Diagnosis of faults in complex, real-time control systems is a complicated task that has resisted solution by traditional methods. We have shown that neural networks can be successfully employed to diagnose faults in digitally controlled powertrain systems. This paper discusses the means we use to develop the appropriate databases for training and testing in order to select the optimum network architectures and to provide reasonable estimates of the classification accuracy of these networks on new samples of data.

Diagnosis of faults in complex, real-time control systems is a complicated task that has resisted solution by traditional methods. We have shown that neural networks can be successfully employed to diagnose faults in digitally controlled powertrain systems. This paper discusses the means we use to develop the appropriate databases for training and testing in order to select the optimum network architectures and to provide reasonable estimates of the classification accuracy of these networks on new samples of data.

Automated diagnosis is an important AI problem not only for its potential practical applications but also because it exposes issues common to all automated reasoning efforts and presents real challenges to existing paradigms. Current research in this area addresses many problems, including managing and structuring probabilistic information, modeling physical systems, reasoning with defeasible assumptions, and interleaving deliberation and action. Furthermore, diagnosis programs must face these problems in contexts where scaling up to deal with cases of realistic size results in daunting combinatorics. This article presents these and other issues as discussed at the First International Workshop on Principles of Diagnosis.

Automated diagnosis is an important AI problem not only for its potential practical applications but also because it exposes issues common to all automated reasoning efforts and presents real challenges to existing paradigms. Current research in this area addresses many problems, including managing and structuring probabilistic information, modeling physical systems, reasoning with defeasible assumptions, and interleaving deliberation and action. Furthermore, diagnosis programs must face these problems in contexts where scaling up to deal with cases of realistic size results in daunting combinatorics. This article presents these and other issues as discussed at the First International Workshop on Principles of Diagnosis.

Decision analysis and expert systems are technologies intended to support human reasoning and decision making by formalizing expert knowledge so that it is amenable to mechanized reasoning methods. Despite some common goals, these two paradigms have evolved divergently, with fundamental differences in principle and practice. Recent recognition of the deficiencies of traditional AI techniques for treating uncertainty, coupled with the development of belief nets and influence diagrams, is stimulating renewed enthusiasm among AI researchers in probabilistic reasoning and decision analysis. We present the key ideas of decision analysis and review recent research and applications that aim toward a marriage of these two paradigms. This work combines decision-analytic methods for structuring and encoding uncertain knowledge and preferences with computational techniques from AI for knowledge representation, inference, and explanation. We end by outlining remaining research issues to fully develop the potential of this enterprise.

AI research is conducted at a number of academic and research units at the Georgia Institute of Technology. Some of this research is basic in nature, and some has an applied character to it. This article briefly describes basic AI research in the College of Computing at Georgia Tech.

This article describes a causal expert system based on hypothetical reasoning and its application to the maintenance of the lower hoist of a Mark 45 turret gun. The system, Hoist, performs fault diagnosis without the use of a repair expert or shallow rules. Its knowledge is coded directly from a structural specification of the Mark 45 lower hoist. The technology reported here for assisting the less experienced diagnostician differs considerably from normal rule-based techniques: It reasons about machine failures from a functional model of the device. In a mechanism like the lower hoist, the functional model must reason about forces, fluid pressures, and mechanical linkages; that is, it must reason about qualitative physics. Hoist technology can be directly applied to any exactly specified device for the modeling and diagnosis of single or multiple faults. Hypothetical reasoning, the process embodied in Hoist, has general utility in qualitative physics and reason maintenance.

It (McDermott 1988), and the idea of generic also helps to explicitly focus on how to go tasks and task-specific architectures (Chandrasekaran from the knowledge level to the symbol or 1983). These various proposals are program level. I call this in-between level the obviously related to each other, which makes knowledge-use level. At the knowledge-use it desirable to construct a synthesis that combines level, we focus on issues such as how the their strengths. Such a synthesis is presented overall task will be decomposed into manageable here in the form of a componential subtasks, what ordering will be imposed framework. The framework stresses modularity on the tasks, what kind of access to knowledge and consideration of the pragmatic constraints will be needed (and, consequently, what of the domain.

Stochastic simulation is a method of computing probabilities by recording the fraction of time that events occur in a random series of scenarios generated from some causal model. This paper presents an efficient, concurrent method of conducting the simulation which guarantees that all generated scenarios will be consistent with the observed data. It is shown that the simulation can be performed by purely local computations, involving products of parameters given with the initial specification of the model. Thus, the method proposed renders stochastic simulation a powerful technique of coherent inferencing, especially suited for tasks involving complex, nondecomposable models where “ballpark” estimates of probabilities will suffice.

The development of an online turbine generator diagnostic system is described from conception to initial field verification. The system is composed of a data center located in the power plant that collects data from online measurement devices and communicates these data to a centralized diagnostic facility in Orlando, Florida, where the actual diagnosis is done. The resulting diagnosis and recommended actions are transmitted to the power plant where they are displayed to the operator by the data center. The market-place need, initial approaches to the product, system field verification are described. The artificial intelligence (AI) diagnostic program has been diagnosing seven large utility generators since July 1984 and has correctly diagnosed a significant number of generator and instrumentation problems. Issues such as a centralized approach, rule base quality control, and the range of resources needed for a successful product are discussed.