Modeling Common-Sense Decisions in Artificial Intelligence
- Saturday, 01 May 2010
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.
Autonomy is imparted to the decision-making process by feedback from mental to motor dynamics. This feedback replaces unavailable external information by information stored in the internal knowledge base. Representation of the dynamical models in a parameterized form reduces the task of commonsense-based decision making to a solution of the following hetero-associated-memory problem: store a set of m predetermined stochastic processes given by their probability distributions in such a way that when presented with an unexpected change in the form of an input out of the set of M inputs, the coupled motor-mental dynamics converges to the corresponding one of the m pre-assigned stochastic process, and a sample of this process represents the decision.
This work was done by Michail Zak of Caltech for NASA’s Jet Propulsion Laboratory. For more information, download the Technical Support Package (free white paper) at www.techbriefs.com/tsp under the Information Sciences category. NPO-44114
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