Technologies, supporting expert systems

Knowledge engineering is the technology behind construction of expert systems, or knowledge systems, or expert support systems. Such systems are designed to advise, inform and solve problems. They can perform at the level of experts, and in some cases exceed expert performance. They do so not because they are "smarter but because they represent the collective expertise of the builders of the systems. They are more systematic and thorough. And they can be replicated and used throughout a laboratory, company or industry at low cost. 

Expert system technology was one of the first branches of artificial intelligence [1,2], It is by now well tried and mature. Nevertheless, expert systems are tools to support human thinking, not magic problem-solvers. While the name expert system is based on the notion that the systems behave like experts, it is generally considered that they should be used by experts or at least the well-informed. 

Chapter 4 covers technologies for representing and processing chemical information in a computer as well as a summary of supporting technologies that typically appear in expert systems. 

With these initial thoughts, we are prepared to go now into some details of the theory of expert systems. For an expert systans to handle real-world problems, we need to incorporate a series of supporting technologies, in particular to deal with the following ... 

A typical application area of expert systems and their supporting technologies is spectroscopy. Since spectra require interpretation, they are ideally suited for automated analysis with or without the aid of a spectroscopist. Particularly vibrational spectra, like infrared spectra, are subject to interpretation with rules and experience. A series of monographs and correlation tables exist for the interpretation of vibrational spectra [7-10]. The relationship between frequency characteristics and structural features is rather complicated, and the number of known correlations between... 

HITERM integrates high-performance computing on parallel machines and workstation clusters with a decision-support approach based on a hybrid expert system. Typical applications are in the domain of technological risk assessment and... 

Expert systems have now been in nse for more than 40 years and have spread into nearly all imaginable areas of application. Evolving from the research area of artih-cial intelligence (Al), they can be fonnd today in commercial applications throughout the entire scientihc and technical areas. We have seen that this technology is not just stand-alone software but is supported by many technologies from mathematics, cheminformatics, bioinformatics, Al, and related areas. 

It is important to define and contrast expert systems with classical decision support systems, executive information systems, and conventional software systems. First business needs are identified, then appropriate technologies are applied, similar to conventional systems. An analysis of the cost effectiveness of a proposed expert system is important. 

Corker, K. M., and Pisanich, G. M., When Reasonable Expert Systems Disagree, Presented at the Topical Meeting of the American Nuclear Society, Computer-Based Human Support Systems Technology, Methods, and Future, Philadelphia, 1995. 

The first application of expert system technology in chemistry was the development of DENDRAL by Nobel award winner J. Lederberg and co-workers at Stanford University. The program supported chonists in analyzing the molecular structure of unknown chemical substances. The decision process took into account data from mass spectrometry and other experimental information. One simple rule taken from DENDRAL will explain the type of knowledge base. 

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