Advertisement

A Note on Tractability and Artificial Intelligence

  • Tarek Richard Besold
  • Robert Robere
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7999)

Abstract

The recognition that human minds/brains are finite systems with limited resources for computation has led researchers in Cognitive Science to advance the Tractable Cognition thesis: Human cognitive capacities are constrained by computational tractability. As also artificial intelligence (AI) in its attempt to recreate intelligence and capacities inspired by the human mind is dealing with finite systems, transferring the Tractable Cognition thesis into this new context and adapting it accordingly may give rise to insights and ideas that can help in progressing towards meeting the goals of the AI endeavor.

Keywords

Common Sense Reasoning Much Probable Explanation CEUR Workshop Proceeding Computer Metaphor Computational Cognitive Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Frixione, M.: Tractable competence. Minds and Machines 11, 379–397 (2001)zbMATHCrossRefGoogle Scholar
  2. 2.
    Flum, J., Grohe, M.: Parameterized Complexity Theory. Springer (2006)Google Scholar
  3. 3.
    Downey, R.G., Fellows, M.R.: Parameterized Complexity. Springer (1999)Google Scholar
  4. 4.
    van Rooij, I.: The tractable cognition thesis. Cognitive Science 32, 939–984 (2008)Google Scholar
  5. 5.
    Nebel, B.: Artificial intelligence: A computational perspective. In: Brewka, G. (ed.) Principles of Knowledge Representation, pp. 237–266. CSLI Publications (1996)Google Scholar
  6. 6.
    Oaksford, M., Chater, N.: Précis of bayesian rationality: The probabilistic approach to human reasoning. Behavioral and Brain Sciences 32, 69–84 (2009)CrossRefGoogle Scholar
  7. 7.
    Kwisthout, J., Wareham, T., van Rooij, I.: Bayesian intractability is not an ailment that approximation can cure. Cognitive Science 35(5), 779–784 (2011)CrossRefGoogle Scholar
  8. 8.
    van Rooij, I.: Rationality, intractability and the prospects of “as if” explanations. In: Szymanik, J., Verbrugge, R. (eds.) Proc. of the Logic & Cognition Workshop at ESSLLI 2012. CEUR Workshop Proceedings, vol. 883, CEUR-WS.org (August 2012)Google Scholar
  9. 9.
    Downey, R.G., Fellows, M.R., Stege, U.: Parameterized complexity: A framework for systematically confronting computational intractability. In: Contemporary Trends in Discrete Mathematics: From DIMACS and DIMATIA to the Future, AMS (1997)Google Scholar
  10. 10.
    Anderson, J.R.: Cognitive Psychology and Its Implications. W. H. Freeman and Company (1985)Google Scholar
  11. 11.
    Gigerenzer, G., Hertwig, R., Pachur, T. (eds.): Heuristics: The Foundation of Adaptive Behavior. Oxford University Press (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Tarek Richard Besold
    • 1
  • Robert Robere
    • 2
  1. 1.Institute of Cognitive ScienceUniversity of OsnabrückGermany
  2. 2.Department of Computer ScienceUniversity of TorontoCanada

Personalised recommendations