Expert system rule base rules

Figure 2. Basic Step in an Expert System Rule Base.

Formative evaluation of the system with a limited number of users has indicated that users are willing to use the help system if their knowledge is deficient. However, the advice that is available for novice users of the system is not always adequate. For instance, help screens including examples and limited practice are insufficient to enable novice users to accurately classify learning outcomes. This decision, like most of the others required of the users, is a complex, coordinate concept outcome that requires more instruction. We are exploring options, including the addition of more interactive instructional activities to the help system or the integration of other expert system rule bases. These rule bases will be executed by the help... 

Rules seemingly have the same format as IF.. THEN.. statements in any other conventional computer language. The major difference is that the latter statements are constructed to be executed sequentially and always in the same order, whereas expert system rules are meant as little independent pieces of knowledge. It is the task of the inference engine to recognize the applicable rules. This may be different in different situations. There is no preset order in which the rules must be executed. Clarity of the rule base is an essential characteristic because it must be possible to control and follow the system on reasoning errors. The structuring of rules into rule sets favours comprehensibility and allows a more efficient consultation of the system. Because of the natural resemblance to real expertise, rule-based expert systems are the most popular. Many of the earlier developed systems are pure rule-based systems. 

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. 

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... 

DEREK (also see Chapter 18) is an expert or rule-based system with a list of chemical substructures correlated with mutagenic activity [24], The model was built using published mutagenic mechanism-of-action data and a list of known mutagenic structural alerts derived from Ashby s list of DNA reactive electrophilic chemical fragments [2,24,25]. Because this is a rule-based system, the... 

A panel discussion was held to review the problems involved in sharing Knowledge bases. This discussion examines the advantages of sharing expert system rules, and the likelihood of knowledge bases being shared. The viewpoints are given by industrial representatives, a university professor, and chemical instrument vendors. 

DEREK DEREK (Deductive Estimation of Risk from Existing Knowledge). An expert system is based on rules. It identifies the so-called toxicophores fragments of the molecule associated with the corresponding activity) and provides the related commentary for them and references to the available information http //www.lhasalimited.org/... 

Expert systems are based on spectral feature-substructure relationship rules that comprise the knowledge base for IR spectral analysis. This is the main difference from neural network techniques, where no prior knowledge about the structure-spectrum relationship is necessary because the network learns inductively from examples. For expert systems a knowledge base has to be established and transformed into a computer operable form. This expert knowledge is expressed in terms of substructure-subspectra relationships. 

Expert systems. In situations where the statistical classifiers cannot be used, because of the complexity or inhomogeneity of the data, rule-based expert systems can sometimes be a solution. The complex images can be more readily described by rules than represented as simple feature vectors. Rules can be devised which cope with inhomogeneous data by, for example, triggering some specialised data-processing algorithms. 

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. 

The need for rapidly accessible estimation of toxicity has led to the development of software and other algorithms that will generate estimations of toxicity, usually for organic compounds [79] such methodology is termed an expert system, which has been defined [34] as any formalised system, not necessarily computer-based, which enables a user to obtain rational predictions about the toxicity of chemicals. Essentially, expert systems fall into two classes— those relying on statistical approaches and those based on explicit rules derived from human knowledge. 

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