Expert systems building

Until recently, most expert system building took place in the research departments of universities and a few major corporations. The primary emphasis was investigation of artificial intelligence principles, and the application was of secondary importance. The expert systems tools used reflect this interest. They are typically stand-alone AI computer systems, using special hardware and software environments (usually Lispr-based) not commonly fo md in scientific and engineering organizations. 

Explanation- driven expert system building leads to robust systems. By testing to ensure that the right conclusions are reached for the right reasons, the probability of the reasoning being correct for unforeseen situations is enhanced. Quality explanation also makes systems more useful as teaching tools. 

Function of TOGA. An HSB employee, Richard I. Lowe, is one of the handful of transformer diagnosis experts in the U.S. His rules have been incorporated in an expert system called TOGA, which was built with the RuleMaster expert system building package. 

The LES system described by Nguyen et al. (1987) is a generic rule-based expert system building tool which has an extensive... 

The recognition ratios achieved by CBR systems developed as part of this project could not be bettered by either neural-network classifiers or rule-based expert system classifiers. In addition, CBR systems should be mote reliable than simple classifiers as they are programmed to recognise unknown data. The knowledge acquisition necessary to build CBR systems is less expensive than for expert systems, because it is simpler to describe the knowledge how to distinguish between certain types of data than to describe the whole data contents. 

F. Hayes-Roth, D.A. Waterman and D.B. Lenat (eds.). Building Expert Systems. Adison-Wesley, London, 1983. 

Harrington, P. B. Voorhees, K. J. MuRES A multivariate rule-building expert system. Anal. Chem. 1990, 62, 729-734. 

Harrington, P. B. Fuzzy Rule-building Expert Systems Minimal Neural Networks. J. Chemometrics 1991, 5, 467 186. 

Characteristics and Values of Expert Systems. What leads me to make such bold and risky statements The answer can be summarized as follows. First, knowledge is power. You can t solve problems using any technology unless you have some detailed knowledge about the problem and how to solve it. This fact seems so obvious that it is unnecessary to state it. Many systems will fail, however, because the builders will attempt to build such systems to solve ill-defined problems. 

Considering commercial applications of the technology, expert systems can create value through giving a company a competitive edge. This consideration means that the first companies to exploit this technology to build useful products will obviously be some steps ahead of those that do not. 

The only phase of development of expert systems that I will say any more about is the first, and in many ways the most crucial, step for those who are contemplating building expert systems for the first time. How do you go about selecting an appropriate application Here are the basic criteria ... 

Fourth, the scope of the application must be bounded. There must be some specification of the functionality of the expert system and characteristics of the problems it is expected to solve. Trying to build an expert system to solve the world s economic problems is not a good application to choose. However, selecting a product mix from an oil refinery based on the current state of supply and demand in the world s energy markets might be a good application. 

A Knowledge-Engineering Facility for Building Scientific Expert Systems... 

RuleMaster is a general-purpose software package for building and delivering expert systems. Its features include l) knowledge acquisition by inductive learning,... 

RuleMaster expert systems are represented as Radial programs. To build an expert system, domain knowledge is normally entered in two parts a module structure and the bodies of the modules. The structure defines the hierarchical organization of decisions used to solve the problem. The code within each module defines the details of one of these decisions. 

TOGA uses the built-in numerical capabilities of Radial to compute functions of concentration values, which are used extensively in the rules. The ratio of hydrogen to acetylene concentration in the corona rule is a simple example of this. User-defined con xDund data types are used to handle blocks of data as a single named structure. These features are invaluable in building practical expert systems, but are not available with all packages. 

A side effect of this approach is the consistency and completeness checking which is performed at building time when the rule pointers are being set up. Errors and oversights are caught at this stage and corrected before the iterative development cycle is continued. Most expert system approaches do not support error... 

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