Distributed Inference and Fusion Algorithms for Real-Time Supervisory Controller Positions

Abstract
The failure of traditional expert system algorithms and mathematics in real-time tasks is described, and an alternative inference engine design more appropriate to real-time problem solving is explored. The approach is generic, but to facilitate its description an example is used involving human supervisory operators of a semiautomated telecommunication network. Each human's real-time process control tasks are first decomposed into several types of subtasks under cases of certain, uncertain, and conflicting information. Two divergent calculi are seen to be required for these various tasks: 1) the "situational calculus" for real-time deterministic control, and 2) the "calculus of uncertainty" for fusion of conflicting information from diverse knowledge sources and for propagating uncertainty through the inference net to reach conclusions that can be executed by the situational calculus. The distributed architecture ramifications of a dual-calculus appoach are explained, and the fusion technique is elaborated.

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