Modeling Common-Sense Decisions in Artificial Intelligence

Common sense is implemented partly by feedback from mental to motor dynamics.

A methodology has been conceived for efficient synthesis of dynamical models that simulate common-sense decision-making processes. This methodology is intended to contribute to the design of artificial-intelligence systems that could imitate human commonsense decision making or assist humans in making correct decisions in unanticipated circumstances. This methodology is a product of continuing research on mathematical models of the behaviors of single- and multi-agent systems known in biology, economics, and sociology, ranging from a single-cell organism at one extreme to the whole of human society at the other extreme. Earlier results of this research were reported in several prior NASA Tech Briefs articles, the three most recent and relevant being “Characteristics of Dynamics of Intelligent Systems” (npo-21037), NASA Tech Briefs, Vol. 26, No. 12 (December 2002), page 48; “Self-Supervised Dynamical Systems” (npo-30634), NASA Tech Briefs, Vol. 27, No. 3 (March 2003), page 72; and “Complexity for Survival of Living Systems” (npo-43302), NASA Tech Briefs, Vol. 33, No. 7 (July 2009), page 62.