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  1. An ontology in owl for legal case-based reasoning.Adam Wyner - 2008 - Artificial Intelligence and Law 16 (4):361-387.
    The paper gives ontologies in the Web Ontology Language (OWL) for Legal Case-based Reasoning (LCBR) systems, giving explicit, formal, and general specifications of a conceptualisation LCBR. Ontologies for different systems allows comparison and contrast between them. OWL ontologies are standardised, machine-readable formats that support automated processing with Semantic Web applications. Intermediate concepts, concepts between base-level concepts and higher level concepts, are central in LCBR. The main issues and their relevance to ontological reasoning and to LCBR are discussed. Two LCBR systems (...)
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  • Padua: A protocol for argumentation dialogue using association rules. [REVIEW]Maya Wardeh, Trevor Bench-Capon & Frans Coenen - 2009 - Artificial Intelligence and Law 17 (3):183-215.
    We describe PADUA, a protocol designed to support two agents debating a classification by offering arguments based on association rules mined from individual datasets. We motivate the style of argumentation supported by PADUA, and describe the protocol. We discuss the strategies and tactics that can be employed by agents participating in a PADUA dialogue. PADUA is applied to a typical problem in the classification of routine claims for a hypothetical welfare benefit. We particularly address the problems that arise from the (...)
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  • Thirty years of artificial intelligence and law: the third decade.Serena Villata, Michal Araszkiewicz, Kevin Ashley, Trevor Bench-Capon, L. Karl Branting, Jack G. Conrad & Adam Wyner - 2022 - Artificial Intelligence and Law 30 (4):561-591.
    The first issue of Artificial Intelligence and Law journal was published in 1992. This paper offers some commentaries on papers drawn from the Journal’s third decade. They indicate a major shift within Artificial Intelligence, both generally and in AI and Law: away from symbolic techniques to those based on Machine Learning approaches, especially those based on Natural Language texts rather than feature sets. Eight papers are discussed: two concern the management and use of documents available on the World Wide Web, (...)
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  • On searching explanatory argumentation graphs.Régis Riveret - 2020 - Journal of Applied Non-Classical Logics 30 (2):123-192.
    Cases or examples can be often explained by the interplay of arguments in favour or against their outcomes. This paper addresses the problem of finding explanations for a collection of cases where an explanation is a labelled argumentation graph consistent with the cases, and a case is represented as a statement labelling. The focus is on semi-abstract argumentation graphs specifying attack and subargument relations between arguments, along with particular complete argument labellings taken from probabilistic argumentation where arguments can be excluded. (...)
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  • On balance.Marc Lauritsen - 2015 - Artificial Intelligence and Law 23 (1):23-42.
    In the course of legal reasoning—whether for purposes of deciding an issue, justifying a decision, predicting how an issue will be decided, or arguing for how it should be decided—one often is required to reach conclusions based on a balance of reasons that is not straightforwardly reducible to the application of rules. Recent AI and Law work has modeled reason-balancing, both within and across cases, with set-theoretic and rule- or value-ordering approaches. This article explores a way to model balancing in (...)
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  • A factor-based definition of precedential constraint.John F. Horty & Trevor J. M. Bench-Capon - 2012 - Artificial Intelligence and Law 20 (2):181-214.
    This paper describes one way in which a precise reason model of precedent could be developed, based on the general idea that courts are constrained to reach a decision that is consistent with the assessment of the balance of reasons made in relevant earlier decisions. The account provided here has the additional advantage of showing how this reason model can be reconciled with the traditional idea that precedential constraint involves rules, as long as these rules are taken to be defeasible. (...)
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  • Resolving counterintuitive consequences in law using legal debugging.Wachara Fungwacharakorn, Kanae Tsushima & Ken Satoh - 2021 - Artificial Intelligence and Law 29 (4):541-557.
    There are cases in which the literal interpretation of statutes may lead to counterintuitive consequences. When such cases go to high courts, judges may handle these counterintuitive consequences by identifying problematic rule conditions. Given that the law consists of a large number of rule conditions, it is demanding and exhaustive to figure out which condition is problematic. For solving this problem, our work aims to assist judges in civil law systems to resolve counterintuitive consequences using logic program representation of statutes (...)
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  • HYPO's legacy: introduction to the virtual special issue.T. J. M. Bench-Capon - 2017 - Artificial Intelligence and Law 25 (2):205-250.
    This paper is an introduction to a virtual special issue of AI and Law exploring the legacy of the influential HYPO system of Rissland and Ashley. The papers included are: Arguments and cases: An inevitable intertwining, BankXX: Supporting legal arguments through heuristic retrieval, Modelling reasoning with precedents in a formal dialogue Game, A note on dimensions and factors, An empirical investigation of reasoning with legal cases through theory construction and application, Automatically classifying case texts and predicting outcomes, A factor-based definition (...)
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  • A history of AI and Law in 50 papers: 25 years of the international conference on AI and Law. [REVIEW]Trevor Bench-Capon, Michał Araszkiewicz, Kevin Ashley, Katie Atkinson, Floris Bex, Filipe Borges, Daniele Bourcier, Paul Bourgine, Jack G. Conrad, Enrico Francesconi, Thomas F. Gordon, Guido Governatori, Jochen L. Leidner, David D. Lewis, Ronald P. Loui, L. Thorne McCarty, Henry Prakken, Frank Schilder, Erich Schweighofer, Paul Thompson, Alex Tyrrell, Bart Verheij, Douglas N. Walton & Adam Z. Wyner - 2012 - Artificial Intelligence and Law 20 (3):215-319.
    We provide a retrospective of 25 years of the International Conference on AI and Law, which was first held in 1987. Fifty papers have been selected from the thirteen conferences and each of them is described in a short subsection individually written by one of the 24 authors. These subsections attempt to place the paper discussed in the context of the development of AI and Law, while often offering some personal reactions and reflections. As a whole, the subsections build into (...)
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  • Explanation in AI and law: Past, present and future.Katie Atkinson, Trevor Bench-Capon & Danushka Bollegala - 2020 - Artificial Intelligence 289 (C):103387.
  • Accommodating change.Latifa Al-Abdulkarim, Katie Atkinson & Trevor Bench-Capon - 2016 - Artificial Intelligence and Law 24 (4):409-427.
    The third of Berman and Hafner’s early nineties papers on reasoning with legal cases concerned temporal context, in particular the evolution of case law doctrine over time in response to new cases and against a changing background of social values and purposes. In this paper we consider the ways in which changes in case law doctrine can be accommodated in a recently proposed methodology for encapsulating case law theories, and relate these changes the sources of change identified by Berman and (...)
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  • A methodology for designing systems to reason with legal cases using Abstract Dialectical Frameworks.Latifa Al-Abdulkarim, Katie Atkinson & Trevor Bench-Capon - 2016 - Artificial Intelligence and Law 24 (1):1-49.
    This paper presents a methodology to design and implement programs intended to decide cases, described as sets of factors, according to a theory of a particular domain based on a set of precedent cases relating to that domain. We useDialectical Frameworks, a recent development in AI knowledge representation, as the central feature of our design method. ADFs will play a role akin to that played by Entity–Relationship models in the design of database systems. First, we explain how the factor hierarchy (...)
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