Dialectical Argumentation with Argumentation Schemes: An Approach to Legal Logic

This paper describes an approach to legal logic based on the formal analysis of argumentation schemes. Argumentation schemes a notion borrowed from the .eld of argumentation theory - are a kind of generalized rules of inference, in the sense that they express that given certain premises a particular conclusion can be drawn. However, argumentation schemes need not concern strict, abstract, necessarily valid patterns of reasoning, but can be defeasible, concrete and contingently valid, i.e., valid in certain contexts or under certain circumstances. A method is presented to analyze argumentation schemes and it is shown how argumentation schemes can be embedded in a formal model of dialectical argumentation.     

Formalizing value-guided argumentation for ethical systems design

The persuasiveness of an argument depends on the values promoted and demoted by the position defended. This idea, inspired by Perelman’s work on argumentation, has become a prominent theme in artificial intelligence research on argumentation since the work by Hafner and Berman on teleological reasoning in the law, and was further developed by Bench-Capon in his value-based argumentation frameworks. One theme in the study of value-guided argumentation is the comparison of values. Formal models involving value comparison typically use either qualitative or quantitative primitives. In this paper, techniques connecting qualitative and quantitative primitives recently developed for evidential argumentation are applied to value-guided argumentation. By developing the theoretical understanding of intelligent systems guided by embedded values, the paper is a step towards ethical systems design, much needed in these days of ever more pervasive AI techniques.     

Subjective logic and arguing with evidence

This paper introduces a subjective logic based argumentation framework primarily targeted at evidential reasoning. The framework explicitly caters for argument schemes, accrual of arguments, and burden of proof; these concepts appear in many types of argument, and are particularly useful in dialogues revolving around evidential reasoning. The concept of a sensor is also useful in this domain, representing a source of evidence, and is incorporated in our framework. We show how the framework copes with a number of problems that existing frameworks have difficulty dealing with, and how it can be situated within a simple dialogue game. Finally, we examine reasoning machinery that enables an agent to decide what argument to advance with the goal of maximising its utility at the end of a dialogue.     

基于Web的群体决策研讨信息自主可视化初探

为了克服在群体决策研讨过程中存在的“信息过载”和“知识断层”以及单纯依赖文本记录方法等给群体交流造成的不便,改善沟通效果并最终有效地辅助决策,需要对研讨信息进行合理组织和表示。文章分析了群体研讨过程中的信息属性及其结构,提出了在研讨某一时点上的信息布局“树状结构”以及在整个研讨进程上的“网络结构”的信息自主可视化概念模型。最后,利用JAVA技术、WEB技术以及多媒体技术等设计并实现了一个简单的体现研讨信息自主可视化思想的平台。     

基于可信度的辩论模型及争议评价算法

辩论是智能主体间为了消除分歧的一种基于言语的交互行为.由于知识的局限性,争议以及争议内部的陈述通常存在不确定性,因此在对辩论进行建模时需要考虑不确定信息处理问题.提出一种基于可信度的辩论模型（CFA）,该模型将争议表示为由若干前提和一个结论组成的可废止规则,并用对话树描述辩论推演过程.为了表示不确定性推理,引入可信度模型,将争议前提的不确定性和争议之间的攻击强度统一用可信度因子表示.在此基础上,提出计算陈述可信度的争议评价算法,并通过设定可信度阈值确定陈述的可接受性,得出最终辩论结果.最后,用一个实例说明该方法的有效性.该模型可以有效处理不确定信息条件下辩论推理过程,其辩论算法建立在数值计算基础之上,所得出的可接受陈述集在给定可信度阈值条件下是唯一的,可以克服Dung 的抽象辩论框架中扩充语义的不足.     

On modelling non-probabilistic uncertainty in the likelihood ratio approach to evidential reasoning

When the likelihood ratio approach is employed for evidential reasoning in law, it is often necessary to employ subjective probabilities, which are probabilities derived from the opinions and judgement of a human (expert). At least three concerns arise from the use of subjective probabilities in legal applications. Firstly, human beliefs concerning probabilities can be vague, ambiguous and inaccurate. Secondly, the impact of this vagueness, ambiguity and inaccuracy on the outcome of a probabilistic analysis is not necessarily fully understood. Thirdly, the provenance of subjective probabilities and the associated potential sources of vagueness, ambiguity and inaccuracy tend to be poorly understood, making it difficult for the outcome of probabilistic reasoning to be explained and validated, which is crucial in legal applications. The former two concerns have been addressed by a wide body of research in AI. The latter, however, has received little attention. This paper presents a novel approach to employ argumentation to reason about probability distributions in probabilistic models. It introduces a range of argumentation schemes and corresponding sets of critical questions for the construction and validation of argument models that define sets of probability distributions. By means of an extended example, the paper demonstrates how the approach, argumentation schemes and critical questions can be employed for the development of models and their validation in legal applications of the likelihood ratio approach to evidential reasoning.     

Using argument schemes for hypothetical reasoning in law

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. The result is compared with Rissland’s (1989) analysis in terms of dimensions and Ashley’s (2008) analysis in terms of his process model of legal argument with hypotheticals.     

Towards a Formal Account of Reasoning about Evidence: Argumentation Schemes and Generalisations

This paper studies the modelling of legal reasoning about evidence within general theories of defeasible reasoning and argumentation. In particular, Wigmore's method for charting evidence and its use by modern legal evidence scholars is studied in order to give a formal underpinning in terms of logics for defeasible argumentation. Two notions turn out to be crucial, viz. argumentation schemes and empirical generalisations.     

A Reasoning Model Based on the Production of Acceptable Arguments

Argumentation is a reasoning model based on the construction of arguments and counter-arguments (or defeaters) followed by the selection of the most acceptable of them. In this paper, we refine the argumentation framework proposed by Dung by taking into account preference relations between arguments in order to integrate two complementary points of view on the concept of acceptability: acceptability based on the existence of direct counter-arguments and acceptability based on the existence of defenders. An argument is thus acceptable if it is preferred to its direct defeaters or if it is defended against its defeaters. This also refines previous works by Prakken and Sartor, by associating with each argument a notion of strength, while these authors embed preferences in the definition of the defeat relation. We propose a revised proof theory in terms of AND/OR trees, verifying if a given argument is acceptable, which better reflects the dialectical form of argumentation.     

John McCarthy's legacy

This special issue is dedicated to John McCarthy, founding father of Artificial Intelligence. It contains a collection of recent contributions to the field of knowledge representation and reasoning, a field that McCarthy founded and that has been a main focus of his research during the last half century. In this introductory article, we survey some of McCarthy's major contributions to the field of knowledge representation and reasoning, and situate the papers in this special issue in the context of McCarthy's previous work.     

Formalizing dialectical explanation support for argument-based reasoning in knowledge-based systems

The concept of explanation has received attention from different areas in Computer Science, particularly in the knowledge-based systems and expert systems communities. At the same time, argumentation has evolved as a new paradigm for conceptualizing commonsense reasoning, resulting in the formalization of different argumentation frameworks and the development of several real-world argument-based applications. Although the notions of explanation and argument for a claim share many common elements in knowledge-based systems their interrelationships have not yet been formally studied in the context of the current argumentation research in Artificial Intelligence. This article explores these ideas by providing a new perspective on how to formalize dialectical explanation support for argument-based reasoning. To do this, we propose a formalization of explanations for abstract argumentation frameworks with dialectical constraints where different emerging properties are studied and analyzed. As a concrete example of the formalism introduced we show how it can be fleshed out in an implemented rule-based argumentation system.     

Artificial argument assistants for defeasible argumentation

The present paper discusses experimental argument assistance tools. In contrast with automated reasoning tools, the objective is not to replace reasoning, but to guide the user's production of arguments. Two systems are presented, and based on . The focus is on defeasible argumentation with an eye on the law. Argument assistants for defeasible argumentation naturally correspond to a view of the application of law as dialectical theory construction. The experiments provide insights into the design of argument assistants, and show the pros and cons of different ways of representing argumentative data. The development of the argumentation theories underlying the systems has culminated in the logical system that formalizes the interpretation of prima facie justified assumptions. introduces an innovative use of conditionals expressing support and attack. This allows the expression of warrants for support and attack, making it a transparent and flexible system of defeasible argumentation.     

Dialectic proof procedures for assumption-based, admissible argumentation

We present a family of dialectic proof procedures for the admissibility semantics of assumption-based argumentation. These proof procedures are defined for any conventional logic formulated as a collection of inference rules and show how any such logic can be extended to a dialectic argumentation system.The proof procedures find a set of assumptions, to defend a given belief, by starting from an initial set of assumptions that supports an argument for the belief and adding defending assumptions incrementally to counter-attack all attacks.The proof procedures share the same notion of winning strategy for a dispute and differ only in the search strategy they use for finding it. The novelty of our approach lies mainly in its use of backward reasoning to construct arguments and potential arguments, and the fact that the proponent and opponent can attack one another before an argument is completed. The definition of winning strategy can be implemented directly as a non-deterministic program, whose search strategy implements the search for defences.     

Towards a Formal and Implemented Model of Argumentation Schemes in Agent Communication

Argumentation schemes are patterns of non-deductive reasoning that have been the focus of extended study in argumentation theory. They have also been identified in computational domains including multi-agent systems as holding the potential for significant improvements in reasoning and communication abilities. By focusing on models of natural language argumentation schemes, and then building formal systems from them, direct implementation in multi-agent environments becomes a possibility. The formal, representational and implementational details are presented here, along with results that demonstrate not only advantages of flexibility, scope, and knowledge sharing, but also of computational efficiency.     

Argument diagram extraction from evidential Bayesian networks

Bayesian networks (BN) and argumentation diagrams (AD) are two predominant approaches to legal evidential reasoning, that are often treated as alternatives to one another. This paper argues that they are, instead, complimentary and proposes the beginnings of a method to employ them in such a manner. The Bayesian approach tends to be used as a means to analyse the findings of forensic scientists. As such, it constitutes a means to perform evidential reasoning. The design of Bayesian networks that accurately and comprehensively represent the relationships between investigative hypotheses and evidence remains difficult and sometimes contentious, however. Argumentation diagrams are representations of reasoning, and are used as a means to scrutinise reasoning (among other applications). In evidential reasoning, they tend to be used to represent and scrutinise the way humans reason about evidence. This paper examines how argumentation diagrams can be used to scrutinise Bayesian evidential reasoning by developing a method to extract argument diagrams from BN.     

On Construction and Evaluation of Analogical Arguments for Persuasive Reasoning

ABSTRACT

Analogical reasoning is a complex process based on a comparison between two pairs of concepts or states of affairs (aka. the source and the target) for characterizing certain features from one to another. Arguments which employ this process to support their claims are called analogical arguments. Our goals are to study the structure and the computation for their defeasibility in light of the argumentation theory. Our proposed assumption-based argumentation with predicate similarity ABA^(p) framework can be seen as an extension of assumption-based argumentation framework (ABA), in which not only assumptions can be used but also similarity of predicates is used to support a claim. ABA ^(p) labels each argument tree with an analogical degree and different ways to aggregate numerical values are studied toward gullible/skeptical characteristics in agent reasoning. The acceptability of analogical arguments is evaluated w.r.t. the semantics of abstract argumentation. Finally, we demonstrate that ABA ^(p) captures the argumentation scheme for argument from analogy and provides an explanation when it is used for persuasion.     

Explanation and Argumentation Capabilities:Towards the Creation of More Persuasive Agents

During the past two decades many research teamshave worked on the enhancement of theexplanation capabilities of knowledge-basedsystems and decision support systems. Duringthe same period, other researchers have workedon the development of argumentative techniquesfor software systems. We think that it would beinteresting for the researchers belonging tothese different communities to share theirexperiences and to develop systems that takeadvantage of the advances gained in eachdomain.We start by reviewing the evolution ofexplanation systems from the simple reasoningtraces associated with early expert systems torecent research on interactive andcollaborative explanations. We then discuss thecharacteristics of critiquing systems that testthe credibility of the user's solution. Therest of the paper deals with the differentapplication domains that use argumentativetechniques. First, we discuss how argumentativereasoning can be captured by a generalstructure in which a given claim or conclusionis inferred from a set of data and how thisargument structure relates to pragmaticknowledge, explanation production and practicalreasoning. We discuss the role of argument indefeasible reasoning and present some works inthe new field of computer-mediated defeasibleargumentation. We review different applicationdomains such as computer-mediatedcommunication, design rationale, crisismanagement and knowledge management, in whichargumentation support tools are used. Wedescribe models in which arguments areassociated to mental attitudes such as goals,plans and beliefs. We present recent advancesin the application of argumentative techniquesto multi-agent systems. Finally, we proposeresearch perspectives for the integration ofexplanation and argumentation capabilities inknowledge-based systems and make suggestionsfor enhancing the argumentation and persuasioncapabilities of software agents.     

There's Nothing Like a Good Argument ...

Since computing pioneers Vannevar Bush and Doug Engelbart envisioned computational support for argumentation, many have pursued the exciting vision of tools for capturing and augmenting collective reasoning. Designers would be able to capture their deliberations on the fly during design sessions, with intuitive visualizations assisting participatory analysis by diverse stakeholders. These traces would later help recover design rationale. When managing requirements, we can think of argument schemes as reusable patterns for tightening up deliberations. Project reviews are an obvious candidate, where decisions must be justified, often to be signed off, and resources committed. As meeting capture becomes a practical reality, we have the basis for requirements platforms that provide new forms of multimedia requirements and rationale traceability.