We present a model of similarity‐based retrieval that attempts to capture three seemingly contradictory psychological phenomena: (a) structural commonalities are weighed more heavily than surface commonalities in similarity judgments for items in working memory; (b) in retrieval, superficial similarity is more important than structural similarity; and yet (c) purely structural (analogical) remindings e sometimes experienced. Our model, MAC/FAC, explains these phenomena in terms of a two‐stage process. The first stage uses a computationally cheap, non‐structural matcher to filter candidate long‐term memory (...) items. It uses content vectors, a redundant encoding of structured representations whose dot product estimates how well the corresponding structural representations will match. The second stage uses SME (structure‐mapping engine) to compute structural matches on the handful of items found by the first stage. We show the utility of the MAC/FAC model through a series of computational experiments: (a) We demonstrate that MAC/FAC can model patterns of access found in psychological data; (b) we argue via sensitivity analyses that these simulation results rely on the theory; and (c) we compare the performance of MAC/FAC with ARCS, an alternate model of similarity‐based retrieval, and demonstrate that MAC/FAC explains the data better than ARCS. Finally, we discuss limitations and possible extensions of the model, relationships with other recent retrieval models, and place MAC/FAC in the context of other recent work on the nature of similarity. (shrink)

Law and legal reasoning are a natural target for artificial intelligence systems. Like medical diagnosis and other tasks for expert systems, legal analysis is a matter of interpreting data in terms of higher-level concepts. But in law the data are more like those for a system aimed at understanding natural language: they tell a story about human events that may lead to a lawsuit. Statements of the law, too, are written in natural language and legal arguments are often arguments about (...) what that language means or ought to mean.This study is one of the few research efforts in this fertile area. It is unique in developing a computational model for analyzing legal problems in a way that brings these strands of AI research together and makes sense from a jurisprudential perspective as well.Gardner first analyzes several positions in Anglo-American jurisprudence and their relevance for work in artificial intelligence. She identifies aspects of legal reasoning that any truly expert system in law must make a place for and suggests a way of decomposing the process of legal analysis that takes these aspects into account. She compares the resulting framework with those used by other legal analysis programs. A solid exposition of current AI techniques follows in chapters covering the author's system (written in Maclisp) for offer and acceptance problems, taken from law examinations, involved in contract law.Anne von der Lieth Gardner has a law degree and a Ph.D. in computer science, both from Stanford University. An Artificial Intelligence Approach to Legal Reasoning inaugurates the series Artificial Intelligence and the Law: Processes and Models of Legal Reasoning, edited by L. Thorne McCarty and Edwina L. Rissland. A Bradford Book. (shrink)

We discuss several aspects of legal arguments, primarily arguments about the meaning of statutes. First, we discuss how the requirements of argument guide the specification and selection of supporting cases and how an existing case base influences argument formation. Second, we present,our evolving taxonomy of patterns of actual legal argument. This taxonomy builds upon our much earlier work on argument moves and also on our more recent analysis of how cases are used to support arguments for the interpretation of legal (...) statutes. Third, we show how the theory of argument used by CABARET, a hybrid case-based/rule-based reasoner, can support many of the argument patterns in our taxonomy. (shrink)

The BankXX system models the process of perusing and gathering information for argument as a heuristic best-first search for relevant cases, theories, and other domain-specific information. As BankXX searches its heterogeneous and highly interconnected network of domain knowledge, information is incrementally analyzed and amalgamated into a dozen desirable ingredients for argument (called argument pieces), such as citations to cases, applications of legal theories, and references to prototypical factual scenarios. At the conclusion of the search, BankXX outputs the set of argument (...) pieces filled with harvested material relevant to the input problem situation.This research explores the appropriateness of the search paradigm as a framework for harvesting and mining information needed to make legal arguments. In this article, we describe how legal research fits the heuristic search framework and detail how this model is used in BankXX. We describe the BankXX program with emphasis on its representation of legal knowledge and legal argument. We describe the heuristic search mechanism and evaluation functions that drive the program. We give an extended example of the processing of BankXX on the facts of an actual legal case in BankXX's application domain — the good faith question of Chapter 13 personal bankruptcy law. We discuss closely related research on legal knowledge representation and retrieval and the use of search for case retrieval or tasks related to argument creation. Finally we review what we believe are the contributions of this research to the understanding of the diverse disciplines it addresses. (shrink)

The New Rhetoric is founded on the idea that since “argumentation aims at securing the adherence of those to whom it is addressed, it is, in its entirety, relative to the audience to be influenced,” says Chaïm Perelman and L. Olbrechts-Tyteca, and they rely, in particular, for their theory of argumentation on the twin concepts of universal and particular audiences: while every argument is directed to a specific individual or group, the orator decides what information and what approaches will achieve (...) the greatest adherence according to an ideal audience. This ideal, Perelman explains, can be embodied, for example, "in God, in all reasonable and competent men, in the man deliberating or in an elite.” Like particular audiences, then, the universal audience is never fixed or absolute but depends on the orator, the content and goals of the argument, and the particular audience to whom the argument is addressed. These considerations determine what information constitutes "facts" and "reasonableness" and thus help to determine the universal audience that, in turn, shapes the orator's approach. /// The adherence of an audience is also determined by the orator's use of values, a further key concept of the New Rhetoric. Perelman's treatment of value and his view of epideictic rhetoric sets his approach apart from that of the ancients and of Aristotle in particular. Aristotle's division of rhetoric into three genres–forensic, deliberative, and epideictic–is largely motivated by the judgments required for each: forensic or legal arguments require verdicts on past action, deliberative or political rhetoric seeks judgment on future action, and epideictic or ceremonial rhetoric concerns values associated with praise or blame and seeks no specific decisions. For Aristotle, the epideictic genre was of limited importance in the civic realm since it did not concern facts or policies. Perelman, in contrast, believes not only that epideictic rhetoric warrants more attention, but that the values normally limited to that genre are in fact central to all argumentation. "Epideictic oratory," Perelman argues, "has significant and important argumentation for strengthening the disposition toward action by increasing adherence to the values it lauds.” These values are central to the persuasiveness of arguments in all rhetorical genres since the orator always attempts to "establish a sense of communion centered around particular values recognized by the audience.”. (shrink)