Epstein Secrets

MemberLinks. This is in accordance with the notion of glocal memory, in the language of which the HPAN attractors are the maps and the corresponding ConceptNodes are the keys. Put simply, when a HPAN attractor is recognized, MemberLinks are created between the HPAN nodes comprising the main body of the attra

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

usual, but making use of Metallodes as well as ordinary CT nodes. The modeling of Metallodes is quite similar to the modeling of Nodes representing ConceptNodes and PredicateNodes using embedding vectors. In this way, one can use standard, small decision tree models to model fairly large portions of CTs (be

resent is carried out, either by - PLN inference within OpenCog, drawing the conclusion that a block is present from the exported predicates, using ImplicationLinks comprising a working definition of a "block" - A predicate comprising the definition of "block", previously imported into DeSTIN from OpenCog and

y, there may be patterns across large numbers of nodes that merit encapsulation in individual nodes. For instance, if there are 1000 fairly similar ConceptNodes, it may be better not to merge them all together, but rather to create a single node to which they all link, reifying the category that they collect

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

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

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

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

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

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

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

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

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