Epstein Secrets

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

chActSchema and SpeechActTriggers) corresponding to fine-grained types of speech act • Dialogue control guided by general cognitive control system (OpenPsi, running within Open Cog) • SpeechActSchema and SpeechActTriggers, in some cases, will internally consult proba- bilistic inference, thus supplying

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

that recognizes patterns in the hierarchies and passes them to the Atomspace; and imprints patterns from the Atomspace on the hierarchies); and also OpenPsi, a special dynam- ical framework for choosing actions based on motivations. Above the Atomspace we have a host of cognitive processes, which act on

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

(that recognizes patterns in the hierarchies and passes them to the Atomspace; and imprints patterns from the Atomspace on the hierarchies); and also OpenPsi, a special dynam- ical framework for choosing actions based on motivations. Above the Atomspace we have a host of cognitive processes, which act on t