Abstract
Since the 1960’s I 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 kind of first-generation model of the brain. Lewis, Prokhorov and I have done some work on second-generation designs. I now argue that mammal brains possess three core capabilities – creativity/imagination and ways to manage spatial and temporal complexity – even beyond the second generation. This chapter reviews previous progress, and describes new tools and approaches to overcome the spatial complexity gap. The Appendices discuss what we can learn about higher functions of the human mind from this kind of mathematical approaches.
Keywords
- Extend Kalman Filter
- Mammal Brain
- Football Player
- Bellman Equation
- Optimal Power Flow
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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Werbos, P.J. (2007). Using ADP to Understand and Replicate Brain Intelligence: The Next Level Design?. In: Perlovsky, L.I., Kozma, R. (eds) Neurodynamics of Cognition and Consciousness. Understanding Complex Systems. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73267-9_6
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