Abstract
Expert system shells, AI tool kits and specialized AI languages showed to considerably improve the efficiency of expert system development [1]. The three types of tools represent different levels of support, comfort and specialization. Specialized AI languages support symbolic data processing and are well-suited for the development of tools for knowledge-based systems. Notwithstanding, even the logic-oriented language, PROLOG can hardly be used for knowledge representation directly. AI tool kits usually support a range of very general representation languages and are therefore often called hybrid tools. These representation languages can be used for knowledge representation in a flexible manner [2]. But as the basis of such flexibility is the absence of a proper semantics for many representational elements and the lack of special problem-solving strategies, the use of such tools demands an unusual represenational discipline and considerable effort for designing problem-solving strategies and appropriate structuring of knowledge. Furthermore, the flexibility of AI tool kits is dearly paid for by weak knowledge engineering tools and difficult handling. The highest level of support and the maximum efficiency is representated by expert system shells [3]. They originate from complete expert systems by taking out all the domain-dependent knowledge [4, 5]. They therefore come with a well-selected knowledge representation formalism, a predefined problemsolving strategy, explanation, trace and display facilities adapted to the knowledge representation formalism and the problem-solving strategy and an integrated dialogue component. They are usually easy to understand and to use and offer high comfort for development. But their predefined problem-solving strategy limits their use to a certain class of applications and therefore the listed advantages only hold when they are used for an adequate problem [1]. Beside this limitation, most shells do not offer the full range of knowledge engineering tools, which are possible and suitable, especially for large applications. In the following we first discuss these problems with expert system shells in some detail. Then we give a brief survey of TWAICE, before we describe some of its knowledge engineering features that are usually not found in expert system shells.
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