Expert systems types

Table 5.9-1 in that report indicates the "V V Lifecycle Phases" for a number of expert system types. Again, the focus is on Lifecycle V V. 

Copies of the TNO peroxide test databases have been provided to E27.07 and the new versions of CHETAH are expected to contain an extensive database as well as pattern-recognition techniques for estimating the hazard of new materials. The CHETAH software will continue to rely on bond energy data and group contribution calculations to estimate energy release potential. Hopefully, the new versions will also incorporate natural language expert system-type front ends so that the CHETAH program(s) will see expanded use in both analytical and tutorial modes. Copies of the LEILA (8) dissertation have also been provided to E27.07 as an example of an expert system approach to selection and use of appropriate theories and computational methods for the solution of problems in chemical kinetics. 

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. 

Expert systems have been applied in various fields such as medicine, chemistiy, engineering, or business. Within all these fields, expert systems are used for similar types of tasks. They can, for example, be used for classification, i.c, to select one... 

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). 

Utilization of intelligent systems in chiral chromatography starts with an original project called CHIRULE developed by Stauffer and Dessy [36], who combined similarity searching and an expert system application for CSP prediction. This issue has recently been reconsidered by Bryant and co-workers with the first development of an expert system for the choice of Pirkle-type CSPs [37]. 

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. 

The current implementation of the UltraLink uses a centralized set of terminologies, concepts, and rules, which may not correspond to the needs of every user. To further increase the flexibility of the expert system, we will implement a personalized version of the UltraLink. This should allow users to personalize the terminology, concepts, relationships, and rules used to identify typed entities and thereby create the UltraLinks best suited for their daily work. This will also enable us to design precustomized UltraLinks specifically tuned for chemists, biologists, and physicians. 

Foulkes et al. (1988) have approached the synthesis of operating procedures from a more empirical angle. They have extended the work of Rivas and Rudd (1974) for the synthesis of complex pump and valve sequencing operations, relying on the use of logical propositions (implemented as rule-based expert systems), which capture the various types of constraints imposed on the states of a processing system. 

The final aim is to construct a formalized representation of the decision process. Decision trees and structured system analysis are possibilities. Some types of expert systems can derive their own rules from examples. These are described in Chapters 18 and 33. 

An early field of application in analytical chemistry is structure elucidation. DENDRAL was one of the first ES in general, designed to the identification of organic compounds from mass spectrometric data (Buchanan and Feigenbaum [1978]). In the 1980s and 1990s a flood of expert systems has been developed in analytical chemistry for different types of application, viz ... 

The information to which the rule is applied might be extracted from the knowledge base, it might be provided by the user in response to questions from the ES, or it may be provided by combining the two. An expert system that uses rule-based reasoning is, quite reasonably, known as a rule-based system. This is the most widely used form of expert system in science, and it is on this type of system that this chapter concentrates. 

Because the expert system was not connected to a real reactor, we built a small table-driven simulation to model the growth of cells in suspension. The graphical interface includes images representing the reactor itself, several feed bins and associated valves. Also shown in Figure 1 are several types of gauges, including a strip chart, monitors of various states and alarm conditions, temperature, and the on/off state of heaters and coolers. 

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. 

The prototype of QualAId currently in existence is one small part of the total framework needed for a useful expert system. The objective of QualAId is to provide advice on how much and what type of QA/QC is needed for various types of environmental analyses. The rules for determining these needs have been derived from the American Chemical Society (ACS) publication, "Principles of Environmental Analysis, (2) and from various protocols and recommendations of the U.S. Environmental Protection Agency (EPA). 

This particular demonstration module only incorporates decisions involving analysis of volatile and semivolatile organic compounds from water. These compounds are, by definition, volatile enough to be separated by gas chromatography (GC). The complete expert system will incorporate decisions based upon any type of chemical in any type of matrix and will also be capable of providing advice specifically for selected EPA methods commonly in use, i.e., EPA Methods 624, 625, 1624, 1625, the various non-mass spectrometric 600 Methods, etc. (Figure 1). 

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