Construction, expert system

SHRDLU was an example of a system that operated in a well-defined task domain and this software was an important steppingstone in the development of AI programs, enabling computer scientists to better understand how to construct expert systems and how to handle the user-computer interaction. However, the market for software that can rearrange children s blocks is limited and the development of the first ES in chemistry was a far more significant milestone in science. 

Knowledge engineering is the means of constructing expert systems as a kind of intellectual technology. Here the gap between the problems of intellectual technologies and the proper content of artificial intelligence as a scientific and technical discipline has been removed ... 

To construct expert systems, researchers in artificial intelligence must develop methods for mining (extracting) experience. I first encountered the... 

Construction of expert systems is facilitated if it is possible (at least approximately) to describe (model) expected signal from defect and non-defect pieces. If no models for the problem are available then the knowledge about the problem has to be acquired from an expert (the NDT inspector). However, the knowledge possessed by the expert is often incomplete and not well formalised, which makes knowledge acquisition a difficult task for the knowledge engineer. 

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 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. 

Readily reproduced unlimited number of copies of an expert system can be made once the first one has been constructed... 

Consider the following tasks. Which, if any, of them would be suitable candidates for the construction of an expert system ... 

Fuzzy logic is often referred to as a way of "reasoning with uncertainty." It provides a well-defined mechanism to deal with uncertain and incompletely defined data, so that one can make precise deductions from imprecise data. The incorporation of fuzzy ideas into expert systems allows the development of software that can reason in roughly the same way that people think when confronted with information that is ragged around the edges. Fuzzy logic is also convenient in that it can operate on not just imprecise data, but inaccurate data, or data about which we have doubts. It does not require that some underlying mathematical model be constructed before we start to assess the data. 

If the available computational models are insufficiently detailed so that behavior is too uncertain to predict, or if the only model that can be constructed is so detailed that its execution is unacceptably slow, we cannot expect to be able to use an expert system to control the process under all circumstances. Fortunately, there is an alternative — a software model that can learn how to control a system rather than needing to be told how to do so. 

The decision-making engine in the CS is the set of classifier condition-action rules therefore, the key to a successful application is a well-constructed set of rules. If the control problem is straightforward, the necessary classifiers could, in principle, be created by hand, but there is rarely much point in doing this. A single classifier is equivalent to a production rule, the same structures that form the basis of most expert systems if a set of classifiers that could adequately control the environment could be created by hand, it would probably be as easy to create an equivalent expert system (ES). As an ES is able to explain its actions but a CS is not, in these circumstances, an ES would be preferable. 

How do we construct programs that aid us in reasoning as opposed to calculating AI is the underlying science. It has several sub-disciplines, including, for example, robotics, machine vision, natural language understanding and expert systems, each of which will make a contribution to the second computer age. My focus is on expert systems. 

Knowledge engineering is the technology behind construction of expert systems, or knowledge systems, or expert support systems. Such systems are designed to advise, inform and solve problems. They can perform at the level of experts, and in some cases exceed expert performance. They do so not because they are "smarter but because they represent the collective expertise of the builders of the systems. They are more systematic and thorough. And they can be replicated and used throughout a laboratory, company or industry at low cost. 

It is interesting to correlate these rules with the first rules that were estimated with no help from RuleMaster. These were the rules used to construct the first prototype expert system. GloveAId for non-halogenated aromatic compounds ... 

Pharmacokinetics and Drug Dosage Regimen Design—A Possible Application Requiring Construction and Manipulation of a Complex Model and Data Base with an Expert System... 

The Definite Clause Grammar (DCG) formalism [7] is utilized throughout this project. Grammar rules are used in the expert system rules to recognize the general class of the parent molecule in the disconnection (c.y., cyclohexene). The class determines the patterns used to construct the resultant synthons (discussed in Section 4). 

An integrated GC/IR/MS instrument is a powerful tool for rapid identification of thermally generated aroma compounds. Fourier transform infrared spectroscopy (GC/IR) provides a complementary technique to mass spectrometry (MS) for the characterization of volatile flavor components in complex mixtures. Recent improvements in GC/IR instruments have made it possible to construct an integrated GC/IR/HS system in which the sensitivity of the two spectroscopic detectors is roughly equal. The combined system offers direct correlation of IR and MS chromatograms, functional group analysis, substantial time savings, and the potential for an expert systems approach to identification of flavor components. Performance of the technique is illustrated with applications to the analysis of volatile flavor components in charbroiled chicken. 

A novel approach to reduce the experimental effort associated with constructing pseudoternary phase diagrams is by using expert systems to predict the phase behavior of multicomponent ME-forming systems. Artificial neural networks have been investigated and were shown to be promising in phase behavior studies [17,35,36] as well as in the process of ingredient selection [37]. 

A very important role in the proliferation and practical use of mathematical chemistry has been played by computers, especially in the last two decades, when the use of computers was extended from numerical computations and data handling to decision-making and logical problem-solving processes. The DENDRAL project, started at Stanford in the 1960s, may serve as an illustrative example [3], It was fairly successful, and strongly influenced the collaboration between chemists, computer scientists, and mathematicians. Many applications of artificial intelligence in chemistry, and especially the construction of expert systems, are natural consequences of this collaboration [4],... 

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