Information expert systems

Computer-aided trend identification offers potential benefits, but is dependent on the quality of the input information. Expert systems and artificial intelligence are tools being tested. When successful, they may give improved insight into identifying common causes and trend analyses. 

At the start of the development, it had been intended use an expert system shell to implement this tool, however, after careful consideration, it was concluded that this was not the optimum strategy. An examination procedure can be considered as consisting of two parts fixed documentary information and variable parameters. For the fixed documentary information, a hypertext-like browser can be incorporated to provide point-and-click navigation through the standard. For the variable parameters, such as probe scanning paths, the decisions involved are too complex to be easily specified in a set of rules. Therefore a software module was developed to perfonn calculations on 3D geometric models, created fi om templates scaled by the user. 

The quahty of an analytical result also depends on the vaUdity of the sample utilized and the method chosen for data analysis. There are articles describiag Sampling and automated sample preparation (see Automated instrumentation) as well as articles emphasizing data treatment (see Chemometrics Computer technology), data iaterpretation (see Databases Imaging technology), and the communication of data within the laboratory or process system (see Expert systems Laboratory information managet nt systems). 

Evidence of the appHcation of computers and expert systems to instmmental data interpretation is found in the new discipline of chemometrics (qv) where the relationship between data and information sought is explored as a problem of mathematics and statistics (7—10). One of the most useful insights provided by chemometrics is the realization that a cluster of measurements of quantities only remotely related to the actual information sought can be used in combination to determine the information desired by inference. Thus, for example, a combination of viscosity, boiling point, and specific gravity data can be used to a characterize the chemical composition of a mixture of solvents (11). The complexity of such a procedure is accommodated by performing a multivariate data analysis. 

The earliest practical use of an expert system was made in the software named MYCIN for diagnosing a toxic poison from the symptoms of a patient and recommending the antidote (62). This type of activity is generally carried out by a human expert who processes information about a situation (in this case, symptoms of a patient), refers to the expert s experience and expert knowledge, and then recommends action (in this case, the antidote). 

J. P. Hickey, A. Aldridge, D. R. May Passino, A. M. Frank, Expert System Predicts Aquatic Toxicityfrom Contaminant Chemical Structure,NMoa-A Fisheries Research Center-Great Lakes, U.S. Fish and Wildlife Service, Ann Arbor, Mich., 1991 Ibid., Drug Information Journal 26, 487 (1992). 

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. 

In the first stages of the development of an Action plan all control options are considered. In the case of lakes, this process is aided by a PC-based expert system , PACGAP, which looks at the physical and chemical characteristics of the lake to determine the most likely option for control. Once further, more detailed information has been collected on the lake s nutrient inputs and other controlling factors, amore complex interactive model can be used (Phytoplankton Response To Environmental CHange, PROTECH-2) to define the efficacy of proposed control options more accurately. This model is able to predict the development of phytoplankton species populations under different nutrient and stratification regimes. 

There are some aspects of process design in which decisions are based primarily on past experience rather than on quantitative performance models. Problems of this type include the selection of constraction materials, the selection of appropriate models for evaluating the physical properties of homogeneous and heterogeneous mixtures of components, and the selection of safety systems. Advances in expert systems technology and information management will have a profound impact on expressing the solutions to these problems. 

For convenience of searching for information on similar compounds, chemicals are grouped under 80 chemical classes. These same classes are used with the expert system. Synonyms for many of the compounds are also included where it is likely that the synonym might simplify searching. These same synonyms are also used in the expert system. 

Equally impressive are the results in engineering and science. Characterized as knowledgeable, informed, expert, intelligent, or any other denotation, software systems have expanded tremendously the scope of automation in scientific and engineering activities. (4-13)... 

In Section 18.4, we explained that inductive expert systems can be applied for classification purposes and we refer to that section for further information and example references. It should be pointed out that the method is essentially univariate. Indeed, one selects a splitting point on one of the variables, such that it achieves the best discrimination, the best being determined by, e.g., an entropy function. Several references are given in Chapter 18. A comparison with other methods can be found, for instance, in an article by Mulholland et al. [22]. 

As an example, consider the automation efforts for chemical laboratories in the last decades. Chemical laboratories of today are equipped with instruments that, in principle, can run automatically for 24 hours a day. This results in a higher productivity, since more samples can be analysed with an equal technical effort. Decisions about the analysis itself, how many and which samples must be analysed with what method or technique, etc., are still the responsibility of the laboratory personnel. Since experience can be incorporated into expert systems, they can provide significant benefits as decision-supporting tools. Therefore, the main ideas of expert systems and their development are explained in this chapter. More detailed information can be found in the numerous textbooks on expert systems [7-10]. 

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