Epstein Secrets

sambigua- tion that needs to be done to get the structure of the resultant Atom-set correct. Any further disambiguation is left to be done later, by MindAgents acting on the Atom-sets after they've already been placed in the AtomSpace. • In the old approach, the RelEx2Frame rules attempted, in many cases,

current OpenCog NL comprehension pipeline, the RelEx2Frame component uses hand-coded rules to map RelEx output into sets of relationships utilizing FrameNet and other similar semantic resources. This is definitively viewed as a "stopgap" without a role in a human- level AGI system, but it's described her

onents in practical projects, including theme with proto-AGI intentions. The syntax currently used for describing semantic relationships drawn from FrameNet and other sources is exemplified by the example Al_Benefit:Benefitor(give,$varl) The n indicates the data source, where 1 is a number indicating t

hich 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 "adva

ilation 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, l

• Fl ame2Atom: A simple system of hand-coded rules for translating the output of RelEx2Frame (logical representation of semantic relationships using FrameNet relationships) into Atoms. • Freezing: Saving Atoms from the in-RAM AtomSpace to disk. • General Intelligence: Often used in an informal, commonsen

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

e Frame2Atom: A simple system of hand-coded rules for translating the output of RelEx2Frame (logical representation of semantic relationships using FrameNet relationships) into Atoms. e Freezing: Saving Atoms from the in-RAM Atomspace to disk. e General Intelligence: Often used in an informal, commonsen

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

e programmed in. In the latter approach, one may use knowledge from statistical corpus analysis, one may use electronic resources like WordNet and FrameNet, and one may use sophisticated, specialized tools like natural language parsers with hand-coded grammars. Rather than trying to emulate the way a h

d effort management frameworks attempt to enforce this, via minimizing the amount of effort spent by the system in getting to a certain conclusion. MindAgents operating primarily on one kind of knowledge (e.g. MOSES, PLN) may for a time seek to follow the shortest paths within their particular correspondin

• Frame2Atom: A simple system of hand-coded rules for translating the output of RelEx2Frame (logical representation of semantic relationships using FrameNet relationships) into Atoms. • Freezing: Saving Atoms from the in-RAM AtomSpace to disk. • General Intelligence: Often used in an informal, commonsen