エキスパートシステムの試行/開発/利用/保守環境

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Recently, many expert systems have been developed as the expert system building tools have come into the market. However, most tools are designed for the knowledge engineers and/or the programmers. This paper presents four environments to build expert systems based on the analysis of the expert system building process and its participants : (1) Experiment environment which allows experts to make a small knowledge base, (2) Development environment which allows knowledge engineers and programmers to design and implement the expert systems for practical use, (3) Operation environment which allows end-users to execute the expert systems, and (4) Maintenance environment which allows experts to refine the knowledge base. The development environment among them provides basic functions to build expert systems, which includes three primitives : OBJECT which represents knowledge, K-SET (Knowledge SET) which stores knowledge, and STAGE which is a working area to verify knowledge. OBJECTs interact with each other on STAGE by sending and receiving message, each of them is so modular that one can modify any OBJECT, respectively. STAGE is designed to shorten the turn around time for verifying the knowledge base. K-SET clarifies the source of knowledge and is responsible for security. Three kinds of view for knowledge base, defined by the knowledge engineers, are presented for other environments. In the operation environment, the knowledge operation view allows one OBJECT to join in more than one expert system. Knowledge maintenance view presents application specific user interface in the maintenance environment to refine knowledge base. Standard knowledge maintenance view (experiment view) acts in the experiment environment as if it were domain specific expert system building tool. The presented architecture supports whole life cycle to build expert systems. Once a new expert system is built by knowledge engineers, its knowledge maintenance views would be also used as the standard knowledge views in experiment environment for similar expert systems. If an expert system generated from the experiment environment is not appropriate for a new domain, then it would be modified in the development environment. The presented environments facilitate the enlargement of the computerization field in the business applications.
社団法人人工知能学会の論文
1989-07-20

エキスパートシステムの試行/開発/利用/保守環境

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