Confabulation (neural Networks) - Computational Inductive Reasoning

Computational Inductive Reasoning

The term confabulation is also used by R. Hecht-Nielsen in describing inductive reasoning accomplished via Bayesian networks. Confabulation is used to select the expectancy of the concept that follows a particular context. This is not an Aristotelian deductive process, although it yields simple deduction when memory only holds unique events. However, most events and concepts occur in multiple, conflicting contexts and so confabulation yields a consensus of an expected event that may only be minimally more likely than many other events. However, given the winner take all constraint of the theory, that is the event/symbol/concept/attribute that is then expected. This parallel computation on many contexts is postulated to occur in less than a tenth of a second. Confabulation grew out of vector analysis of data retrieval like that of latent semantic analysis and support vector machines. It is currently used to detect credit card fraud. It is being implemented computationally on parallel computers.