Abstract: Logic-based approaches to legal problem solving model the rule-governed nature of legal argumentation, justification, and other legal discourse but suffer from two key obstacles: the absence of efficient, scalable techniques for creating authoritative representations of legal texts as logical expressions; and the difficulty of evaluating legal terms and concepts in terms of the language of ordinary discourse. Data-centric techniques can be used to finesse the challenges offormalizing legal rules and matching legal predicates with the language of ordinary parlance by exploiting knowledge latent in legal corpora. However, these techniques typically are opaque and unable to support the rule-governed discourse needed for persuasive argumentation and justification. This paper distinguishes representative legal tasks to which each approach appears to be particularly well suited and proposes a hybrid model that exploits the complementarity of each.

Abstract: Local methods for detecting community structure are necessary when a graph's size or node-expansion cost make global community-detection methods infeasible. Various algorithms for local community detection have been proposed, but there has been little analysis of the circumstances under which one approach is preferable to another. This paper describes an evaluation comparing the accuracy of five alternative local community-detection algorithms in detecting two distinct types of community structures—vertex partitions that maximize modularity, and link clusters that maximize partition density—in a variety of graphs. In this evaluation, the algorithm that most accurately identified modularity-maximizing community structure in a given graph depended on how closely the graph's degree distribution approximated a power-law distribution. When the target community structure was partition-density maximization, however, an algorithm based on spreading activation generally performed best, regardless of degree distribution.

Abstract: Many algorithms for finding community structure in graphs search for a partition that maximizes modularity. However, recent work has identified two important limitations of modularity as a community quality criterion: a resolution limit; and a bias towards finding equal-sized communities. Information-theoretic approaches that search for partitions that minimize description length are a recent alternative to modularity. This paper shows that two information-theoretic algorithms are them- selves subject to a resolution limit, identifies the component of each approach that is responsible for the resolution limit, proposes a variant, SGE (Sparse Graph Encoding), that addresses this limitation, and demonstrates on three artificial data sets that (1) SGE does not exhibit a resolution limit on sparse graphs in which other approaches do, and that (2) modularity and the compression-based algorithms, including SGE, behave similarly on graphs not subject to the resolution limit.

Abstract: Legal analysis is a task underlying many forms of legal problem solving. In the Anglo-American legal system, legal analysis is based in part on legal precedents, previously decided cases. This paper describes a reduction-graph model of legal precedents that accounts for a key characteristic of legal precedents: a precedent's relevance to subsequent cases is determined by the theory under which the precedent is decided. This paper identifies the implementation requirements for legal analysis using the reduction-graph model of legal precedents and describes GREBE, a program that satisfies these requirements.

Abstract: Name matching - recognizing when two different strings are likely to denote the same entity, is essential for automatic detection of conflicts of interest in legal case-management systems (LCMSs). Unfortunately, most name-matching algorithms developed for LCMSs are proprietary and are therefore not amenable to independent evaluation, improvement, or comparison. This paper proposes a three-step framework for name matching in LCMSs, identifies how each step in the framework addresses the naming variations that typically arise in LCMSs, describes several alternative approaches to each step, and evaluates the performance of various combinations of the alternatives on a representative collection of names drawn from a United States District Court LCMS. The best tradeoff between accuracy and efficiency in this LCMS was achieved by algorithms that standardize capitalization, spacing, and punctuation; filter redundant terms; index using an abstraction function that is both order-insensitive and tolerant of small numbers of omissions or additions; and compare names in a symmetrical, word-by-word fashion.

Abstract: Pro se litigants constitute a growing burden to the judiciary. Advisory systems designed to help litigants understand the available legal remedies and satisfy the substantive and procedural requirements to obtain those remedies have the potential to reduce this burden. This paper presents a four-component model of advisory systems for pro se litigants. This model was implemented in the Protection Order Advisory (POA), an advisory system for pro se Protection Order applicants. POA illustrates how existing inference, document-drafting, and interface-design techniques can be used to construct advisory systems for pro se litigants in a wide range of legal domains for which (1) determining whether a prima facie case is satisfied does not require open-textured reasoning, and (2) the documents required to initiate an action are characterized by homogeneity and simple structure.

Abstract: Document drafting is an essential component of judicial problem solving. This paper distinguishes several classes of judicial documents based on (1) the stage of the judicial process in which they are created, (2) the complexity of the documents, and (3) the party who drafts the documents: a judge, judicial support personnel, or a litigant. Three approaches to automated document drafting are identified and the applicability of the these approaches to each class of judicial document is described. The paper concludes with a description of several implemented prototype systems for drafting judicial documents at various stages of the judicial process.

Abstract: Document drafting is a central judicial problem-solving activity. Development of automated systems to assist judicial document drafting has been impeded by the absence of an explicit model of (1) the connection between the document drafter's goals and the text intended to achieve those goals, and (2) the rhetorical constraints expressing the stylistic and discourse conventions of the document's genre. This paper proposes a model in which the drafter's goals and the stylistic and discourse conventions are represented in a discourse structure consisting of a tree of illocutionary and rhetorical operators with document text as leaves. A document grammar based on the discourse structures of a representative set of documents can be used to synthesize a wide range of additional documents from sets of case facts. The applicability of this model to a representative class of judicial orders--jurisdictional show-cause orders--is demonstrated by illustrating (1) the analysis of show-cause orders in terms of discourse structures, (2) the derivation of a ocument grammar from discourse structures of two typical show-cause orders, and (3) the synthesis of a new show-cause order from the document grammar.

Abstract: Many complex biological systems are characterized both by incomplete models and limited empirical data. Accurate prediction of the behavior of such systems requires exploitation of multiple, individually incomplete, knowledge sources. Model-based adaptation is a technique for integrating case-based reasoning with model-based reasoning to predict the behavior of biological systems. This approach is implemented in CARMA, a system for rangeland grasshopper management advising that implements a process model derived from protocol analysis of human expert problem-solving episodes. CARMA's ability to predict the forage consumption judgments of expert pest managers was empirically compared to that of case-based and model-based reasoning techniques in isolation. This evaluation provided initial confirmation for the hypothesis that an integration of model-based and case-based reasoning can lead to more accurate predictions than either technique individually.

Abstract: Decision support systems often require knowledge of users' preferences. However, preferences may vary among individual users or be difficult for users to articulate. This paper describes how user preferences can be acquired in the form of preference predicates by a learning apprentice system and proposes two new instance-based algorithms for preference predicate acquisition: 1ARC and Compositional Instance-Based Learning (CIBL). An empirical evaluation using simulated preference behavior indicated that the instance-based approaches are preferable to decision-tree induction and perceptrons as the learning component of a learning apprentice system if representation of the relevant characteristics of problem-solving states requires a large number of attributes, if attributes interact in a complex fashion, or if there are very few training instances. Conversely, decision-tree induction or perceptron learning is preferable if there are a small number of attributes and the attributes do not interact in a complex fashion unless there are very few training instances. When tested as the learning component of a learning apprentice system used by astronomers for scheduling astronomical observations, both CIBL and decision-tree induction rapidly achieved useful levels of accuracy in predicting the astronomers' preferences.