BRIAN: A Basic Regimen for Intelligent Analysis Using Networks

A method for machine-learning by analogy, comprising: providing a network of component computer systems, each component having sequential layers, each layer having parallel segments, each segment comprises a processor and a memory store, each memory store is configured to store a domain-specific case base, which is defined by a situation-action pair; stochastically transforming cases in each case base through automatic deterministic generalization and analogy when the corresponding processor is in a dream mode to create transformed cases; providing a user-entered situation; modifying the user-entered situation by expanding its contextual mnemonics; searching each case base for cases and transformed cases that include contextual subsets of the modified, user-entered situation; for a given case base, mapping the modified, user-entered situation to a matched action within the given case base; creating a new case comprising the user-entered situation and the matched action.