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2. BACKGROUND AND HISTORYA majority of early expert systems were diagnostic (or classification) systems, especially in the medical, electronic, and manufacturing domains (Buchanan and Shortliffe, 1984). We can better understand the reasons why model-based reasoning techniques were developed by briefly recapitulating the history of diagnostic systems. Early systems like MYCIN and INTERNIST relied on a knowledge base containing diagnostic rules:
A rule is a direct link between an observed set of symptoms and the fault (or disease in the medical domain) that these symptoms indicate. Many extensions to this simple pattern were needed in practice, such as certainty factors, screening rules, etc. Each rule expresses a fragment of a domain expert's knowledge in a direct, compiled form. The advantages are easy comprehension and very efficient application (by a rule interpreter) of knowledge in this form. Indeed, many successful experts systems were, and still are, developed along those lines. However, it soon became clear that the compiled diagnostic rule format for expressing diagnostic knowledge had distinct drawbacks:
In short, diagnostic systems developed according to the compiled rule paradigm tend to be lacking in coverage, clarity, flexibility, and cost-effective extendability. The model-based reasoning paradigm was developed, first in the diagnostic domain, in order to answer these deficiencies. In an MBR-based diagnostic system, the set of diagnostic rules is replaced by a model of the target system (patient, device), defining its structure and behavior (Davis, 1984):
Later, we will categorize and see specific examples of structural/behavioral models. Using a model of this kind, an MBR diagnostic engine is able to:
This approach has a number of advantages over the compiled rule approach, including:
The MBR approach is not without problems, including:
The distinction between expert systems built using the compiled rule paradigm and systems using models were recognized during the 1980s. The two types of systems were given different labels, such as "shallow" (compiled rules) and "deep" (model-based) systems. Model-based systems were also called "second-generation expert systems." Many researchers and practitioners saw the advantages of both approaches, and proposed hybrid solutions. In the diagnostic domain, these hybrids generally work along the following principles:
The MBR paradigm is not restricted to the diagnostic domain, even if most results have been reported for diagnostic applications. Other types of problems that have been attacked with this approach include design/configuration problems, and monitoring and control problems (the latter two often in combination with diagnostic systems). A recent survey of methods and applications of MBR can be found in Dague (1995). In a later section, some examples of application in these various domains will be described briefly.
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