C03-03 project.

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References

  1. Angluin, D., Frazier, M., Pitt, L.: Learning conjunctions of Horn clauses. Machine Learning 9, 147–164 (1992)

    MATH  Google Scholar 

  2. McCarthy, J.: Formalizing Common Sense. Papers by John McCarthy. Ablex. Edited by Vladimir Lifschitz (1990)

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  3. Sierra, J.: Declarative formalization of reasoning strategies: A case study on heuristic nonlinear planning. Annals of Math. and Artif. Intelligence 39(1-2), 61–100 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  4. Balcazar, J.: Query learning of horn formulas revisited. In: Computability in Europe Conference, Amsterdam (2005)

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Author information

Authors and Affiliations

  1. Universidad Politécnica de Cataluña, Spain

    Josefina Sierra

  2. Universidad de La Rioja, Spain

    Josefina Santibáñez

Authors
  1. Josefina Sierra
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  2. Josefina Santibáñez
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Editor information

Editors and Affiliations

  1. School of Computer Science and information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Xiaodong Li

  2. Melbourne School Engineering, Department of Computer Science and Software Engineering, The University of Melbourne, VIC 3001, Melbourne, Australia

    Michael Kirley

  3. School of Mathematics, Statistics and Computer Science, Victoria University of Wellington, P.O. Box 600, 6140, Wellington, New Zealand

    Mengjie Zhang

  4. Faculty of Information Technology, Monash University, Clayton Campus, VIC 3800, Australia

    David Green

  5. School of Computer Science and Information Technology, RMIT University, VIC 3001, Melbourne, Australia

    Vic Ciesielski

  6. School of Information Technology and Electrical Engineering, Australian Defence Force Academy, University of New South Wales, Canberra, Australia

    Hussein Abbass

  7. School of Computer Science, University of Adelaide, SA 5005, Adelaide, Australia

    Zbigniew Michalewicz

  8. Faculty of Information and Communication Technologies, Swinburne University of Technology, Melbourne, Australia

    Tim Hendtlass

  9. Indian Institute of Technology Kanpur, Department of Mechanical Engineering, Kanpur Genetic Algorithms Laboratory (KanGAL), 208016, Kanpur, Uttar Pradesh, India

    Kalyanmoy Deb

  10. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore

    Kay Chen Tan

  11. Institute AIFB, University of Karlsruhe, 76128, Karlsruhe, Germany

    Jürgen Branke

  12. Xi’an Jiaotong-Liverpool University, 215123, Suzhoum, China

    Yuhui Shi

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© 2008 Springer-Verlag Berlin Heidelberg

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Cite this paper

Sierra, J., Santibáñez, J. (2008). Horn Query Learning with Multiple Refinement. In: Li, X., et al. Simulated Evolution and Learning. SEAL 2008. Lecture Notes in Computer Science, vol 5361. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89694-4_51

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  • DOI: https://doi.org/10.1007/978-3-540-89694-4_51

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-89693-7

  • Online ISBN: 978-3-540-89694-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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