Hypothesis Formation and Testing in an Interpretive Domain: a Model and Intelligent Tutoring System

自培根和伽利略以来，对自然现象提出假设并根据经验数据对其进行检验一直是自然科学的基石。作为一种认知框架，假设的形成和检验在法律的推理中同样重要。然而，法律的领域与自然科学和数学在一个重要的方面是不同的。确定一个假设的规则和提议的结果是否与过去的法律的决定一致，更多的是一个解释的问题。该项目的目的是（1）设计和评估一个人工智能（AI）认知模型的框架和测试假设的解释域，法律的推理，（2）将该模型纳入智能辅导系统（ITS）教法律学生的过程。该项目建立在两个最新的发展基础上：（1）一种新发明的方法，根据现有先例的人工智能数据库来构建和评估预测新案件结果的假设;（2）一个方便的在线语料库，以听觉和书面形式提供美国最高法院口头辩论，包括许多法律的假设框架和测试的具体例子。为了回应辩护律师提出的关于案件应该如何判决的假设，大法官经常通过提出假设来挑战它，有时迫使辩护律师修改或放弃假设。通过研究这些例子，研究人员，参与法律专业的学生和法律教师将图式化和建模的框架和测试法律的假设的过程中，计算实现它，评估它的经验，并使用它来设计ITS。导师将在各个法律的领域实施该模型，每个领域都有一套法律的规则，问题，先例和原则，以支持假设制定，预测，测试和解释的方式操作。利用该模型，它将引导和挑战学生的论点。它将预测案例的结果，帮助学生构建测试和合理的预测，并帮助他们通过提出或回应假设的挑战来评估假设。研究人员将根据以下方面来评估项目的成功：（1）模型对新案例的预测准确性以及它改善案例检索的程度;（2）模型生成的论点与最高法院口头辩论或法律学生生成的论点相比如何;（3）接受过ITS培训的学生与使用传统法学院方法教授相同过程的对照组相比如何;（4）接受过ITS培训的学生是否对最高法院的口头辩论做出了更准确的自我解释。这项工作将人工智能技术扩展到了一个比自然科学和数学结构更不完善的领域，一个更像人工智能尚未解决的常识领域。 通过使用人工智能来经验性地调查认知现象，在解释领域中构建和测试假设，它将有助于人工智能法律，基于案例的推理，人工智能教育和认知科学的研究。

Hypothesis Formation and Testing in an Interpretive Domain:a Model and Intelligent Tutoring SystemAbstractSince the days of Bacon and Galileo, formulating hypotheses about natural phenomena and testing them against empirical data have been cornerstones of the natural sciences. As a cognitive framework, hypothesis formation and testing are also important in legal reasoning. The legal domain, however, is different from natural science and mathematics in a significant respect. Determining whether a hypothesized rule and proposed outcome are consistent with past legal decisions is much more a matter of interpretation. The aims of this project are to (1) design and evaluate an Artificial Intelligence (AI) cognitive model of framing and testing hypotheses in an interpretive domain, legal reasoning, and (2) incorporate the model in an intelligent tutoring system (ITS) to teach law students the process. The project builds upon two recent developments: (1) a newly invented means to frame and evaluate hypotheses predicting the outcomes of new cases based on an AI database of existing precedents; (2) a convenient, on-line corpus of U.S. Supreme Court oral arguments in aural and written form, including many concrete examples of legal hypothesis framing and testing. In response to an advocate's proposed hypothesis of how the case should be decided, the Justices often challenge it by posing hypotheticals, sometimes forcing the advocate to modify or abandon the hypothesis. By studying these examples, the researchers, participating law students and law faculty will schematize and model the process of framing and testing legal hypotheses, implement it computationally, evaluate it empirically, and use it to design the ITS. The tutor will implement the model in various legal domains, each with a body of legal rules, issues, precedents, and principles, operationalized in a way that supports hypothesis formulation, prediction, testing, and explanation. Using the model, it will guide and challenge students' arguments. It will predict outcomes of cases, help students construct tests and rationales justifying the prediction, and help them evaluate the hypothesis by posing or responding to hypothetical challenges.The researchers will evaluate the project's success in terms of: (1) the accuracy of the model's predictions for new cases and the extent it improves case retrieval; (2) how well model-generated arguments compare to those in the Supreme Court oral arguments or generated by law students; (3) how well ITS-trained students compare to a control group taught the same process using conventional law school methods; (4) whether ITS-trained students generate more accurate self-explanations of the Supreme Court oral arguments. This work extends AI techniques to a much less well-structured domain than natural science and mathematics, one more like the common sense domains AI has yet to address. By using AI to investigate empirically a cognitive phenomenon, framing and testing hypotheses in an interpretive domain, it will contribute to research in AI & Law, Case-based Reasoning, AI & Education, and Cognitive Science.