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Learning What to Value

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNAI,volume 6830)

Abstract

I. J. Good’s intelligence explosion theory predicts that ultraintelligent agents will undergo a process of repeated self-improvement; in the wake of such an event, how well our values are fulfilled would depend on the goals of these ultraintelligent agents. With this motivation, we examine ultraintelligent reinforcement learning agents. Reinforcement learning can only be used in the real world to define agents whose goal is to maximize expected rewards, and since this goal does not match with human goals, AGIs based on reinforcement learning will often work at cross-purposes to us. To solve this problem, we define value learners, agents that can be designed to learn and maximize any initially unknown utility function so long as we provide them with an idea of what constitutes evidence about that utility function.

Keywords

  • Utility Function
  • Reinforcement Learning
  • Expected Utility
  • Full Search
  • Agent Function

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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References

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© 2011 Springer-Verlag Berlin Heidelberg

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Dewey, D. (2011). Learning What to Value. In: Schmidhuber, J., Thórisson, K.R., Looks, M. (eds) Artificial General Intelligence. AGI 2011. Lecture Notes in Computer Science(), vol 6830. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22887-2_35

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  • DOI: https://doi.org/10.1007/978-3-642-22887-2_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22886-5

  • Online ISBN: 978-3-642-22887-2

  • eBook Packages: Computer ScienceComputer Science (R0)