Epstein Secrets

s of word sense creation. Suppose we have a WordNode W that has ReferenceLinks to a number of different Con- ceptNodes. A common case is that these ConceptNodes fall into clusters, each one denoting a "sense" of the word. The clusters are defined by the following relationships: 1. ConceptNodes within a clus

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

Knowledge in the Atomspace. A simple example of implicit knowledge in the Atomspace. The "chicken" and "food" concepts are represented by "maps" of ConceptNodes interconnected by HebbianLinks, where the latter tend to form between Con- ceptNodes that are often simultaneously important. The bundle of links bet

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

at contains all required packages and tools to test and develop OpenCog. • Concept Creation: A general term for cognitive processes that create new ConceptNodes, PredicateNodes or concept maps representing new concepts. • Conceptual Blending: A process of creating new concepts via judiciously combining pie

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