Using Adaptive Dynamic Programming to Understand and Replicate Brain Intelligence: the Next Level Design

Biology – Quantitative Biology – Neurons and Cognition

Scientific paper

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13p. Preprint for invited talk, IEEE Conference on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL07), expanded

Scientific paper

Since the 1960s 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), which is 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 myself have early second-generation work. Mammal brains possess three core capabilities, creativity/imagination and ways to manage spatial and temporal complexity, even beyond the second generation. This paper reviews previous progress, and describes new tools and approaches to overcome the spatial complexity gap.

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