Epstein Secrets

mpilation 319 schema learning the learning of schemata carrying out cognitive processes in more specialized, context-dependent ways than the general MindAgents do. Eventually, once a CogPrime instance becomes sufficiently complex and advanced, these cognitive schema may replace the MindAgents altogether, lea

which data and procedure are explicitly identical, and there is just one new active data structure in place of the distinction between AtomSpace and MindAgents Where on this continuum does the "mere learning" end and the "real self-modification" start? In this chapter we consider some mechanisms for "advan

rations) Extension: In PLN, the extension of a node refers to the instances of the category that the node represents. In contrast is the intension. Fishgram (Frequent and Interesting Sub-hypergraph Mining): A pattern mining algorithm for identifying frequent and/or interesting sub-hypergraphs in the Atoms

idual cognitive processes. The CogPrime design gravitates more toward the latter approach. though also with some specific mechanisms within various MindAgents; and efforts have been made to have these specific mechanisms modulated by the generic attention allocation structures and dynamics wherever possibl

routines supplied to it by an external source (e.g. another AI system such as OpenCog) 2. Utilizing one of OpenCogPrime's cognitive processes (the "Fishgram" frequent subhyper- graph mining algorithm) to recognize patterns in sets of DeSTIN states, and then recording these patterns in OpenCogPrime's Atom

links that could simply be found by the pattern matcher when trying to find variable assignments to satisfy (El, E2, E3, E4}. 36.4.1 Breakdown into MindAgents To make this sort of PLN dynamic work, we require a number of MindAgents to be operating "ambiently" in the background whenever inference is occurr

a greedy search, so it can quickly find many patterns. In contrast, algorithms like MOSES are designed to find a small number of the best patterns. Fishgram works by finding a set of objects that have links in common, so it will be most effective if the AtomSpace has a lot of raw data, with simple patter