Skip to main content

Coherence Progress: A Measure of Interestingness Based on Fixed Compressors

  • Conference paper
Artificial General Intelligence (AGI 2011)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6830))

Included in the following conference series:

Abstract

The ability to identify novel patterns in observations is an essential aspect of intelligence. In a computational framework, the notion of a pattern can be formalized as a program that uses regularities in observations to store them in a compact form, called a compressor. The search for interesting patterns can then be stated as a search to better compress the history of observations. This paper introduces coherence progress, a novel, general measure of interestingness that is independent of its use in a particular agent and the ability of the compressor to learn from observations. Coherence progress considers the increase in coherence obtained by any compressor when adding an observation to the history of observations thus far. Because of its applicability to any type of compressor, the measure allows for an easy, quick, and domain-specific implementation. We demonstrate the capability of coherence progress to satisfy the requirements for qualitatively measuring interestingness on a Wikipedia dataset.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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.

Similar content being viewed by others

References

  1. Cilibrasi, R., Vitanyi, P.: Clustering by compression. IEEE Transactions on Information Theory 51, 1523–1545 (2005)

    Article  MathSciNet  Google Scholar 

  2. Geng, L., Hamilton, H.J.: Interestingness measures for data mining: A survey. ACM Comput. Surv. 38 (September 2006)

    Google Scholar 

  3. Homer: Iliad (ca. 800 BC). Translated by Alexander Pope, London (1715)

    Google Scholar 

  4. Huffman, D.A.: A method for construction of minimum-redundancy codes. Proceedings IRE 40, 1098–1101 (1952)

    Article  Google Scholar 

  5. Itti, L., Baldi, P.F.: Bayesian surprise attracts human attention. In: Advances in Neural Information Processing Systems 19, pp. 547–554. MIT Press, Cambridge (2005)

    Google Scholar 

  6. Schmidhuber, J.: Curious model-building control systems. In: Proceedings of the International Joint Conference on Neural Networks, Singapore, vol. 2, pp. 1458–1463. IEEE press, Los Alamitos (1991)

    Google Scholar 

  7. Schmidhuber, J.: A possibility for implementing curiosity and boredom in model-building neural controllers. In: Proc. of the International Conference on Simulation of Adaptive Behavior: From Animals to Animats, pp. 222–227. MIT Press/Bradford Books (1991)

    Google Scholar 

  8. Schmidhuber, J.: Developmental Robotics, Optimal Artificial Curiosity, Creativity, Music, and the Fine Arts. Connection Science 18, 173–187 (2006)

    Article  Google Scholar 

  9. Schmidhuber, J.: Driven by compression progress: A simple principle explains essential aspects of subjective beauty, novelty, surprise, interestingness, attention, curiosity, creativity, art, science, music, jokes. In: Pezzulo, G., Butz, M.V., Sigaud, O., Baldassarre, G. (eds.) Anticipatory Behavior in Adaptive Learning Systems. LNCS, vol. 5499, pp. 48–76. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  10. Storck, J., Hochreiter, S., Schmidhuber, J.: Reinforcement driven information acquisition in non-deterministic environments. In: Proceedings of the International Conference on Artificial Neural Networks, Paris, vol. 2, pp. 159–164. EC2 & Cie (1995)

    Google Scholar 

  11. Wundt, W.M.: Grundzüge der Phvsiologischen Psychologie. Engelmann, Leipzig (1874)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Schaul, T., Pape, L., Glasmachers, T., Graziano, V., Schmidhuber, J. (2011). Coherence Progress: A Measure of Interestingness Based on Fixed Compressors. In: Schmidhuber, J., Thórisson, K.R., Looks, M. (eds) Artificial General Intelligence. AGI 2011. Lecture Notes in Computer Science(), vol 6830. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22887-2_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-22887-2_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22886-5

  • Online ISBN: 978-3-642-22887-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics