Anytime Bounded Rationality

  • Eric Nivel
  • Kristinn R. Thórisson
  • Bas Steunebrink
  • Jürgen Schmidhuber
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9205)


Dependable cyber-physical systems strive to deliver anticipative, multi-objective performance anytime, facing deluges of inputs with varying and limited resources. This is even more challenging for life-long learning rational agents as they also have to contend with the varying and growing know-how accumulated from experience. These issues are of crucial practical value, yet have been only marginally and unsatisfactorily addressed in AGI research. We present a value-driven computational model of anytime bounded rationality robust to variations of both resources and knowledge. It leverages continually learned knowledge to anticipate, revise and maintain concurrent courses of action spanning over arbitrary time scales for execution anytime necessary.


Conjunctive Model Time Semantic Arbitrary Time Scale Cognitive Cycle Disjunctive 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.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Eric Nivel
    • 1
  • Kristinn R. Thórisson
    • 1
    • 2
  • Bas Steunebrink
    • 3
  • Jürgen Schmidhuber
    • 3
  1. 1.Icelandic Institute for Intelligent MachinesReykjavikIceland
  2. 2.Reykjavik University, CADIAReykjavikIceland
  3. 3.The Swiss AI Lab IDSIA, USI & SUPSIMannoSwitzerland

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