Epstein Secrets

esentation in order to do their cognitive work. Toward this end we have come to a similar conclusion to some other researchers (e.g. Joscha Bach and Stan Franklin), and con- cluded that given the strengths and weaknesses of current and near-future digital computers, a (loosely) neural-symbolic network is a goo

icitly. In general, entropy maximization principles provide the foundation for learning systems such as (hidden) Markov models, Markov networks and Hopfield neural networks, and they connect indirectly with Bayesian probability based analyses. However, the actual task of maximizing the entropy is an NP-h

d drives, and would then develop complex cognitive capabilities as part of this development process. 4.5.7 LIDA The LIDA architecture developed by Stan Franklin and his colleagues [13F09] is based on the concept of the "cognitive cycle" - a notion that is important to nearly every BICA (Biologically Inspire

FTA00623877 102 5 A Generic Architecture of Human-Like Cognition touch and smell (the latter being better modeled as something like an asymmetric Hopfield net, prone to frequent chaotic dynamics ILIAV*051) - these may also cross-connect with each other and with the more hierarchical perceptual subnetw

nd drives, and would then develop complex cognitive capabilities as part of this development process. 4.5.7 LIDA The LIDA architecture developed by Stan Franklin and his colleagues [BF 09] is based on the concept of the “cognitive cycle” - a notion that is important to nearly every BICA (Biologically Inspired

RSIGHT_013017 102 5 A Generic Architecture of Human-Like Cognition touch and smell (the latter being better modeled as something like an asymmetric Hopfield net, prone to frequent chaotic dynamics [LLW~05]) — these may also cross-connect with each other and with the more hierarchical perceptual subnetwork