TY - RPRT T1 - The Maryland Large-Scale Integrated Neurocognitive Architecture Y1 - 2008 A1 - Reggia, James A. A1 - Tagamets,M. A1 - Contreras-Vidal,J. A1 - Jacobs, David W. A1 - Weems,S. A1 - Naqvi,W. A1 - Yang,C. KW - *COMPUTATIONS KW - *HYBRID SYSTEMS KW - *NEURAL NETS KW - *NEUROCOGNITIVE ARCHITECTURE KW - ADAPTIVE SYSTEMS KW - Artificial intelligence KW - BRAIN KW - Cognition KW - COMPUTER PROGRAMMING KW - COMPUTER PROGRAMMING AND SOFTWARE KW - HYBRID AI KW - Machine intelligence KW - MECHANICAL ORGANS KW - MODULAR CONSTRUCTION KW - NERVOUS SYSTEM KW - PE61101E KW - PLASTIC PROPERTIES KW - PROCESSING EQUIPMENT KW - RECURRENT NEURAL NETWORK AB - Recent progress in neural computation, high performance computing, neuroscience and cognitive science suggests that an effort to produce a general-purpose, adaptive machine intelligence is likely to yield a qualitatively more powerful system than those currently existing. Here we outline our progress in developing a framework for creating such a large-scale machine intelligence, or neurocognitive architecture that is based on the modularity, dynamics and plasticity of the human brain. We successfully implemented three intermediate-scale parts of such a system, and these are described. Based on this experience, we concluded that for the short term, optimal results would be obtained by using a hybrid design including neural, symbolic AI, and artificial life methods. We propose a three-tiered architecture that integrates these different methods, and describe a prototype mini-Roboscout that we implemented and evaluated based on this architecture. We also examined, via computational experiments, the effectiveness of genetic programming as a design tool for recurrent neural networks, and the speed-up obtained for adaptive neural networks when they are executed on a graphical processing unit. We conclude that the implementation of a large-scale neurocognitive architecture is feasible, and outline a roadmap for proceeding. PB - University of Maryland College Park UR - http://stinet.dtic.mil/oai/oai?&verb=getRecord&metadataPrefix=html&identifier=ADA481261 ER -