Epstein Secrets

s. - We have not yet thought through exactly how rich the semantic formalism should be for handling th

l-symbolic network is a good representation for directly storing many kinds of memory, and interfacing between those that it doesn't store directly. CogPrinte's AtomSpace is a neural-symbolic network designed to work nicely with PLN, MOSES, ECAN and the other key CogPrime cognitive processes; it supplies

ine instances of matter into new ones in a fairly rich way: e.g. glue or tie solids togethermix liquids

d quite distant from the agent's current sensory observations; but it may still be formally evaluated based on the agent's history. In the standard CogPrinte notation as introduced formally in Chapter 20 (where indentation has function-argument syntax similar to that in Python, and relationship types are

twork, in which a hierarchy exists, along with a rich network of heterarchical links joining nodes in t

this portion to the rest of the heterarchy. A rigorous mathematical analysis of the formation of hierarchical, heterarchical and dual networks in CogPrinte systems has not yet been undertaken, and would certainly be an inter- esting enterprise. Similar to the theory of small world networks, there is ampl