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VP-Expert
AI software From Wikipedia, the free encyclopedia
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VP-Expert is an artificial intelligence development tool that gained popularity in the late 1980s and early 1990s. Published by Paperback Software, VP-Expert was designed to facilitate the creation of rule-based expert systems, primarily for applications in business and industry.[1][2] It was the best-selling expert-system software for microcomputers in the late 1980s.
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History
VP-Expert was created by Brian Sawyer and published by Paperback Software in 1987.[3] VP-Expert was widely adopted during the late 1980s. By April 1989, InfoWorld described it as "the best-selling expert-system software for personal computers."[4]
In June 1991, ownership of VP-Expert transferred from Paperback Software to WordTech Systems, Inc.[5] following Paperback Software’s liquidation after a legal dispute with Lotus Development Corporation regarding its VP-Planner spreadsheet.[6] VP-Expert continued to receive positive reviews with InfoWorld stating in 1992 "for automatically creating simple expert systems and being able to edit them into more sophisticated applications, hardly a better product exists than VP-Expert".[7]
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Features
VP-Expert used an inference engine based on backward chaining to reach conclusions through English-like if/then rules. It operated through a text interface and included an explanation facility that showed the reasoning steps used to justify its conclusions.[8]
Applications
VP-Expert found applications across various domains. In environmental analysis, researchers used VP-Expert to develop a knowledge-based system for analyzing the impact of particulate matter air pollution on human health.[9] In engineering design, VP-Expert was utilized in the creation of a prototype expert system to assist in fishway design.[10] In aviation management, the tool was employed to develop an expert system aimed at maximizing airport capacity while adhering to noise-mitigation plans.[11]
Limitations
While VP-Expert offered certain advantages, it also had limitations. Its rule-based approach could become challenging to manage for large and complex knowledge bases,[8] and the process of eliciting and encoding knowledge from experts could be time-consuming and difficult.[8]
References
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