Expert domain

Training and education to learn about a particular expert domain. 

Consensual vision between different stakeholders it is often difficult to make people agree on common words with common definitions. Definitions are slightly different from one expert to another, but it is often enough to stop convergence. The quest for a real objectivity in a particular expert domain is... 

From these definitions we can see that an indispensable component to both early warning and proactive control systems is the set of causal relations between deviations in food supply networks and determinant factors. Ultimately, expert domain knowledge is required to verify those causal relations. However, current advances of ICT systems provide the opportunities to largely automatically disentangle causal relations from huge amounts of data. 

Definition / An expert system is a computer program that manipulates large amounts of symboHc knowledge using quaUtative techniques, to solve problems that can otherwise be solved only by expert human problem solvers. Expert systems capture the human problem solver s expertise in the form of domain-specific knowledge and domain-independent problem-solving strategies. 

Problem Selection. To select the problem correcdy, the criteria discussed earHer should be carefully appHed before launching a project the existence of a knowledge bottleneck, the inappHcabiHty of exact numerical methods, the existence of either an expert or a theory for the task, the narrowness of the domain, and the business issues of payout and cost. If needed, the various criteria can be quantified and weighted based on their... 

An expert system is a computer program that uses an experts knowledge in a particular domain to solve a narrowly focused, complex problem. An off-line system uses information entered manually and produces results in visual form to guide the user in solving the problem at hand. An on-line system uses information taken direc tly from process measurements to perform tasks automatically or instruct or alert operating personnel to the status of the plant. 

Today, the use of CHIRBASE as a tool in aiding the chemist in the identification of appropriate CSPs has produced impressive and valuable results. Although recent developments diminish the need for domain expertise, today the user must possess a certain level of knowledge of analytical chemistry and chiral chromatography. Nevertheless, further refinements will notably reduce this required level of expertise. Part of this effort will include the design of an expert system which will provide rule sets for each CSP in a given sample search context. The expert system will also be able to query the user about the specific requisites for each sample (scale, solubility, etc.) and generate rules which will indicate a ranked list of CSPs as well their most suitable experimental conditions (mobile phase, temperature, pH, etc.). 

There are a growing number of tools to assess applicability domain, and a number of expert systems, for example, TOPKAT and MultiCASE, have their own measures of fit. These need to be developed and their application to larger drug libraries demonstrated. 

An expert system is a knowledge-based system that emulates expert thought to solve significant problems in a particular domain of expertise. [1]... 

The next few steps are very similar to those required in any software project. One of the first stages is the clear definition of the knowledge domain. It must be clear which problems the expert system must solve. It is at this stage not the intention to define how this can be done. Clarity and specificity must be the major guides here. Fuzziness at this stage will, more than in classical software projects, have to be paid for later when different interpretations cause misunderstandings. Equally important is the clear definition of the end user(s). An expert system set up as decision support tool for professionals is totally different from an expert system that can be used as a training support for less professional people. 

After the knowledge domain, the end users and the experts have been defined, a choice must be made for the software environment for the development of the expert system. In addition, the hardware for the development process as well as for the delivery system must be identified. Real application expert systems always require at least a mid-sized tool (see Section 43.7). 

In Chapter 43 the incorporation of expertise and experience in data analysis by means of expert systems is described. The knowledge acquisition bottleneck and the brittleness of domain expertise are, however, the major drawbacks in the development of expert systems. This has stimulated research on alternative techniques. Artificial neural networks (ANN) were first developed as a model of the human brain structure. The computerized version turned out to be suitable for performing tasks that are considered to be difficult to solve by classical techniques. 

Drawing the bottom line, this book is very useful for scientists in various disciplines and experts in domain of interface physical chemistry interested in development and application of the method of semiconductor sensors as well as for post-graduate and graduate students specialized in above domain of science. 

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