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ssumption about the nature of the environments that intelligent agents are operating in. As discussed in roe06] it requires an assumption related to Peirce's philosophical axiom of the "tendency to take habits," which posits that in the real world, entities possessing some similar patterns have a proba
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
ing according to Charles Sanders Peirce's theory of semiosis [Pei341, in which there are three basic types of signs: icons, indices and symbols. In Peirce's ontology of semiosis, an icon is a sign that physically resembles what it stands for. Representational pictures, for example, are icons because th
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
The Origin of Life. Dover, 1952. Pal82. Gunter Palm. Neural Assemblies. An Alternative Approach to Artificial Intelligence. Springer, 1982. Pei34. C, Peirce. Collected papers: Volume V. Pragmatism and pragmaticism. Harvard University Press. Cambridge MA., 1934. Pel05. Martin Pelikan. Hierarchical Bayesian
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
Entities connected to both Peirce and ConceptNodes

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