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Contextual relevance in analogical reasoning: A model of legal argument.

机译:类比推理中的上下文相关性:法律论证的模型。

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Analogical reasoning is used in rational argument and problem-solving. It is used by knowledge workers in organizations for many kinds of problem-solving tasks. Providing information systems to support the information requirements of knowledge workers requires an understanding of action and representation knowledge required in such tasks. Analogical reasoning is a knowledge-based, problem-solving process in which intelligent agents apply knowledge from precedents to problems. The justification for use of a precedent is relevant similarity between a problem and a precedent.; The contextual relevance theory of analogical reasoning explains the process by which relevance judgment varies with a change in problem solving context. Relevance judgment is the selection of a precedent as relevant to a problem case. The theory explains a method for measuring similarity that is computationally practicable in information processors with bounded resources.; The contextual relevance theory is a knowledge level theory of analogical reasoning. The unit of analysis is knowledge possessed by an agent as opposed to a particular symbolic representation of the knowledge. In the contextual relevance theory conceptual knowledge is the knowledge that permits effective similarity judgment and that affords shifts in relevance judgment with changes in context. Conceptual knowledge includes heuristic schemata for recognizing possible instances of a concept and includes constraints on instances of a concept. The similarity of a problem to a precedent is measured with respect to a concept. Situations that contain objects that are instances of the same concept are similar with respect to that concept. A precedent is useful when it provides knowledge that helps to meet a goal in a problem. Relevance is based on conceptual similarity and usefulness.; BRAMBLE is a model of the contextual relevance theory of analogical reasoning. It is also a design specification for a computer program implementing the theory. BRAMBLE is realized as a logic program written in MRS. The knowledge-base in BRAMBLE is derived from data collected in a study of legal reasoning about corporate acquisitions. The program demonstrates and tests the contextual relevance theory, showing the power of conceptual similarity to respond to changes in context and to generate contextually plausible analogical arguments.
机译:类比推理用于理性论证和问题解决。组织中的知识工作者使用它来解决许多类型的问题。提供信息系统以支持知识工作者的信息需求需要了解此类任务中所需的动作和表示知识。类比推理是一个基于知识的问题解决过程,其中智能代理将知识从先例应用于问题。使用先例的理由是问题与先例之间的相关相似性。类比推理的上下文相关理论解释了相关判断随问题解决上下文的变化而变化的过程。关联性判断是选择与问题案例相关的先例。该理论解释了一种用于测量相似性的方法,该方法在具有有限资源的信息处理器中在计算上是可行的。上下文相关理论是类比推理的知识水平理论。分析的单位是代理拥有的知识,而不是知识的特定符号表示。在上下文相关性理论中,概念性知识是指可以进行有效的相似性判断并随着上下文的变化提供相关性判断变化的知识。概念知识包括用于识别概念的可能实例的启发式图式,并且包括对概念实例的约束。从概念上衡量问题与先例的相似性。包含作为同一概念实例的对象的情况在该概念方面相似。当先例提供有助于实现问题目标的知识时,它很有用。相关性基于概念上的相似性和实用性。 BRAMBLE是类比推理的上下文相关理论的模型。它也是实现该理论的计算机程序的设计规范。 BRAMBLE是用MRS编写的逻辑程序。 BRAMBLE中的知识库源自对公司收购的法律推理研究中收集的数据。该程序演示并测试了上下文相关性理论,显示了概念相似性对上下文中的变化做出响应并生成上下文中合理的类比论证的力量。

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