Expert system shell

At the start of the development, it had been intended use an expert system shell to implement this tool, however, after careful consideration, it was concluded that this was not the optimum strategy. An examination procedure can be considered as consisting of two parts fixed documentary information and variable parameters. For the fixed documentary information, a hypertext-like browser can be incorporated to provide point-and-click navigation through the standard. For the variable parameters, such as probe scanning paths, the decisions involved are too complex to be easily specified in a set of rules. Therefore a software module was developed to perfonn calculations on 3D geometric models, created fi om templates scaled by the user. 

Case-based reasoning is very much dependent on the structure and content of its cases and their representation because case retrieval involves identifying those features in the problem that best match those in the case base. The dynamic addition of new cases means that CBR is intrinsically a learning methodology such that the performance of an expert system based on this approach will improve with time [9]. Systems may be developed with conventional computer languages or shells [7]. 

Taken together, all of these points suggest that it might be possible to prepare a toolkit consisting of the essential components of an ES, apart from the knowledge base, and then fill it with application-specific data. This is such a useful way to work that in all expert systems there is a clean separation between the information that the ES manipulates and the tools that are required to perform that manipulation. This division between the part of an ES that changes between applications and that which is constant has led to the development of the expert system shell. 

Once one has built or acquired an expert system shell, an expert system is usable and useful at an early stage of development. Subsequent development consists of increasing and refining the knowledge, expanding the functionality and improving the efficiency of the system. 

General. We have studied the characterization of multicomponent materials by combining modem analytical instrumentation with a commercially available AI expert system development tool. Information generated from selected analytical databases may be accessed using TIMM, ( The Intelligent Machine Model, ) available from General Research Corp., McLean, VA. This Fortran expert system shell has enabled development of EXMAT, a heuristically-1inked network of expert systems for materials analysis. 

Additionally, use of a commercial AI shell for expert system development has been demonstrated without the need to learn computer programming languages (C, Pascal, LISP or any of its variations), nor to have an intermediary knowledge engineer. Although this development effort of 4-5 man months was on a minicomputer, adaptation of EXMAT to the microcomputer version of TIMM is anticipated. The completed implementation of EXMAT will support the belief that AI combined with intelligent instrumentation can have a major impact on future analytical problem-solving. 

At present, most of Shell s robots are self-correcting in that they can be programmed to respond to particular kinds of error. Newer robots will be more involved in making on-line decisions, and could ultimately evolve into expert systems. 

The development of expert systems need not be costly. There are several expert system shells commercially available, and an expert in artificial intelligence (AI) is no longer needed to program them. Simple IF-THEN rules can easily be programmed in more commonly used languages like FORTRAN and BASIC. In fact, the McDonnell Aircraft expert system referred to earilier was programmed in FORTRAN. Cost depends on the number of rules and... 

Several expert systems of this type have been described in the literature (Table 1). In DECADE (DE-sign expert for CAtalyst DEvelopment) Baiiarcs-Al-cantara et al. [19] used knowledge processing methods (the expert system shell) of an expert system which was previously used for other problems, and added information about CO hydrogenation catalysis. Proposals for the selection of a catalyst and reaction conditions arc reached on different levels of fundamental knowledge ... 

Relational database with a user friendly front-end Shell-based expert system with a database on CP data... 

Expert systems can be developed using either conventional computer languages, special purpose languages, or with the assistance of development shells or tool-kits. Conventional languages such as PASCAL and C have the advantages of wide applicability and full flexibility to create the control and inferencing strategies required. They also are well supported and easy to customize. However, considerable amounts of time and effort are needed to create the basic facilities. 

Expert system shells and tool kits are sets of computer programs written in either conventional or specialized languages that can form an expert system when loaded with the relevant knowledge. They compromise on applicability and flexibility but allow more rapid development. Many offer basic facilities, including the means to prepare and store knowledge as a set of rules and to make deductions by chaining the rules together in an inferential process. 

Andersen, S. K., Jensen, F. V., Olesen, K. G., Jensen, F. (1989). HUGIN A shell for building Bayesian belief universes for expert systems [Computer program]. Aalborg, Denmark HUGIN Expert Ltd. 

Expert systems use this approach for problem solving by separating technical aspects clearly from the domain aspects. Consequently, the domain experts are seldom development experts who create an expert system. However, developers of expert systems are able to create a reusable shell that allows domain experts to enter knowledge in an understandable fashion. 

Expert System Shells are software packages that facilitate the building of knowledge-based systems by providing a knowledge representation scheme and an inference engine. The developer adds domain knowledge. 

Most expert systems are developed via specialized software tools called shells. These shells come equipped with an inference mechanism — backward chaining, forward chaining, or both — and require knowledge to be entered according to a specified format. We will have a closer look at several tools for developing expert system in this chapter. 

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