Epstein Secrets

links (such as SimilarityLinks) • adding "Hebbian weights" to other links (such as SimilarityLinks) • implicitly interpreting other links (such as SimilarityLinks) as HebbianLinks Further, these strategies may potentially be used together. There are some obvious semantic relationships to be used in interpreti

hema execution in the first place. The problem of credit assignment thus becomes a problem of creating appropriate heuristics to guide inference of ImplicationLinks of the form described above. Assignment of credit is then implicit in the calculation of truth values for these links. The difficulty is that the pr

licit low-strength SimilarityLinks in the system in these cases, is obviously much cheaper than using them for all cases. We've been talking about SimilarityLinks, but the approach is more broadly applicable. Any symmetric link type can be dealt with about the same way. For instance, it might be useful to kee

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

ch one denoting a "sense" of the word. The clusters are defined by the following relationships: 1. ConceptNodes within a cluster have high-strength SimilarityLinks to each other 2. ConceptNodes in different clusters have low-strength (i.e. dissimilarity-denoting) Similar- ityLinks to each other When a word is

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

more large, complex and tangled than even this one. In CogPrime, ECAN enforces heterarchy via building SymmetricHebbianLinks, and PLN by building SimilarityLinks, IntensionalSimilarityLinks and ExtensionalSimilarityLinks. Fur- thermore, these various link types reinforce each other. PLN control is guided by im

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

ar more large, complex and tangled than even this one. In CogPrime, ECAN enforces heterarchy via building SymmetricHebbianLinks, and PLN by building SimilarityLinks, IntensionalSimilarityLinks and ExtensionalSimilarityLinks. Fur- thermore, these various link types reinforce each other. PLN control is guided by im

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