Using ADP to Understand and Replicate Brain Intelligence: the Next Level Design

@article{Werbos2007UsingAT,
  title={Using ADP to Understand and Replicate Brain Intelligence: the Next Level Design},
  author={P. J. Werbos},
  journal={2007 IEEE International Symposium on Approximate Dynamic Programming and Reinforcement Learning},
  year={2007},
  pages={209-216}
}
Since the 1960's the author proposed that we could understand and replicate the highest level of intelligence seen in the brain, by building ever more capable and general systems for adaptive dynamic programming (ADP) - like "reinforcement learning" but based on approximating the Bellman equation and allowing the controller to know its utility function. Growing empirical evidence on the brain supports this approach. Adaptive critic systems now meet tough engineering challenges and provide a… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 42 CITATIONS

Toward a Smart Grid: Integration of computational intelligence into Power Grid

  • The 2010 International Joint Conference on Neural Networks (IJCNN)
  • 2010
VIEW 4 EXCERPTS
CITES BACKGROUND
HIGHLY INFLUENCED

Four nonlinear multi-input multi-output ADHDP constructions and algorithms based on topology principle

  • 2017 4th International Conference on Systems and Informatics (ICSAI)
  • 2017
VIEW 1 EXCERPT
CITES METHODS

The Optimal Control of Heuristic Dynamic Programming in Molecular Distillation

  • 2017 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC)
  • 2017

Multiple Actor-Critic Structures for Continuous-Time Optimal Control Using Input-Output Data

  • IEEE Transactions on Neural Networks and Learning Systems
  • 2015
VIEW 3 EXCERPTS
CITES BACKGROUND & METHODS