The Pragmatic Roots of Context

  • Bruce Edmonds
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1688)

Abstract

When modelling complex systems one can not include all the causal factors, but one has to settle for partial models. This is alright if the factors left out are either so constant that they can be ignored or one is able to recognise the circumstances when they will be such that the partial model applies. The transference of knowledge from the point of application to the point of learning utilises a combination of recognition and inference — a simple model of the important features is learnt and later situations where inferences can be drawn from the model are recognised. Context is an abstraction of the collection of background features that are later recognised. Different heuristics for recognition and model formulation will be effective for different learning tasks. Each of these will lead to a different type of context. Given this, there two ways of modelling context: one can either attempt to investigate the contexts that arise out of the heuristics that a particular agent actually applies or one can attempt to model context using the external source of regularity that the heuristics exploit. There are also two basic methodologies for the investigation of context: a top-down approach where one tries to lay down general, a priori principles and a bottom-up approach where one can try and find what sorts of context arise by experiment and simulation. A simulation is exhibited which is designed to illustrate the practicality of the bottom-up approach in elucidating the sorts of internal context that arise in an artificial agent which is attempting to learn simple models of a complex environment.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Akman, V. (1997). Context as a Social Construct. Context in Knowledge Representation and Natural Language, AAAI Fall Symposium, November 1997, MIT, Cambridge.Google Scholar
  2. 2.
    Akman, V. and Surav, M. (1996). Steps Towards Formalizing Context. AI Magazine, 17:55–72.Google Scholar
  3. 3.
    Barwise, J. and Perry, J. (1983). Situations and Attitudes. Cambridge: MIT Press.Google Scholar
  4. 4.
    Chialvo, D. R. and Bak, P. (1997). Learning by Mistakes. Sante Fe Working Paper 97-08-077.Google Scholar
  5. 5.
    Drescher, G. L. (1991). Made-up Minds — A Constructivist Approach to Artificial Intelligence. Cambridge, MA: MIT Press.MATHGoogle Scholar
  6. 6.
    Edmonds, B. (1998). Modelling Socially Intelligent Agents. Applied Artificial Intelligence, 12: 677–699.CrossRefGoogle Scholar
  7. 7.
    Edmonds, B. (in press). Modelling Bounded Rationality In Agent-Based Simulations using the Evolution of Mental Models.In Brenner, T. (ed.), Computational Techniques for Modelling Learning in Economics, Kluwer.Google Scholar
  8. 8.
    Edmonds, B. (1999). Capturing Social Embeddedness: a Constructivist Approach. Adaptive Behaviour, 7(3/4).Google Scholar
  9. 9.
    Edmonds, B. A Simple-Minded Network Model with Context-like Objects. European Conference on Cognitive Science (ECCS’97), Manchester, April 1997. (http://www.cpm.mmu.ac.uk/cpmrep15.html)
  10. 10.
    Hayes, P. (1995). Contexts in Context. Context in Knowledge Representation and Natural Language, AAAI Fall Symposium, November 1997, MIT, Cambridge.Google Scholar
  11. 11.
    Hirst, G. (1997). Context as a Spurious Concept. Context in Knowledge Representation and Natural Language, AAAI Fall Symposium, November 1997, MIT, Cambridge.Google Scholar
  12. 12.
    McCarthy, J. (1996). A logical AI approach to context. Unpublished note, 6 February 1996. http://www-formal.stanford.edu/jmc/logical.html
  13. 13.
    Moss, S., Gaylard, H., Wallis, S. and Edmonds, B. (1998). SDML: A Multi-Agent Language for Organizational Modelling. Computational and Mathematical Organization Theory, 4, 43–69.CrossRefGoogle Scholar
  14. 14.
    Palmer, R.G. et. al. (1994). Artificial Economic Life — A Simple Model of a Stockmarket. Physica D, 75:264–274.MATHCrossRefMathSciNetGoogle Scholar
  15. 15.
    Pearl, J. (forthcoming). An Axiomatic Characteriztion of Causal Counterfactuals. Foundations of Science.Google Scholar
  16. 16.
    Trun S. and Mitchell, T. M. (1995). Learning One More Thing. Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI’95). San Mateo, CA: Morgan Kaufmann, 1217–1223.Google Scholar
  17. 17.
    Vaario, J. (1994). Artificial Life as Constructivist AI. Japanese Society of Instrument and Control Engineers, 33:65–71.Google Scholar
  18. 18.
    Wagner, A. (1997). Causality in Complex Systems. Sante Fe Working Paper 97-08-075.Google Scholar
  19. 19.
    Wheeler, M. and Clark, A. (forthcoming). Genic Representation: Reconciling Content and Causal Complexity. British journal for the Philosophy of Science.Google Scholar
  20. 20.
    Widmer, G. (1997). Tracking Context Changes through Meta-Learning. Machine Learning, 27:259–286.CrossRefGoogle Scholar
  21. 21.
    Zadrozny, W. (1997). A Pragmatic Approach to Context. Context in Knowledge Representation and Natural Language, AAAI Fall Symposium, November 1997, MIT, Cambridge.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1999

Authors and Affiliations

  • Bruce Edmonds
    • 1
  1. 1.Centre for Policy ModellingManchester Metropolitan UniversityManchesterUK

Personalised recommendations