Among the difficulties in evaluating AItype medical diagnosis systems are: the intermediate conclusions of the AI system need to be looked at in addition to the "final" answer; the "superhuman human" fallacy must be resisted; both pro-and anti-computer biases during evaluation must be guarded against; and methods for estimating how the approach will scale upwards to larger domains are needed We propose a type of Turing test for the evaluation problem, designed to provide some protection against the problems listed above We propose to measure both the accuracy of diagnosis and the structure of reasoning, the latter with a view to gauging how well the system will scale up A staple of many of the evaluations of AI systems that have so far been conducted (Colby, Hilf, Weber, 81 Kraemer, 1972; Yu et al, 1979) is a central idea from a well-known proposal to evaluate AI systems: The Turing Test (Turing, 1963) The meat of the idea is to see if a neutral observer, given a set of performances on a task, some by a machine and others by humans, but unlabelled as to authorship, could identify, better than chance, which were machine and which were human-produced. Note that this really attempts to answer the question, "DO we know how to design a machine to perform a task which until now required human intelligence?", The latter question subsumes the former in a sense: because the machine not performing well in comparison to a human would presumably increase the cost significantly. In this paper I follow tradition and consider the evaluation of AI systems for medical diagnosis from the viewpoint of the first question above. The proposed procedure is also a variant of Turing's Test.

Most high-performance expert systems rely primarily on an ability to represent surface knowledge about associations between observable evidence or data, on the one hand, and hypotheses or classifications of interest, on the other. Although the present generation of practical systems shows that this architectural style can be pushed quite far, the limitations of current systems motivate a search for representations that would allow expert systems to move beyond the prevalent "symptom-disease" style. One approach that appears promising is to couple a rule-based or associational system module with some other computational model of the phenomenon or domain of interest. According to this approach, the domain knowledge captured in the second model would be selected to complement the associational knowledge represented in the first module. Simulation models have been especially attractive choices for the complementary representation because of the causal relations embedded in them (Brown & Burton, 1975; Cuena, 1983).

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. Diagnosis has historically provided an obliging rock for each succeeding generation of AI researchers to blunt their axes on.

I report on my experience over the past few years in introducing automated, model-based diagnostic technologies into industrial settings. In particular, I discuss the competition that this technology has been receiving from handcrafted, rule-based diagnostic systems that has set some high standards that must be met by model-based systems before they can be viewed as viable alternatives. The battle between model-based and rulebased approaches to diagnosis has been over in the academic literature for many years, but the situation is different in industry where rule-based systems are dominant and appear to be attractive given the considerations of efficiency, embeddability, and cost effectiveness. Traditionally, industrial diagnostic systems have been handcrafted to reflect the knowledge of a domain expert. They take the form of if-then rules that associate certain forms of abnormal system behavior with faults that could have caused this behavior.

The eighteenth annual International Workshop on Principles of Diagnosis was held in Nashville, Tennessee, May 29-31, 2007. Papers presented at the workshop covered a variety of theories, principles, and computational techniques for diagnosis, monitoring, testing, reconfiguration, fault-adaptive control, and repair of complex systems. Before deployment they are subjected to strict testing and validation. Although these procedures reduce the likelihood of initial system failures, degradation and faults in system components still occur because of wear and tear from sustained operations. Industry sources, service agencies, and the military report that down time, due to maintenance and repairs of equipment, is still a significant cost of daily operations.

Through the technology of expert systems, the expertise of highly skilled personnel can be automated and used to assist lesser skilled personnel in the diagnosis and repair of complex machines. Expert systems that incorporate causal reasoning represent a second-generation approach to the provision of diagnostic assistance. The technology involved performs postdiction by reasoning from first principles. This article is based on research in qualitative physics and the philosophy of causality. A new implementation vehicle for causal reasoning is described, one that embodies hypothetical or counterfactual reasoning (Roach, Eichelman, and Whitehead 1985) in a language called Wif (What IF).

This article describes a common diagnostic framework used to evaluate these algorithms. These competitions, started in 2009, have significantly helped shape subsequent diagnostic algorithms. Diagnostic algorithms (DAs) (1) detect malfunctioning systems, (2) isolate the faulty component or components that cause the malfunction, and possibly (3) repair the system to restore its functionality. The fundamental challenge of diagnosis is that the system is only partially observable. Therefore, diagnostic algorithms must reason backwards from symptoms to causes.

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When old man winter comes to town, it's important to make sure you and your family are ready for more than just a heavy snow fall. We recently got this email from a concerned parent. Dear Dr. Manny, My kids wait about 10-15 minutes for their school bus every morning, should I be worried that they could get frostbite while they wait? Frostbite is a serious medical condition that occurs when the skin and underlying tissues literally freeze. Since kids lose more heat from their skin than adults, they are at an increased risk for developing the condition.

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