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

Simple item page
dc.creatorWerbos, Paul J.
dc.date2006-12-24
dc.date2007-05-08
dc.date.accessioned2026-07-07T07:59:53Z
dc.date.available2026-07-07T07:59:53Z
dc.descriptionSince 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.
dc.description13p. Preprint for invited talk, IEEE Conference on Adaptive Dynamic Programming and Reinforcement Learning (ADPRL07), expanded into chapter in R. Kozma, Neurodynamics of Higher-Level Cognition and Consciousness, Springer, 2007. New version adds appendix E, clarifications, and a new paragraph on clocks in the brain
dc.identifierhttps://arxiv.org/abs/q-bio/0612045
dc.identifierhttp://arxiv.org/abs/q-bio/0612045
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/128539
dc.subjectNeurons and Cognition
dc.titleUsing Adaptive Dynamic Programming to Understand and Replicate Brain Intelligence: the Next Level Design
dc.typetext

Files

Collections

arXiv