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Many tasks require “reasoning”—i.e., deriving conclusions from a corpus of explicitly stored information—to solve their range of problems. An ideal reasoning system would produce all-and-only the correct answers to every possible query, produce answers that are as specific as possible, be expressive enough to permit any possible fact to be stored and any possible query to be asked, and be (time) efficient . Unfortunately, this is provably impossible: as correct and precise systems become more expressive, they can become increasingly inefficient, or even undecidable. This survey first formalizes these hardness results, in the context of both logic- and probability-based reasoning, then overviews the techniques now used to address, or at least side-step, this dilemma.

This paper presents a new approach to nuclear plant fault diagnosis using probabilistic reasoning techniques, specifically, a Bayesian network. The scheme is well suited to the task since the symptoms of certain faults are ambiguous. This approach provides a way to capture the knowledge and reach rational decisions in uncertain domains by casting the decision-making process as computation with a discrete probability distribution represented by a causal network. This scheme, unlike some other learning schemes, supports a mathematical explanation of the results, which is necessary in many critical applications.A brief review of probabilistic reasoning via Bayesian networks is provided. Learning the probability values from expert beliefs and statistical data is discussed. The system design process and architecture are explained, and some performance measurements are presented. This module will be deployed as part of the Situation-Related Operator Guidance system (intelligent hypertext manual).

This paper presents summaries of five research and development activities in intelligent distributed simulation and control of power plants which were presented in a panel session of the same name at the IEEE Power Engineering Society Winter Meeting on February 6, 1997 in New York City. Each of the panelists discussed methods of how they have incorporated intelligent systems techniques into their research and development efforts in power plant control. The panel was organized by the Working Group on Intelligent Methods in Station Control, Station Control Subcommittee, and the Energy Development and Power Generation Committee of the IEEE Power Engineering Society. Index Terms-Control room evolution, instrumentation and control, intelligent control, power plant automation, real-time monitoring.