Computer software expert systems

DART software A diagnostic software expert system, developed by IBM, that is used to diagnose equipment failure prctblems. It does not hold information about why equipment fails but instead contrasts the expected behavior with the actual behavior of the equipment to diagnose the problem. See computer software fabricating process. 

The preparation of computer databases, expert systems, and software facilitating the designing process, and service of protection installations are great achievements. The knowledge databases, for example, the one published by NACE and systematically supplemented International Abstracts of the World s Corrosion Control Literature COR-AB, contain over 3000 abstracts on cathodic and anodic protection gathered from... 

Human-computer interface, expert systems, software engineering, high-performance computing, databases, visualization... 

The computer has become an accepted part of our daily lives. Computer applications in applied polymer science now are focussing on modelling, simulation, robotics, and expert systems rather than on the traditional subject of laboratory instrument automation and data reduction. The availability of inexpensive computing power and of package software for many applications has allowed the scientist to develop sophisticated applications in many areas without the need for extensive program development. 

The need for rapidly accessible estimation of toxicity has led to the development of software and other algorithms that will generate estimations of toxicity, usually for organic compounds [79] such methodology is termed an expert system, which has been defined [34] as any formalised system, not necessarily computer-based, which enables a user to obtain rational predictions about the toxicity of chemicals. Essentially, expert systems fall into two classes— those relying on statistical approaches and those based on explicit rules derived from human knowledge. 

P.G. Raeth, Expert systems a software methodology for modern applications. IEEE Computer Society Press Reprint Collection, IEEE Computer Society Press, Los Alamitos, CA, USA, 1990. 

Advances in computer science continue to serve as the basis for new extensions to software products. In particular, artificial intelligence techniques have begun to mature to the point at which they can play a role in scientific software. In the future, scientific software will incorporate expert systems technology in order to provide a new level of assistance to scientists in applying statistical and graphical techniques to data analysis. 

Expert systems appear in some ways to be the "classic" application of Artificial Intelligence. An expert system is a kind of personal advisor that engages the user in a conversation with the aim of providing help on a specialist topic. In sophisticated systems, the interaction between the expert system (ES) software and the user may be so natural that the user almost forgets that it is a computer rather than a human that is holding up the other end of the conversation. As developers of an ES usually do their best to create software that can participate in "intelligent" conversations, in order to enhance the user s confidence in the system, expert systems can seem the most human and friendly side of AI. 

SHRDLU was an example of a system that operated in a well-defined task domain and this software was an important steppingstone in the development of AI programs, enabling computer scientists to better understand how to construct expert systems and how to handle the user-computer interaction. However, the market for software that can rearrange children s blocks is limited and the development of the first ES in chemistry was a far more significant milestone in science. 

If the available computational models are insufficiently detailed so that behavior is too uncertain to predict, or if the only model that can be constructed is so detailed that its execution is unacceptably slow, we cannot expect to be able to use an expert system to control the process under all circumstances. Fortunately, there is an alternative — a software model that can learn how to control a system rather than needing to be told how to do so. 

Other more mathematical techniques, which rely on appropriate computer software and are examples of chemometrics (p. 33), include the generation of one-, two- or three-dimensional window diagrams, computer-directed searches and the use of expert systems (p. 529). A discussion of these is beyond the scope of this text. 

Analytical chemistry in the new millennium will continue to develop greater degrees of sophistication. The use of automation, especially involving robots, for routine work will increase and the role of ever more powerful computers and software, such as intelligent expert systems, will be a dominant factor. Extreme miniaturisation of techniques (the analytical laboratory on a chip ) and sensors designed for specific tasks will make a big impact. Despite such advances, the importance of, and the need for, trained analytical chemists is set to continue into the foreseeable future and it is vital that universities and colleges play a full part in the provision of relevant courses of study. 

Expert systems represent a branch of artificial intelligence that has received enormous publicity in the last two to three years. Many companies have been formed to produce computer software for what is predicted to be a substantial market. This paper describes what is meant by the term expert system and the kinds of problems that currently appear amenable to solution by such systems. The physical sciences and engineering disciplines are areas for application that are receiving considerable attention. The reasons for this and several examples of recent applications are discussed. The synergism of scientists and engineers with machines supporting expert systems has important implications for the conduct of chemical research in the future some of these implications are described. 

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. 

Virtually all tasks which require the routine application of human expertise, in an organized way, are candidates for expert systems. The computer implementation of expertise has such advantages as speed, around-the-clock availability, and ease of expansion of the knowledge base. As such, expert systems represent the next generation of higher level software, performing tasks presently done by human operators. 

In the last 20 years advances in computer technology and computational chemistry have resulted in the availability of a variety of different commercially available software packages or methodologies that can assist in the prediction or rationalization of various aspects of chemical metabolism in mammals. Such tools broadly divide into those that may be used to predict whether a particular biotransformation is likely to be catalyzed by a particular class or type of enzyme and those (usually expert systems) that give predictions of the full range of likely, initial, and subsequent biotransformations for a chemical in a particular biological system. 

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