In this paper, we describe exploratory experiments for detecting potential "Gray Cygnet" cases that follow a known Black Swan. Gray Cygnets (GCs) are cases that are highly similar and subsequent to novel, surprising, provocative, exceptional cases, so-called Black Swans. They too are surprising, exceptional and provocative in the sense of continuing the change initiated by the Black Swan. Our experiments were carried out using a corpus of common law cases from the United States, particularly New York and Massachusetts, and the United Kingdom primarily in the era 1852-1916 during which there was dramatic change in the prevailing doctrine regarding recovery for damages by a remote buyer. It was provoked by the 1852 landmark case Thomas & Wife v. Winchester and elaborated in subsequent cases. Our methods use similarity measures from Case-Based Reasoning (CBR) based on classic claim lattice and nearest neighbor techniques. We also define and use "supreme on-point cases" (sopc's) that reside in the root node of a claim lattice. We define four simple tests to determine whether a problem case is a potential Gray Cygnet. The tests use the position of a known Black Swan in the analysis of a problem case, specifically whether it appears as a sopc, or mopc, and/or as a nearest neighbor. We also use the tests as an ensemble by using the decision of their majority vote. To evaluate the performance of our tests, we created an answer key based on a closed form definition of a GC in our domain.

We present the SPIRE system, a hybrid case-based reasoning (CBR) and information retrieval (IR) system. It relies on a small case-base to seed the IR system to 1) select documents that are relevant to a presented problem case, and then (2) highlight within these retrieved documents passages that contain relevant information about specific case features. SPIRE aids not only problem-solving but knowledge acquisition by focusing a text extractor---person or program---on areas of text where needed information is likely to be found. Once extracted, this information can be used to create new cases or data-base objects thus closing the loop in the problem-solving---knowledge-acquisition cycle. 1 Introduction When confronted with a new problem, we frequently try to relate it to one that we have dealt with previously. If we have knowledge of a similar prior problem, we might try to apply the same or a related solution. Similarly, we might want to evaluate a scenario and hypothesize the likeli...

This paper presents a hybrid case-based reasoning (CBR) and information retrieval (IR) system, called SPIRE, that locates passages likely to contain information about legally relevant features of cases found in full-text court opinions. SPIRE uses an example base of excerpts from past opinions to form queries, which are run by the INQUERY IR text retrieval engine on individual case opinions. These opinions can be those found by SPIRE in a prior stage of processing, which also employs a hybrid CBR-IR approach to retrieve relevant texts from large document corpora. (This aspect of SPIRE was reported on at ICAIL95.) We present an overview of SPIRE, run through an extended example, and give results comparing SPIRE's with human performance. 1 Introduction There is an enormous amount of legal text available on-line and it is growing every day. While this is a decided benefit for legal research, it also presents a problem of how to search it effectively. In particular, it is no easy task to...

In this paper we discuss a hybrid approach combining CaseBased Reasoning (CBR) and Information Retrieval (IR) for the retrieval of legal documents. Our hybrid CBR-IR approach takes as input a standard symbolic representation of a problem ease and retrieves texts of relevant cases from a document corpus dramatically larger than the case base available to the CBR system. Our system works by first performing a standard HYPO-style CBR anatysis and then using texts associated with certain important classes of cases found in this analysis to “seed” a modified version of INQUERY’s relevance feedback mechanism in order to generate a query. Our approach provides two benefits: it extends the reach of CBR (for retrievat purposes) to much larger corpora, and it enables the injection of knowledgebased techniques into traditional IR. We deseribe our CBRIR approach and report on on-going experiments performed in two different legal domains.

Article Free Access Share on Detecting change in legal concepts Authors: Edwina L. Rissland Department of Computer Science, University of Massachusetts, Amherst, MA Department of Computer Science, University of Massachusetts, Amherst, MAView Profile , M. Timur Friedman Department of Computer Science, University of Massachusetts, Amherst, MA Department of Computer Science, University of Massachusetts, Amherst, MAView Profile Authors Info & Claims ICAIL '95: Proceedings of the 5th international conference on Artificial intelligence and lawMay 1995 Pages 127–136https://doi.org/10.1145/222092.222209Online:24 May 1995Publication History 25citation356DownloadsMetricsTotal Citations25Total Downloads356Last 12 Months9Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

We have built a hybrid Case-Based Reasoning (CBR) and Information Retrieval (IR) system that generates a query to the IR system by using information derived from CBR analysis of a problem situation. The query is automatically formed by submitting in text form a set of highly relevant cases, based on a CBR analysis, to a modified version of INQUERY's relevance feedback module. This approach extends the reach of CBR, for retrieval purposes, to much larger corpora and injects knowledge-based techniques into traditional IR. 1 Introduction One strength of Case-Based Reasoning (CBR) systems is the ability to reason about a problem case and perform highly intelligent problem-solving, such as the generation of legal arguments or detailed operational plans [9]. In particular, CBR systems have at their core the ability to retrieve highly relevant cases. However, CBR systems are limited by the availability of cases actually represented in their case bases. Among current case-based reasoning syst...

In this paper we describe a system, called BankXX, which generates arguments by performing a heuristic best-first search of a highly interconnected network of legal knowledge. The legal knowledge includes cases represented from a variety of points of view—cases as collections of facts, cases as dimensionally-analyzed fact situations, cases as bundles of citations, and cases as prototypical factual scripts—as well as legal theories represented in terms of domain factors. BankXX performs its search for useful information using one of three evaluation functions encoded at different levels of abstraction: the domain level, an “argument-piece” level, and the overall argument level. Evaluation at the domain level uses easily accessible information about the nodes, such as their type; evaluation at the argument-piece level uses information about generally useful components of case-based argument, such as best cases and supporting legal theories; evaluation at the overall-argument level uses factors, called argument dimensions, which address the overall substance and quality of an argument, such as the centrality of its supporting cases or the success record of its best theory. BankXX is instantiated in the area of personal bankruptcy governed by Chapter 13 of the U.S. Bankruptcy Code, which permits a debtor to be discharged from debts through completion of a court-approved payment plan. In particular, our system addresses the requirement that such Chapter 13 plans be “proposed in good faith.”

We discuss a theoqy of heurtitic strategies and tactics for making arguments in a domain guided by rules where the primary task is to use previous cases to argue for a particular interpretation of a rule in a new fact situation.Since rules frequently have various problematic aspects, such as unstated exceptions or prerequisites, or use terms that are not clearly defined, the actual interpretation is a matter of debate, and the arguer must use interpretations from precedent cases to form his argument.The argument strategies and tactics actually adopted depend on the arguer's point of view and the complexwn of his case in light of the rules and the precedents.The tactics, called "moves" here, are ultimately expressed in a small set of generic "argument primitives, " such as analogizing and distinguishing.We discuss how these argument strategies, moves, and primitives are used by our mixed paradigm system, CABARET (Roland & Skalak, 1990).We illustrate with examples from an area of U.S. Federal income tax law.

In this paper we examine a sequence of hypotheticals taken from a Supreme Court oral argument. We use the idea of a “dimension,” developed previously in our case-based reasoning system HYPO, to analyze the hypotheticals and to speculate on how the Justices might have arrived at them. The case we consider is taken from the area of Fourth Amendment law concerning warrantless search and seizure.

In this paper we discuss a hybrid approach to the problem of statutory interpretation that involves combining our past approach to case-based reasoning (“CBA”), as exemplified in our previous HYPO and TAX-HYPO systems, with traditional rule-based reasoning (“RBR”), as exemplified by expert systems. We do not tackle the fullblown version of statutory interpretation, which would include reasoning with legislative intent or other normative aspects (the “ought”), but confine ourselves to reasoning with explicit cases and rules. We discuss strategies that can be used to guide interpretation, particularly the interleaving of CBR and RBR, and how they are used in an agenda-based architecture, called CABARET, which we are currently developing in a general way and experimenting with in the particular area of Section §280A(c)(1) of the U.S. Internal Revenue Code, which deals with the so called “home office deduction”.

An interesting and important aspect of legal reasoning is the use of citations to precedent cases as justifications for legal conclusions. In this paper, we describe the standard use of citations as described in the attorney's “Blue Book” and how HYPO, a program that models case-based legal reasoning, generates and uses citations in a very similar way to analyze fact situations and to communicate with an attorney/user. More specifically, we describe how, given a fact situation (“cfs”), HYPO dynamically generates the citations to cases in its Case Knowledge Base (“CKB”) by (1) analyzing the factual features of the cfs to see what dimensions apply, (2) retrieving and constructing a “neighborhood” of citable cases around the cfs (the “Claim Lattice”) and (3) constructing the “Cites Display”, a network of citations to the most on point cases (“mopc”) that is a skeletal frame for a legal argument about the cfs.

In this paper, we give an overview of our case-based reasoning program, HYPO, which operates in the field of trade secret law. We discuss key ingredients of case-based reasoning, in general, and the correspondence of these to elements of HYPO. We conclude with an extended example of HYPO working through a hypothetical trade secrets case, patterned after an actual case.