A BOLTZMANN MACHINE FOR THE ORGANIZATION OF INTELLIGENT MACHINES

Recent technological developments have fostered a need for the development and utilization of machines that contain enough intelligence to perform autonomous tasks in uncertain environments. A three-tier structure consisting of organization, coordination, and execution levels forms the architecture of an intelligent machine under the principle of increasing precision with decreasing intelligence from hierarchically intelligent control. This system has been formulated as a probabilistic model, where uncertainty and imprecision can be expressed in terms of entropies. The optimal strategy for decision planning and task execution by the intelligent machine can be found by minimizing the total entropy in the system. The focus is on the design of the organization level of the intelligent machine as a Boltzmann machine, as described in current neural network literature. Since this level is responsible for planning, the actions of the machine, the problem at this tier is formulated as the construction of the right sequence of tasks or events that minimizes the entropy for the desired action. The Boltzmann machine is reformulated to use entropy as the cost function to be minimized. Simulated annealing, expanding subinterval random search, and the genetic algorithm are presented as search techniques to efficiently find the desired action sequence. The genetic algorithm is modified to ensure convergence in probability to the minimum entropy of the system. Simulations of these algorithms are shown and performance is analyzed. © 1990 IEEE