246 13 Local, Global and Glocal Knowledge Representation . Then we turn to distributed, neural-net-like knowledge representation, reviewing a host of general issues related to knowledge representation in attractor neural networks, turning finally to “glocal” knowledge representation mechanisms, in which ANNs combine localist and globalist representation, and explaining the relationship of the latter to CogPrime. The glocal aspect of CogPrime knowledge representation will become prominent in later chapters such as: e in Chapter 23 of Part 2, where Economic Attention Networks (ECAN) are introduced and seen to have dynamics quite similar to those of the attractor neural nets considered here, but with a mathematics roughly modeling money flow in a specially constructed artificial economy rather than electrochemical dynamics of neurons. e in Chapter 42 of Part 2, where “map formation” algorithms for creating localist knowledge from globalist knowledge are described 13.2 Localized Knowledge Representation using Weighted, Labeled Hypergraphs There are many different mechanisms for representing knowledge in AI systems in an explicit, localized way, most of them descending from various variants of formal logic. Here we briefly describe how it is done in CogPrime, which on the surface is not that different from a number of prior approaches. (The particularities of CogPrime’s explicit knowledge representation, however, are carefully tuned to match CogPrime’s cognitive processes, which are more distinctive in nature than the corresponding representational mechanisms.) 13.2.1 Weighted, Labeled Hypergraphs One useful way to think about CogPrime’s explicit, localized knowledge representation is in terms of hypergraphs. A hypergraph is an abstract mathematical structure [Bol98], which con- sists of objects called Nodes and objects called Links which connect the Nodes. In computer science, a graph traditionally means a bunch of dots connected with lines (i.e. Nodes connected by L