Epstein Secrets

of the Syn2Sem approach mentioned above into a reverse-order. Sem2Syn system for language generation. Given that the Syn2Sem rules are expressed as ImplicationLinks, they can be reversed auto- matically and immediately - although, the reversed versions will not necessarily have the same truth values. So if a col

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

ms an action and achieves a goal. There is code to create implications based on these conjunctions. After finding many conjunctions, it can produce ImplicationLinks based on some of them. Here is an example where the Al-controlled virtual robot discovers how to get energy. (ImplicationLink (AndLink (Evaluatio

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

ergoals like gaining knowledge are not funded as much with STI currency just now, as they are not immediately relevant. Action selection, based on ImplicationLinks derived via PLN (between various possible activ- ities and the subgoal S) causes it to start experimentally building some blocks structures. Past ex

• 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

or of Link2Atom. However, the ideas embodied in these rules may be useful; if cleaned up the rules might profitably be ported into the Atomspace as ImplicationLinks. • Representation Building: A stage within MOSES, wherein a candidate Combo program tree (within a deme) is modified by replacing one or more tree

• 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

intelligent system explicitly represents in its knowledge base, and expends some resources trying to achieve. Goal Nodes (which may be Nodes or, e.g. ImplicationLinks) are used for this purpose in OpenCog. e Goal-Driven Learning: Learning that is driven by the cognitive schematic i.e. by the quest of figuring out

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