Analytic formulation of the principle of increasing precision with decreasing intelligence for intelligent machines

Intelligent machines, like intelligent robots are capable of performing autonomously in uncertain environments, and have imposed new design requirements for modern engineers. New concepts, drawn from areas like artificial intelligence, operations research and control theory, are required in order to implement anthropomorphic tasks with minimum intervention of an operator. This work deals with the analytic formulation of the principle of increasing precision with decreasing intelligence; the fundamental principle of hierarchically intelligent control. A three-level structure representing the organization, coordination and execution has been developed as a probabilistic model of such a system and the approaches necessary to implement each one of them on an intelligent machine are discussed. The principle is derived also from a probabilistic model and can be expressed in terms of entropies. It is compatible with the current formulation of the hierarchically intelligent control problem, the mathematical programming solution of which minimizes the total entropy. The derivation and design of parallel architectures for artificial intelligence, like the Boltzmann machine is obtained from such formulation.