Frank M. Shipman
Raymond J. McCall
Abstract
Computers require formally represented information to perform computations that support users; yet users who have needed such support have often proved to be unable or unwilling to formalize it. To address this problem, this article introduces an approach called incremental formalization, in which, first, users express information informally and then the system aids them in formalizing it. Incremental formalization requires a system architecture the (1) integrates formal and informal representations and (2) supports progressive formalization of information. The system should have both tools to capture naturally available informal information and techniques to suggest possible formalizations of this information. The hyper-object substrate (HOS) was developed to satisfy these requirements. HOS has been applied to a number of problem domains, including network design, archeological site analysis, and neuroscience education. Users have been successful in adding informal information and then later formalizing it incrementally with the aid of the system. Our experience with HOS has reaffirmed the need for information spaces to evolve during use and has identified additional considerations in the design and instantiation of systems enabling and supporting incremental formalization
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Index Terms
- Incremental formalization with the hyper-object substrate
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Towards the formalisation of object-oriented methodologies
SAICSIT '11: Proceedings of the South African Institute of Computer Scientists and Information Technologists Conference on Knowledge, Innovation and Leadership in a Diverse, Multidisciplinary EnvironmentFormal methods have been shown to be beneficial in increasing the quality of and confidence in software systems. The adoption of formal methods in industry has however been limited where the use of informal and semi-formal notations is favoured. To ...
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Christopher Fox
The term “formalization” is used here in the sense of “recording detailed information explicitly for computational processing” rather than in the sense of “stating with mathematical rigor.” In short, this paper is about knowledge representation in artificial intelligence, not about mathematically rigorous statements of requirements and designs. The system discussed allows its users to enter explicit knowledge about a domain gradually rather than all at once, and with modest help from the system itself. The goal is to overcome user unwillingness to enter such information during the creation of knowledge-based systems. The paper reports three uses of the system: two graduate class projects, and one research prototype. The authors claim the system was successful based on these case studies, but no formal experiments were conducted and no measurements of any kind are reported. This paper may be of interest to researchers in knowledge acquisition and computer-supported cooperative work.
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