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

TL;DR

This paper discusses using adaptive dynamic programming to understand and replicate brain intelligence, highlighting progress and new tools to address spatial complexity in modeling mammal brains.

Contribution

It introduces new approaches and tools to overcome spatial complexity challenges in modeling mammal brain intelligence using ADP.

Findings

Empirical evidence supports ADP as a model for brain intelligence.

Adaptive critic systems address engineering challenges in brain modeling.

New tools help bridge the spatial complexity gap in brain simulation.

Abstract

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.