276 14 Representing Implicit Knowledge via Hypergraphs

276 14 Representing Implicit Knowledge via Hypergraphs Cognitive Equation Principle: In an intelligent system, many abstract patterns that are present in the system at a certain time as patterns among other Schema and Concepts, will at a near-future time be present in the system as patterns among elementary system components. The Cognitive Equation Principle, briefly discussed in Chapter 3, basically means that Con- cepts and Schema emergent in the system are recognized by the system and then embodied as elementary items in the system so that patterns among them in their emergent form be- come, with the passage of time, patterns among them in their directly-system-embodied form. This is a natural consequence of the way intelligent systems continually recognize patterns in themselves. Note that derived hypergraphs may be constructed corresponding to any complex system which demonstrates a variety of internal dynamical patterns depending on its situation. How- ever, if a system is not intelligent, then according to the patternist philosophy evolution of its derived hypergraph can’t necessarily be expected to follow the above principles. 14.4.1 SMEPH Principles in CogPrime We now more explicitly elaborate the application of these ideas in the CogPrime context. As noted above, in addition to explicit knowledge representation in terms of Nodes and Links, CogPrime also incorporates implicit knowledge representation in the form of what are called Maps: collections of Nodes and Links that tend to be utilized together within cognitive processes. These Maps constitute a CogPrime system’s derived hypergraph, which will not be iden- tical to the hypergraph it uses for explicit knowledge representation. However, an interesting feedback loop arises here, in that the intelligence’s self-study will generally lead it to recognize large portions of its derived hypergraph as patterns in itself, and then embody these patterns within its concretely implemented knowledge hypergraph. This relates to the Cognitive Equa- tion Principle defined above 3, in which an intelligent system continually recognizes patterns in itself and embodies these patterns in its own basic structure (so that new patterns may more easily emerge from them). Often it happens that a particular CogPrime node will serve as the center of a map, so that e.g. the Concept Link denoting cat will consist of a number of nodes and links roughly centered around a ConceptNode that is linked to the WordNode cat. But this is not guaranteed and some CogPrime maps are more diffuse than this with no particular center. Somewhat similarly, the key SMEPH dynamics are represented explicitly in CogPrime: prob- abilistic reasoning is carried out via explicit application of PLN on the CogPrime hypergraph, evolutionary learning is carried out via application of the MOSES optimization algorithm, and attention allocation is carried out via a combination of inference and evolutionary pattern min- ing. But the SMEPH dynamics also occur implicitly in CogPrime: emergent maps are reasoned on probabilistically as an indirect consequence of node-and-link level PLN activity; maps evolve as a consequence of the coordinated whole of CogPrime dynamics; and attention shifts between maps according to complex emergent dynamics. To see the need for maps, consider that even a Node that has a particular meaning attached to it - like the Iraq Node, say - doesn’t contain much of the meaning of Jrag in it. The meaning of frag lies in the Links attached to this Node, and the Links attached to their Nodes - and the other Nodes and Links not explicitly represented in the system, which will be created by HOUSE_OVERSIGHT_013192