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TASK-STRUCTURE ANALYSIS FOR KNOWLEDGE MODELING
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OA
AI
DOI：10.1145/130994.131002.png)
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摘要
En 中文
In recent years there has been increasing interest in describing complicated information processing systems in terms of the knowledge they have, rather than by the details of their implementation. This requires a means of modeling the knowledge in a system. Several different approaches to knowledge modeling have been developed by researchers working in Artificial Intelligence (AI). Most of these approaches share the view that knowledge must be modeled with respect to a goal or task. In this article, we outline our modeling approach in terms of the notion of a task-structure, which recursively links a task to alternative methods and to their subtasks. Our emphasis is on the notion of modeling domain knowledge using tasks and methods as mediating concepts. We begin by tracing the development of a number of different knowledge-modeling approaches. These approaches share many features, but their differences make it difficult to compare systems that have been modeled using different approaches. We present these approaches and describe their similarities and differences. We then give a detailed description, based on the task structure, of our knowledge-modeling approach and illustrate it with task structures for diagnosis and design. Finally, we show how the task structure can be used to compare and unify the other approaches.
Keyword:
DESIGN
METHODOLOGY
ANALYSIS
MODELING
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期刊
IF:
12.2
论文数:
1.2W
被引数:
3.7W
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