Seeking Allies

  • Tom AddisEmail author
Part of the Advanced Information and Knowledge Processing book series (AI&KP)


In this chapter I will describe in some detail a formal computer model of inferential discourse based on the belief system (see Chaps. 6 and 7). The key issue is that a logical model in a computer, based on rational sets, can usefully model a human situation grounded on irrational sets (see Chap. 9). The background of this work is explained elsewhere, as is the issue of rational and irrational sets. The model is based on the Belief System and it provides a mechanism for choosing queries based on a range of belief. We explain how it provides a way to update the belief based on query results, thus modelling others’ experience by inference. We also demonstrate that for the same internal experience, different models can be built for different actors.


Music Metaphors Perceptions Concert programme Ranking Conversation Z-score Correlation Internal models Indifference Expectation Flexibility Predictive power 


  1. Addis T, Gooding D (1999) Learning as collective belief-revision: simulating reasoning about disparate phenomena, Proceedings of the AISB’99 Symposium on Scientific Creativity, ISBN 1 902 956044Google Scholar
  2. Addis T, Gooding D (2008) Methods for an abductive system in science Foundations of Science, vol 13, No 1, March 2008Google Scholar
  3. Addis T et al (2004) The abductive loop: Tracking irrational sets (this publication)Google Scholar
  4. Billinge D (2000) An analysis of the communicability of musical predication: a feasibility study for artistic decision support systems. PhD Thesis, University of PortsmouthGoogle Scholar
  5. Billinge D, Addis T (2003) The functioning of tropic communication: a mechanism for consistent figurative descriptions of artistic effect. AISB’03 symposium on AI and creativity in arts and science, University of Wales at AberystwythGoogle Scholar
  6. Billinge D, Addis T (2004) Music to our ears: a required paradigm shift in computer science European conference on computing and philosophy. University of Pavia, ItalyGoogle Scholar
  7. Feller W (1968) An introduction to probability theory and its applications, 3rd ed, vol 1. Wiley, NYGoogle Scholar
  8. Gooding DC, Addis TR. (2008) Foundations of Science, vol 13, No 1, March 2008.Google Scholar
  9. Hewitt C (1979) Control structures as patterns of passing messages. Artificial intelligence: an MIT perspective, vol 2. MIT, CambridgeGoogle Scholar
  10. Kuhn TS (1985) The essential tension: selected studies in scientific tradition and change. University of Chicago Press, LondonGoogle Scholar
  11. Lakoff G. (1986) Women, fire, and dangerous things. University of Chicago Press, LondonGoogle Scholar
  12. Lakoff G, Johnson M (1980) Metaphors we live by. University of Chicago Press, LondonGoogle Scholar
  13. Moroney MJ (1963) Facts from figures. Pelican Books, A236, First published in 1951. Penguin Books, LondonGoogle Scholar
  14. Popper K (1959) The logic of scientific discovery 10th Impression 1980, HutchinsonGoogle Scholar
  15. Stepney S, Braunstein S, Clark J, Tyrrel A, Adamatzky A, Smith R, Addis T, Johnson C, Timmis J, Welch P, Milner R, Partridge D (2004) Journey: non-classical philosophy—socially sensitive computing in journeys non-classical computation: a grand challenge for computing research, 18 May 2004,
  16. Weiner PP (1966) Charles S. Peirce: selected writings. Dover, New YorkGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.University of Portsmouth School of ComputingPortsmouthUnited Kingdom

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