When Thinking Never Comes to a Halt: Using Formal Methods in Making Sure Your AI Gets the Job Done Good Enough

  • Tarek R. BesoldEmail author
  • Robert Robere
Part of the Synthese Library book series (SYLI, volume 376)


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 human-level AI in its attempt to recreate intelligence and capacities inspired by the human mind is dealing with finite systems, transferring this thesis and adapting it accordingly may give rise to insights that can help in progressing towards meeting the classical goal of AI in creating machines equipped with capacities rivaling human intelligence. Therefore, we develop the “Tractable Artificial and General Intelligence Thesis” and corresponding formal models usable for guiding the development of cognitive systems and models by applying notions from parameterized complexity theory and hardness of approximation to a general AI framework. In this chapter we provide an overview of our work, putting special emphasis on connections and correspondences to the heuristics framework as recent development within cognitive science and cognitive psychology.


Cognitive systems Complexity theory Parameterized complexity Approximation theory Tractable AI Approximable AI Heuristics in AI 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.The KRDB Research Centre, Faculty of Computer ScienceFree University of Bozen-BolzanoBozen-BolzanoItaly
  2. 2.Department of Computer ScienceUniversity of TorontoTorontoCanada

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