Speculative scientific inference via synergetic combination of probabilistic logic and evolutionary pattern recognition

Abstract

The OpenCogPrime cognitive architecture is founded on a principle of “cognitive synergy” – judicious combination of different cognitive algorithms, acting on different types of memory, in a way that helps overcome the combinatorial explosions each of the algorithms would suffer if used on its own. Here one manifestation of the cognitive synergy principle is explored – the use of probabilistic logical reasoning (based on declarative knowledge) to generalize procedural knowledge gained by evolutionary program learning. The use of this synergy is illustrated via an example drawn from a practical application of the OpenCog system to the analysis of gene expression data, wherein the MOSES program learning algorithm is used to recognize data patterns and the PLN inference engine is used to generalize these patterns via cross-referencing them with a biological ontology. This is a case study of both automated scientific inference, and synergetic cognitive processing.