Expert systems description

In these frames all specific columns that are relevant for the reasoning process of the expert system can be described in a structured and comprehensive way. The frame-based and rule-based knowledge representation are both required to represent expertise in a natural way. Therefore, in most expert systems a combination of rule-based and frame-based knowledge representation is used. The rule base together with the factual and descriptive knowledge by means, of e.g., frames constitute the knowledge base of the expert system. 

Now that the definition of a volatile liquid has been settled, the expert system could apply the rule. However, this approach is clearly unsatisfactory. The all-or-nothing crisp set that defines "volatile" does not allow for degrees of volatility. This conflicts with our common sense notion of volatility as a description, which changes smoothly from low-boiling liquids, like diethyl ether (boiling point = 34.6°C), which are widely accepted to be volatile, to materials like graphite or steel that are nonvolatile. If a human expert used the rule ... 

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 System Programming. Many of the concepts and terms which will be used in the description of this work are unique to the fields of AI and computer science. The reader should refer to the article by Dessy ( y or to the introductory article of this symposium for a more detailed description of these concepts. 

The scope of evolutionary simulation is presently largely limited by the size and time problems. Its force fields are mostly expert systems and are far from a faithful reproduction of real physical effects - think of the wrong description of short-range Coulombic forces resulting from locating formal charges at atomic nuclei,... 

Lately, there has been a great deal of interest in the use of artificial neural networks in many fields, including that of prediction and expert systems, and they are of interest here for the description of response surfaces that have a non-linear relation to the factor vari-ables. In such cases, the response surface may well fit the data better than that calculated from the model estimated by least-squares regression. " ... 

Description of the type of model. This section specifies the type of model (e.g., SAR, regression-based QSAR, expert system, battery of (Q)SARs) and defines the endpoint and the dependent variable being modeled, reporting also information (if available) on the quality of the data used... 

Application of statistics in expert systems is a topic that fills more than a single book. However, some of the investigations presented in the next chapters are based on methods of descriptive statistics. The terms and basic concepts of importance for the interpretation of these methods should be introduced first. Algorithms and detailed descriptions can be found in several textbooks [43-47]. 

Even though several software solutions exist for exception or complaints management — most of them as a module of a LIMS — none of them takes advantage of technologies used in expert systems. Most of the existing systems rely heavily on textual information that has to be interpreted by a domain expert. Interpretation could be done by an expert system module, if the description is entered in a formal fashion that allows automatic parsing and interpretation. 

On the other hand, expert systems are not necessarily developed in descriptive programming languages as long as they are able to present the knowledge in a descriptive manner. Expert system shells and descriptive languages are merely tools that allow — in some, but not all cases — development to be done more efficiently. 

As an example consider the 1990 U.S. Census classification task. Free text samples from 22 million citizens were classified by assigning industry and occupation codes, based on job descriptions given in questionnaires [14]. An expert system called AIOCS was used. The rules were laboriously derived from a training data base consisting of 132 247 cases classified by human experts. The time required for the development of this system was equivalent to 192 month/person (16 years/person). Still, accuracy is rather low (Table 1). An alternative memory based system called PACE was developed in just 4 months/person and efficiently implemented on CM-2 parallel computer. 

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