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Explicit Representations of Problem-Solving Strategies to Support Knowledge Acquisition

机译:解决问题策略的明确表示形式,以支持知识获取

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Role-limiting approaches support knowledge acquisition (KA) by centering knowledge base construction on common types of tasks or domain-independent problem-solving strategies. Within a particular problem-solving strategy, domain-dependent knowledge plays specific roles. A KA tool then helps a user to fill these roles. Although role-limiting approaches are useful for guiding KA, they are limited because they only support users in filling knowledge roles that have been built in by the designers of the KA system. EXPECT takes a different approach to KA by representing problem-solving knowledge explicitly, and deriving from the current knowledge based the knowledge gaps that must be resolved by the user during KA. This paper contrasts role-limiting approaches and EXPECT's approach, using the propose-and-revise strategy as an example. EXPECT not only supports users in filling knowledge roles, but also provides support in making other modifications to the knowledge base, including adapting the problem-solving strategy. EXPECT's guidance changes as the knowledge base changes, providing a more flexible approach to knowledge acquisition. This work provides evidence supporting the need for explicit representations in building knowledge-based systems.
机译:角色限制方法通过将知识库构建集中在常见任务类型或与领域无关的问题解决策略上来支持知识获取(KA)。在特定的问题解决策略中,与领域相关的知识扮演着特定的角色。然后,KA工具可以帮助用户填补这些角色。尽管角色限制方法对于指导KA很有用,但由于它们仅在填充KA系统的设计人员所内置的知识角色时支持用户,因此它们受到了限制。 EXPECT通过显式表示解决问题的知识,并根据当前知识派生用户在KA期间必须解决的知识差距,对KA采取不同的方法。本文以提议和修订策略为例,对比了角色限制方法和EXPECT的方法。 EXPECT不仅支持用户担任知识角色,而且还提供对知识库进行其他修改(包括调整问题解决策略)的支持。 EXPECT的指导随着知识库的变化而变化,从而为知识获取提供了一种更加灵活的方法。这项工作提供了证据,支持在建立基于知识的系统中需要明确表示的需求。

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