EAGER: Modeling Interpretive Argument with Case Analogies and Rules in Ill-Defined Domains

EAGER：在定义不明确的领域中通过案例类比和规则对解释性论证进行建模

• 批准号: 1049414
• 负责人: Kevin Ashley
• 金额: $ 5万
• 依托单位: University of Pittsburgh
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2012-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1049414&HistoricalAwards=false
• 关键词: EAGER; Modeling; Interpretive; Argument; Case

In ill-defined domains such as engineering design and ethics, mathematical discovery, and law, problems often have more than one 'right' answer. In such situations decision-makers propose rules or hypotheses about how to decide the problem in light of past cases and underlying principles and policies; through this process of interpretive argument decision-makers may then pose hypothetical examples in order to draw out and test the normative, logical, or empirical consequences of the hypothesis. In integrating decision rules, case analogies, hypothetical examples, underlying principles, and policies, interpretive argument is a paradigm of robust reasoning. Although the problems are ill-defined, interpretive argument has an underlying logic, which this project is modeling computationally. By working in successively more complex argument microworlds, families of cases, rules, concepts, principles and policies drawn from realistic legal domains, the project is developing an ontology and implementing inference control mechanisms and argument schema with which a computer program will engage in interpretive argument. Empirical evaluation of the computational model is comparing arguments by the computer and by human arguers.The work contributes to the fields of AI, argumentation theory, AI and Law, and case-based reasoning, and it aims in the longer term to contribute to intelligent tutoring systems that will prepare students for making effective, supportable and otherwise rational arguments, and to design artificial agents as proxies and advocates for humans engaged in disputes.

在工程设计和伦理学、数学发现和法律等定义不清的领域，问题往往有不止一个“正确”答案。在这种情况下，决策者会提出规则或假设，说明如何根据过去的案例以及基本原则和政策来决定问题;通过这一解释性论证过程，决策者可能会提出假设性的例子，以引出和检验假设的规范性、逻辑性或经验性后果。在整合决策规则，案例类比，假设的例子，基本原则和政策，解释性论点是一个强大的推理范式。虽然问题是不明确的，解释性论点有一个潜在的逻辑，这个项目是计算建模。通过在更复杂的论证微观世界中工作，从现实的法律的领域中提取的案例、规则、概念、原则和政策的家庭，该项目正在开发本体，并实现推理控制机制和论证模式，计算机程序将参与解释性论证。计算模型的实证评估是比较计算机和人类辩论者的论点。这项工作有助于人工智能，论证理论，人工智能和法律，以及基于案例的推理领域，其长期目标是为智能教学系统做出贡献，使学生能够做出有效的，可支持的和其他理性的论点，并设计人工代理人作为代理人和倡导人参与争端。