Expert systems values

As computing capabiUty has improved, the need for automated methods of determining connectivity indexes, as well as group compositions and other stmctural parameters, for existing databases of chemical species has increased in importance. New naming techniques, such as SMILES, have been proposed which can be easily translated to these indexes and parameters by computer algorithms. Discussions of the more recent work in this area are available (281,282). SMILES has been used to input Contaminant stmctures into an expert system for aquatic toxicity prediction by generating LSER parameter values (243,258). 

Uses raw data from field tests to compute hydraulic conductivity computed value is evaluated by the expert system for its correctness with regard to these considerations site-specific geological characteristics, validity of test procedures, accuracy of the raw data, and the computational method. System is written in Arity-Prolog on a PC. 

The most popular representation scheme in expert systems is the mle-based scheme. In a rule-based system the knowledge consists of a number of variables, also called attributes, to which a number of possible values are assigned. The rules are the functions that relate the different attributes with each other. A mle base consists of a number of If... Then... mles. The IF part contains the conditions that must be satisfied for the actions or conclusions in the THEN part to be valid. As an example, suppose we want to express in a mle that a compound is unstable in an alkaline solution if it contains an ester function. In a semi-formal way the mle can be written ... 

The syntax for implementing this in an actual expert system is, of course, quite diverse and it is beyond the scope of this book to describe this in more detail. As an example the above-mentioned mle is translated in a realistic syntax. This starts by the definition of the attributes and their possible values. In this examples two attributes must be defined ... 

Although most expert systems are designed to advise fairly unsophisticated users, some act instead as assistants to established human experts DENDRAL, which we introduce shortly, falls in this category. Rather than replacing the expert, these systems enhance their productivity. Assistants of this sort have particular value in fields, such as law or medicine, in which the amount of potentially relevant background material may be so large that not even a human expert may fully be cognizant with it. 

Scientists are pragmatists, so an ES that provides advice on chemical or biological problems will be of much greater value than one that can balance the merits of Florida against those of the Costa del Sol. Expert systems address real-world problems in a well-defined, generally narrow task domain this defines the field in which the system has knowledge of value. Typical task domains include ... 

An expert system does much more than extract information from a database, format it, and offer it up to the user it analyzes and processes the information to make deductions and generate recommendations. Because an ES may be required to present alternative strategies and give an estimate of the potential value of different courses of action, it must contain a reasoning capacity, which relies on some sort of general problem-solving method. 

Thus, propanol, C3H70H, has a membership of 1 in the three-carbon molecule class, while ethanol, C2H5OH, has a membership of 0 in the same class. As the membership in a crisp set must take one of only two possible values, Boolean (two-valued) logic can be used to manipulate crisp sets. If all the knowledge that we have can be described by placing objects in sets that are separated by crisp divisions, the sort of rule-based approach to the development of an expert system described in the previous chapter is appropriate. 

In a conventional expert system, the only rules to fire are those for which the condition is met. In a fuzzy system, all of the rules fire because all are expressed in terms of membership, not the Boolean values of true and false. Some rules may involve membership values only of zero, so have no effect, but they must still be inspected. Implicitly, we assume an or between every pair of rules, so the whole rule base is... 

Characteristics and Values of Expert Systems. What leads me to make such bold and risky statements The answer can be summarized as follows. First, knowledge is power. You can t solve problems using any technology unless you have some detailed knowledge about the problem and how to solve it. This fact seems so obvious that it is unnecessary to state it. Many systems will fail, however, because the builders will attempt to build such systems to solve ill-defined problems. 

The facts in a knowledge base include descriptions of objects, their attributes and corresponding data values, in the area to which the expert system is to be applied. In a process control application, for example, the factual knowledge might include a description of a physical plant or a portion thereof, characteristics of individual components, values from sensor data, composition of feedstocks and so forth. 

Expert systems create value for groups of people, ranging from laboratory units to entire companies, in several ways, by ... 

Considering commercial applications of the technology, expert systems can create value through giving a company a competitive edge. This consideration means that the first companies to exploit this technology to build useful products will obviously be some steps ahead of those that do not. 

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 value of this approach is that a running expert system is rapidly created, without forcing the expert to articulate a general problem-solving procedure. The prototype system is available for the iterative knowledge refinement process, which draws out more details of the decision-making procedure from the expert to gradually build a complete and tested exi>ert system. 

In conventional expert systems, the facts and knowledge upon which the inference is based are static. In the industrial application, the facts or process measurements are dynamic. In an industrial application there may be several thousand measurements and alarms which may significantly change in value or status in a few minutes. 

Rules in the expert system are structured to allow flexibility and future expansion. For speed of execution, the IF-THEN clauses are actually executable LISP code. Tables II and III contain examples of how rules are structured. The IF clauses contain functions, called predicates. Predicates have a value of either... 

An important aspect of our AI application is the attention paid to including well-established Fortran programs and database search methods into the decision structure of an expert system network. Only certain AI software tools (such as TIMM) effectively handle this critical aspect for the analytical instrumentation field at this time (57-60)> The ability to combine symbolic and numeric processing appears to be a major factor in development of multilevel expert systems for practical instrumentation use. Therefore, the expert systems in the EXMAT linked network access factor values and the decisions from EXMATH, an expert system with chemometric/Fortran routines which are appropriate to the nature of the instrumental data and the information needed by the analyst. Pattern recognition and correlation methods are basic capabilities in this field. 

CONVERTS MATH RESULTS TO FACTOR VALUES FOR INPUT TO TIMM EXPERT SYSTEMS... 

A number of software packages or expert systems for ruggedness testing has been developed. RES (commercialized under the name Shaiker ) is an expert system created by Van Leeuwen et al. [4,23] and has been validated and evaluated [42,43]. It uses fractional factorial and Plackett-Burman designs and allows to test the factors at two or three levels. The interpretation criteria used here are the predefined values (see Section 3.4.8). 

Figure 4.21 shows the sensor output for the smart automated sensor expert system-controlled run. The resin reached the center sensor at 37 min. The viscosity is maintained at a low value by permitting slow increases in the temperature. At 60 min, fabric impregnation was complete. The resin was advanced during a 121 °C hold to a predetermined value of degree of cure of 0.35, based on the Loos model s predictions of the extent of the exothermic effect. This value of a is clearly dependent on panel thickness. Then at 130 min, the ramp to 177°C was begun. Achievement of an acceptable complete degree of cure was determined by the sensor at 190 min. Then the cure process was shut down. 

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