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

自培根和伽利略的时代以来，阐述关于自然现象的假说并用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推理中也很重要。然而，法律领域在很大程度上不同于自然科学和数学。确定一项假设的规则和拟议的结果是否与过去的法律决定一致，更多的是一个解释问题。这个项目的目标是(1)设计和评估一个人工智能(AI)认知模型，该模型在解释领域法律推理中构建和测试假设，以及(2)将该模型整合到智能教学系统(ITS)中，以教授法律系学生这一过程。该项目建立在两个最新发展的基础上：(1)一种新发明的手段，可以根据现有先例的人工智能数据库来框架和评估预测新案件结果的假设；(2)一个方便的、在线的美国最高法院口头辩论语料库，以听觉和书面形式，包括许多法律假设框架和测试的具体例子。为了回应辩护人提出的如何决定案件的假设，法官们经常通过提出假设来挑战它，有时会迫使倡导者修改或放弃假设。通过对这些例子的研究，研究人员、参与研究的法律系学生和法学教师将对构建和测试法律假设的过程进行图案化和模型化，以计算的方式实施，以经验的方式进行评估，并将其用于智能交通系统的设计。导师将在不同的法律领域实施该模型，每个领域都有一系列法律规则、问题、先例和原则，并以支持假设形成、预测、测试和解释的方式运作。使用该模型，它将指导和挑战学生的论点。它将预测案件的结果，帮助学生构建证明预测正确的测试和理由，并帮助他们通过提出或回应假设挑战来评估假设。研究人员将从以下几个方面评估该项目的成功：(1)模型对新案件预测的准确性以及它改善案件检索的程度；(2)模型生成的论点与最高法院口头辩论或法律系学生生成的论点相比有多好；(3)受过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有多好；(4)受过ITS培训的学生是否能对最高法院的口头辩论做出更准确的自我解释。这项工作将人工智能技术扩展到一个结构比自然科学和数学差得多的领域，更像是人工智能尚未解决的常识领域。通过使用人工智能对认知现象进行实证研究，在解释领域构建和测试假设，它将有助于人工智能与法律、基于案例的推理、人工智能与教育以及认知科学的研究。

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.