| Authors | Reggia JA, Tagamets M, Contreras-Vidal J, Jacobs DW, Weems S, Naqvi W, Winder R, Chabuk T, Jung J, Yang C |
| Abstract | In Part 1 of this report, we outlined a framework for creating an intelligent agentbased upon modeling the large-scale functionality of the human brain. Building on
those results, we begin Part 2 by specifying the behavioral requirements of a
large-scale neurocognitive architecture. The core of our long-term approach remains
focused on creating a network of neuromorphic regions that provide the mechanisms
needed to meet these requirements. However, for the short term of the next few years,
it is likely that optimal results will be obtained by using a hybrid design that
also includes symbolic methods from AI/cognitive science and control processes from the
field of artificial life. We accordingly propose a three-tiered architecture that
integrates these different methods, and describe an ongoing computational study of a
prototype 'mini-Roboscout' based on this architecture. We also examine the implications
of some non-standard computational methods for developing a neurocognitive agent.
This examination included computational experiments assessing the effectiveness of
genetic programming as a design tool for recurrent neural networks for sequence
processing, and experiments measuring the speed-up obtained for adaptive neural
networks when they are executed on a graphical processing unit (GPU) rather than a
conventional CPU. We conclude that the implementation of a large-scale neurocognitive
architecture is feasible, and outline a roadmap for achieving this goal.
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