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Trevor Bench-Capon [18]Trevor J. M. Bench-Capon [8]
  1. Katie Atkinson, Trevor Bench-Capon & Douglas Walton (2013). Distinctive Features of Persuasion and Deliberation Dialogues. Philosophical Explorations 4 (2):105-127.
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  2. Rolando Medellin-Gasque, Katie Atkinson, Trevor Bench-Capon & Peter McBurney (2013). Strategies for Question Selection in Argumentative Dialogues About Plans. Philosophical Explorations 4 (2):151-179.
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  3. 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). A History of AI and Law in 50 Papers: 25 Years of the International Conference on AI and Law. [REVIEW] 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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  4. John F. Horty & Trevor J. M. Bench-Capon (2012). A Factor-Based Definition of Precedential Constraint. 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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  5. Maya Wardeh, Trevor Bench-Capon & Frans Coenen (2011). Arguing From Experience Using Multiple Groups of Agents. Argument and Computation 2 (1):51 - 76.
    A framework to support ?Arguing from Experience? using groups of collaborating agents (termed participant agents/players) is described. The framework is an extension of the PISA multi-party arguing from experience framework. The original version of PISA allowed n participants to promote n goals (one each) for a given example. The described extension of PISA allows individuals with the same goals to pool their resources by forming ?groups?. The framework is fully described and its effectiveness illustrated using a number of classification scenarios. (...)
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  6. Trevor Bench-Capon & Henry Prakken (2010). Using Argument Schemes for Hypothetical Reasoning in Law. Artificial Intelligence and Law 18 (2):153-174.
    This paper studies the use of hypothetical and value-based reasoning in US Supreme-Court cases concerning the United States Fourth Amendment. Drawing upon formal AI & Law models of legal argument a semi-formal reconstruction is given of parts of the Carney case, which has been studied previously in AI & law research on case-based reasoning. As part of the reconstruction, a semi-formal proposal is made for extending the formal AI & Law models with forms of metalevel reasoning in several argument schemes. (...)
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  7. Floris Bex, Trevor Bench-Capon & Katie Atkinson (2009). Did He Jump or Was He Pushed? Artificial Intelligence and Law 17 (2):79-99.
    In this paper, we present a particular role for abductive reasoning in law by applying it in the context of an argumentation scheme for practical reasoning. We present a particular scheme, based on an established scheme for practical reasoning, that can be used to reason abductively about how an agent might have acted to reach a particular scenario, and the motivations for doing so. Plausibility here depends on a satisfactory explanation of why this particular agent followed these motivations in the (...)
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  8. Maya Wardeh, Trevor Bench-Capon & Frans Coenen (2009). Padua: A Protocol for Argumentation Dialogue Using Association Rules. [REVIEW] 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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  9. Katie Atkinson & Trevor J. M. Bench-Capon (2007). Practical Reasoning as Presumptive Argumentation Using Action-Based Alternating Transition Systems. Artificial Intelligence 171 (10-15):855-874.
    In this paper we describe an approach to practical reasoning, reasoning about what it is best for a particular agent to do in a given situation, based on presumptive justifications of action through the instantiation of an argument scheme, which is then subject to examination through a series of critical questions. We identify three particular aspects of practical reasoning which distinguish it from theoretical reasoning. We next provide an argument scheme and an associated set of critical questions which is able (...)
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  10. Katie Atkinson, Trevor Bench-Capon & Peter McBurney (2006). Computational Representation of Practical Argument. Synthese 152 (2):157 - 206.
    In this paper we consider persuasion in the context of practical reasoning, and discuss the problems associated with construing reasoning about actions in a manner similar to reasoning about beliefs. We propose a perspective on practical reasoning as presumptive justification of a course of action, along with critical questions of this justification, building on the account of Walton. From this perspective, we articulate an interaction protocol, which we call PARMA, for dialogues over proposed actions based on this theory. We outline (...)
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  11. Katie Atkinson, Trevor Bench-Capon & Peter McBurney (2006). PARMENIDES: Facilitating Deliberation in Democracies. [REVIEW] Artificial Intelligence and Law 14 (4):261-275.
    Governments and other groups interested in the views of citizens require the means to present justifications of proposed actions, and the means to solicit public opinion concerning these justifications. Although Internet technologies provide the means for such dialogues, system designers usually face a choice between allowing unstructured dialogues, through, for example, bulletin boards, or requiring citizens to acquire a knowledge of some argumentation schema or theory, as in, for example, ZENO. Both of these options present usability problems. In this paper, (...)
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  12. Katie Atkinson & Trevor Bench-Capon (2005). Legal Case-Based Reasoning as Practical Reasoning. Artificial Intelligence and Law 13 (1):93-131.
    In this paper we apply a general account of practical reasoning to arguing about legal cases. In particular, we provide a reconstruction of the reasoning of the majority and dissenting opinions for a particular well-known case from property law. This is done through the use of Belief-Desire-Intention (BDI) agents to replicate the contrasting views involved in the actual decision. This reconstruction suggests that the reasoning involved can be separated into three distinct levels: factual and normative levels and a level connecting (...)
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  13. Trevor J. M. Bench-Capon & Paul E. Dunne (2005). Argumentation in AI and Law: Editors' Introduction. [REVIEW] Artificial Intelligence and Law 13 (1):1-8.
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  14. Alison Chorley & Trevor Bench-Capon (2005). An Empirical Investigation of Reasoning with Legal Cases Through Theory Construction and Application. Artificial Intelligence and Law 13 (3-4):323-371.
    In recent years several proposals to view reasoning with legal cases as theory construction have been advanced. The most detailed of these is that of Bench-Capon and Sartor, which uses facts, rules, values and preferences to build a theory designed to explain the decisions in a set of cases. In this paper we describe CATE (CAse Theory Editor), a tool intended to support the construction of theories as described by Bench-Capon and Sartor, and which produces executable code corresponding to a (...)
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  15. Alison Chorley & Trevor Bench-Capon (2005). Agatha: Using Heuristic Search to Automate the Construction of Case Law Theories. [REVIEW] Artificial Intelligence and Law 13 (1):9-51.
    In this paper we describe AGATHA, a program designed to automate the process of theory construction in case based domains. Given a seed case and a number of precedent cases, the program uses a set of argument moves to generate a search space for a dialogue between the parties to the dispute. Each move is associated with a set of theory constructors, and thus each point in the space can be associated with a theory intended to explain the seed case (...)
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  16. Martin Možina, Jure Žabkar, Trevor Bench-Capon & Ivan Bratko (2005). Argument Based Machine Learning Applied to Law. Artificial Intelligence and Law 13 (1):53-73.
    In this paper we discuss the application of a new machine learning approach – Argument Based Machine Learning – to the legal domain. An experiment using a dataset which has also been used in previous experiments with other learning techniques is described, and comparison with previous experiments made. We also tested this method for its robustness to noise in learning data. Argumentation based machine learning is particularly suited to the legal domain as it makes use of the justifications of decisions (...)
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  17. Trevor Bench-Capon (2004). Book Review: Bram Roth, Case-Based Reasoning in the Law: A Formal Theory of Reasoning by Case Comparison. Ph. D. Thesis, the University of Maastricht, 2003. 181 Pp. [REVIEW] Artificial Intelligence and Law 12 (3):227-229.
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  18. Trevor J. M. Bench-Capon (2003). Try to See It My Way: Modelling Persuasion in Legal Discourse. [REVIEW] Artificial Intelligence and Law 11 (4):271-287.
    In this paper I argue that to explain and resolve some kinds of disagreement we need to go beyond what logic alone can provide. In particular, following Perelman, I argue that we need to consider how arguments are ascribed different strengths by different audiences, according to how accepting these arguments promotes values favoured by the audience to which they are addressed. I show how we can extend the standard framework for modelling argumentation systems to allow different audiences to be represented. (...)
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  19. Trevor J. M. Bench-Capon & Giovanni Sartor (2003). A Model of Legal Reasoning with Cases Incorporating Theories and Values. Artificial Intelligence 150 (1-2):97-143.
    Reasoning with cases has been a primary focus of those working in AI and law who have attempted to model legal reasoning. In this paper we put forward a formal model of reasoning with cases which captures many of the insights from that previous work. We begin by stating our view of reasoning with cases as a process of constructing, evaluating and applying a theory. Central to our model is a view of the relationship between cases, rules based on cases, (...)
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  20. Trevor Bench-Capon (2001). Luuk Matthijssen: Interfacing Between Lawyers and Computers: An Architecture for Knowledge-Based Interfaces to Legal Databases. [REVIEW] Artificial Intelligence and Law 8 (4):349-352.
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  21. Trevor J. M. Bench-Capon (2001). George C. Christie, the Notion of an Ideal Audience in Legal Argument. Artificial Intelligence and Law 9 (1):59-71.
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  22. Trevor J. M. Bench-Capon (1999). Zenon Bankowski, Ian White, and Ulrike Hahn, Informatics and the Foundations of Legal Reasoning. Artificial Intelligence and Law 7 (4):363-365.
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  23. Pepijn R. S. Visser & Trevor J. M. Bench-Capon (1998). A Comparison of Four Ontologies for the Design of Legal Knowledge Systems. Artificial Intelligence and Law 6 (1):27-57.
    There is a growing interest in how people conceptualise the legal domain for the purpose of legal knowledge systems. In this paper we discuss four such conceptualisations (referred to as ontologies): McCarty's language for legal discourse, Stamper's norma formalism, Valente's functional ontology of law, and the ontology of Van Kralingen and Visser. We present criteria for a comparison of the ontologies and discuss the strengths and weaknesses of the ontologies in relation to these criteria. Moreover, we critically review the criteria.
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  24. Trevor Bench-Capon (1997). Argument in Artificial Intelligence and Law. Artificial Intelligence and Law 5 (4):249-261.
    In this paper I shall discuss the notion of argument, and the importanceof argument in AI and Law. I shall distinguish four areas where argument hasbeen applied: in modelling legal reasoning based on cases; in thepresentation and explanation of results from a rule based legal informationsystem; in the resolution of normative conflict and problems ofnon-monotonicity; and as a basis for dialogue games to support the modellingof the process of argument. The study of argument is held to offer prospectsof real progress (...)
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  25. Pepijn Visser, Trevor Bench-Capon & Jaap van den Herik (1997). A Method for Conceptualising Legal Domains. An Example From the Dutch Unemployment Benefits Act. Artificial Intelligence and Law 5 (3):207-242.
    There has been much talk of the need to build intermediate models of the expertise required preparatory to constructing a knowledge-based system in the legal domain. Such models offer advantages for verification, validation, maintenance and reuse. As yet, however, few such models have been reported at a useful level of detail. In this paper we describe a method for conceptualising legal domains as well as its application to a substantial fragment of the Dutch Unemployment Benefits Act (DUBA).We first discuss the (...)
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  26. Paul Soper & Trevor Bench-Capon (1993). Coupling Hypertext and Knowledge Based Systems: Two Applications in the Legal Domain. [REVIEW] Artificial Intelligence and Law 2 (4):293-314.
    Hypertext and knowledge based systems can be viewed as complementary technologies, which if combined into a composite system may be able to yield a whole which is greater than the sum of the parts. To gain the maximum benefits, however, we need to think about how to harness this potential synergy. This will mean devising new styles of system, rather than merely seeking to enhance the old models.In this paper we describe our model for coupling hypertext and a knowledge based (...)
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