Distinguishing - A Reasoner's Wedge

In this paper we focus on the Distinguisher 's Wedge, an intellectual tool for responding to an argument that two cases are alike by asserting reasons why they are different and why the differences matter. W e characterize the wedge as involving a search for distinctions, factual differences between the cases that tie into justifications for treating them differently. W e show how the wedge can be modelled computationally in a Case-Based ReEisoning ("CBR") system using precerfenh'a/justifications and describe how the model is realized in our H Y P O program which performs legal reasoning in the domain of trade secret law. Legal argument, with its emphzisis on citing and distinguishing precedents and lack of a strong domain model, is an excellent domain for studying the wedge. W e show how H Y P O uses "dimensions", "case-analysis-record" and "claim lattice"mechanisms to cite and distinguish real cases and suggest how the model may be extended to cover more sophisticated kinds of distinguishing

Introduction: Cybersecurity in Pittsburgh

This article provides a brief introduction to cybersecurity issues in the Pittsburgh region and introduces the student article series

Teaching Law and Digital Age Legal Practice with an AI and Law Seminar

This article provides a guide and examples for using a seminar on Artificial Intelligence (AI) and Law to teach lessons about legal reasoning and about legal practice in the digital age. Artificial Intelligence and Law is a subfield of AI/ computer science research that focuses on computationally modeling legal reasoning. In at least a few law schools, the AI and Law seminar has regularly taught students fundamental issues about law and legal reasoning by focusing them on the problems these issues pose for scientists attempting to computationally model legal reasoning. AI and Law researchers have designed programs to reason with legal rules, apply legal precedents, predict case outcomes, argue like a legal advocate and visualize legal arguments. The article illustrates some of the pedagogically important lessons that they have learned in the process. As the technology of legal practice catches up with the aspirations of AI and Law researchers, the AI and Law seminar can play a new role in legal education. With advances in such areas as e-discovery, legal information retrieval (IR), and semantic processing of web-based information for electronic contracting, the chances are increasing that, in their legal practices, law students will use, and even depend on, systems that employ AI techniques. As explained in the Article, an AI and Law seminar invites students to think about processes of legal reasoning and legal practice and about how those processes employ information. It teaches how the new digital documents technologies work, what they can and cannot do, how to measure performance, how to evaluate claims about the technologies, and how to be savvy consumers and users of the technologies

Ethical Reasoning Strategies and Their Relation to Case-Based Instruction: Some Preliminary Results

This paper describes some preliminary results of an experiment to collect, analyze and compare protocols of arguments concerning practical ethical dilemmas prepared by novice and more experienced ethical reasoners. We report the differences we observed between the novice and experienced reasoners' apparent strategies for analyzing ethical dilemmas. We offer an explanation of the differences in terms of specific differences in the difficulty of the strategies' information processing requirements. Finally, we attempt to explain the utility of case-based ethics instruction in terms of the need to inculcate information processing skills required by the experienced reasoners' strategy

Using a Well-Structured Model to Teach in an Ill-Structured Domain

Our goal is to develop a tutoring system, called CATO, that teaches law students skills of making arguments with cases. CATO's domain model provides a plausible account of legal arguments with cases, but is limited in that it does not repre?sent certain background knowledge. It is important, however, that students leam to apply and integrate this background knowledge when making arguments with cases. Given that modeling this background knowledge is difficult in an ill?stiuctured domain like legal reasoning, it is worth exploring how effectively one can teach with a model that represents ar?gument structure but relatively little background knowledge. The CATO instructional envirormient, comprising a case da?tabase and retrieval tools, enables students to apply the CATO model to a specific problem. In a formative evaluation study with 17 beginning law students, we compared instruction with the CATO environment, under the guidance of a human tutor, against more traditional classroom instruction not based on the CATO model. W e found that human-led instruction with CATO is as good as, but not better than, classroom instruction. How?ever, answers generated by the CATO program received higher grades than the students' answers, suggesting that the model can potentially be employed to teach even more effectively. Examples drawn fitom protocols show that students were able to use the CATO model flexibly and integrate background knowledge appropriately, at least when guided by a human tu?tor

Case-Based Comparative Evaluation in TRUTH-TELLER

Case-based comparative evaluation appears to be an important strat?egy for addressing problems in weak analytic domains, such as the law and practical ethics. Comparisons to paradigm, hypothetical, or past cases may help a reasoner make decisions about a current dilemma. W e are investigating the uses of comparative evaluation in practical ethical reasoning, and whether recent philosophical models of casuistic rea?somng in medical ethics may contribute to developing models of com?parative evaluation. A good comparative reasoner, we believe, should be able to integrate abstract knowledge of reasons and principles into its analysis and still take a problem's context and details adequately into account. TRUTH-TELLER is a program we have developed that compares pairs of cases presentmg ethical dilemmas about whether to tell the truth by marshaling relevant similarities and differences in a context sensitive manner. The program has a variety of methods for reasoning about reasons. These include classifying reasons as prin?cipled or altruistic, comparing the strengths of reasons, and qualifying reasons by participants' roles and the criticality of consequences. W e describe a knowledge representation and comparative evaluation pro?cess for this domain. In an evaluation of the program, five professional ethicists scored the program's output for randomly-selected pairs of cases. The work contributes to context sensitive similarity assessment and to models of argumentation in weak analytic domains

Wildland Recreation Disturbance: Broad‐Scale Spatial Analysis and Management

Wildland recreation that does not involve animal harvests (non‐consumptive recreation) often influences various components of natural systems, including soils, water, air, soundscapes, vegetation, and wildlife. The effects of non‐consumptive recreation on wildlife have typically been assessed at spatial scales that are not only much smaller than the overall distributions of this disturbance but also much smaller than the areas that species use during a season or year. This disparity in scales has prevented effective assessment and management of broad‐scale recreation disturbance for many species, especially wildlife. We applied three software systems (ArcGIS, FRAGSTATS, and Conefor) to demonstrate how metrics commonly measured by landscape ecologists can be used to quantify broad‐scale patterns of non‐consumptive recreation. Analysts can employ such metrics to develop predictive models of how recreation disturbance – by itself and in additive or interactive combinations with other landscape characteristics – may affect wildlife responses across large areas. In turn, these models can inform decision making in broad‐scale recreation management

Adaptive Rückmeldungen im intelligenten Tutorensystem LARGO

The Intelligent Tutoring System LARGO is designed to help law students learn argumentation skills. The approach implemented in LARGO uses transcripts of oral arguments as learning resources: Students annotate them and create graphical representations of the argument flow. The system encourages students to reflect upon arguments proposed by the attorneys and helps students detect possible weaknesses in their analysis of the dispute. Technically, graph grammar and collaborative filtering algorithms are employed to detect these weaknesses. This article describes how “usage contexts” are determined and used to create adaptive feedback in LARGO. On the basis of a controlled study with the system that took place with law students at the University of Pittsburgh, we discuss to what extent the automatically calculated usage contexts can predict student’s learning gains

Using Event Progression to Enhance Purposive Argumentation in the Value Judgment Formalism

ABSTRACT This paper expands on the previously published value judgment formalism. The representation of situations is enhanced by introducing event progressions similar to actions in general AI planning. Using event progressions, situations can be assessed as to what facts they contain as well as what facts may ensue with some likelihood, thereby opening up a situation space. Purposive legal argumentation can be modeled using propositions and rules controlling the likelihoods of value-laden consequences. The paper expands the formalism to cover event progressions and illustrates the functionality using an example based on Young v. Hitchens