Skip to main content

Requirements for Machine Lifelong Learning

  • Conference paper
Bio-inspired Modeling of Cognitive Tasks (IWINAC 2007)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 4527))

Abstract

A system that is capable of retaining learned knowledge and selectively transferring portions of that knowledge as a source of inductive bias during new learning would be a significant advance in artificial intelligence and inductive modeling. We define such a system to be a machine lifelong learning, or ML3 system. This paper makes an initial effort at specifying the scope of ML3 systems and their functional requirements.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Abu-Mostafa, Y.S.: Hints. Neural Computation 7, 639–671 (1995)

    Article  Google Scholar 

  2. Baxter, J.: Learning internal representations. In: Proceedings of the Eighth International Conference on Computational Learning Theory (1995)

    Google Scholar 

  3. Caruana, R.A.: Multitask learning. Machine Learning 28, 41–75 (1997)

    Article  Google Scholar 

  4. Mitchell, T.M.: The need for biases in learning generalizations. In: Shavlik, J.W., Dietterich, T.G. (eds.) Readings in Machine Learning, pp. 184–191 (1980)

    Google Scholar 

  5. Mitchell, T.M.: Machine Learning. McGraw Hill, New York (1997)

    MATH  Google Scholar 

  6. Naik, D.K., Mammone, R.J.: Learning by learning in neural networks. In: Artificial Neural Networks for Speech and Vision (1993)

    Google Scholar 

  7. O’Quinn, R.: Knowledge Transfer in Artificial Neural Networks. Honours Thesis, Jodrey School of Computer Science, Acadia University, Wolfville, NS (2005)

    Google Scholar 

  8. Poirier, R., Silver, D.L.: Effect of curriculum on the consolidation of neural network task knowledge. In: Proc. of IEEE International Joint Conf. on Neural Networks, IJCNN’05 (2005)

    Google Scholar 

  9. Ring, M.: Learning sequential tasks by incrementally adding higher orders. In: Giles, C.L., Hanson, S.J., Cowan, J.D. (eds.) Advances in Neural Information Processing Systems 5, pp. 155–122 (1993)

    Google Scholar 

  10. Robins, A.V.: Catastrophic forgetting, rehearsal, and pseudorehearsal. Connection Science 7, 123–146 (1995)

    Article  Google Scholar 

  11. Shavlik, J.W., Dietterich, T.G.: Readings in Machine Learning. Morgan Kaufmann Publishers, San Mateo (1990)

    Google Scholar 

  12. Silver, D.L., Alisch, R.: A measure of relatedness for selecting consolidated task knowledge. In: Proceedings of the 18th Florida Artificial Intelligence Research Society Conference (FLAIRS05), pp. 399–404 (2005)

    Google Scholar 

  13. Silver, D.L., McCracken, P.: Selective transfer of task knowledge using stochastic noise. In: Xiang, Y., Chaib-draa, B. (eds.) Canadian AI 2003. LNCS (LNAI), vol. 2671, pp. 190–205. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  14. Silver, D.L., Mercer, R.E.: The parallel transfer of task knowledge using dynamic learning rates based on a measure of relatedness. Connection Science (Special Issue: Transfer in Inductive Systems) 8(2), 277–294 (1996)

    Article  Google Scholar 

  15. Silver, D.L., Mercer, R.E.: The task rehearsal method of life-long learning: Overcoming impoverished data. In: Cohen, R., Spencer, B. (eds.) Canadian AI 2002. LNCS (LNAI), vol. 2338, pp. 90–101. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  16. Singh, S.P.: Transfer of learning by composing solutions for elemental sequential tasks. Machine Learning (1992)

    Google Scholar 

  17. Thrun, S.: Lifelong learning algorithms. In: Learning to Learn, pp. 181–209. Kluwer Academic Publishers, Dordrecht (1997)

    Google Scholar 

  18. Utgoff, P.E.: Machine Learning of Inductive Bias. Kluwer Academc Publisher, Boston (1986)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

José Mira José R. Álvarez

Rights and permissions

Reprints and permissions

Copyright information

© 2007 Springer Berlin Heidelberg

About this paper

Cite this paper

Silver, D.L., Poirier, R. (2007). Requirements for Machine Lifelong Learning. In: Mira, J., Álvarez, J.R. (eds) Bio-inspired Modeling of Cognitive Tasks. IWINAC 2007. Lecture Notes in Computer Science, vol 4527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73053-8_31

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-73053-8_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-73052-1

  • Online ISBN: 978-3-540-73053-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics