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An Information-Theoretic Predictive Model for the Accuracy of AI Agents Adapted from Psychometrics

  • Nader Chmait
  • David L. Dowe
  • Yuan-Fang Li
  • David G. Green
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10414)

Abstract

We propose a new model to quantitatively estimate the accuracy of artificial agents over cognitive tasks of approximable complexities. The model is derived by introducing notions from algorithmic information theory into a well-known (psychometric) measurement paradigm called Item Response Theory (IRT). A lower bound on accuracy can be guaranteed with respect to task complexity and the breadth of its solution space using our model. This in turn permits formulating the relationship between agent selection cost, task difficulty and accuracy as optimisation problems. Further results indicate some of the settings over which a group of cooperative agents can be more or less accurate than individual agents or other groups.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Nader Chmait
    • 1
  • David L. Dowe
    • 1
  • Yuan-Fang Li
    • 1
  • David G. Green
    • 1
  1. 1.Faculty of Information TechnologyMonash UniversityClaytonAustralia

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