The manufacturing plant represents a complex environment, rife with uncertainty. The complexity arises from the multitude of interactions that must be considered when attempting to model most manufacturing processes. Important process variables can remain unidentified; or even if they are identified, their interactions may remain uncertain. This complexity and the uncertainties that are often its derivatives cause various inefficiencies when conventional control methods are employed. In an attempt to remedy this situation, an intelligent control methodology termed zone logic has been advanced. Various extensions to it have been proposed which are designed to increase its domain of applicability. This paper further extends zone logic into the area of stochastic controls by using concepts from Bayesian belief networks. An information theoretic analysis of an initial application of stochastic zone logic is performed. This analysis indicates that an object oriented computational scheme best matches the real-time performance requirements for knowledge-based control systems.

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