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.     

Modular Representation of Agent Interaction Rules through Argumentation

Communication between agents needs to be flexible enough to encompass together a variety of different aspects such as, conformance to society protocols, private tactics of the individual agents, strategies that reflect different classes of agent types (or personal attitudes) and adaptability to the particular external circumstances at the time when the communication takes place. In this paper, we propose an argument-based framework for representing communication theories of agents that can take into account in a uniform way these different aspects. We show how this approach can be used to realize existing types of dialogue strategies and society protocols in a way that facilitates their modular development and extension to make them more flexible in handling different or special circumstances.     

Legal Case-based Reasoning as Practical Reasoning

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 the two, with conclusions at one level forming premises at the next. We begin by summarising our general approach, which uses instantiations of an argumentation scheme to provide presumptive justifications for actions, and critical questions to identify arguments which attack these justifications. These arguments and attacks are organised into argumentation frameworks to identify the status of individual arguments. We then discuss the levels of reasoning that occur in this reconstruction and the properties and significance of each of these levels. We illustrate the different levels with short examples and also include a discussion of the role of precedents within these levels of reasoning.     

真稳定类与辩论语义

稳定类语义和辨论语义是逻辑程序语义学研究方面突出的两种语义统一框架理论,它们统一了一些很重要的语义,因为它们有着不同的直觉和概念,它们的相互关系并不清楚。我们在它们最基础的概念层次上证明了,常规逻辑程序句法下二者是等价的,它们有着同样的语义统一能力。基于这个结果,我们为有前途的辨论语义指明一种迭代构造的方法。     

Argumentation in disjunctive logic programming

Ｉｎｏｒｄｅｒｔｏｅｎａｂｌｅｌｏｇｉｃｐｒｏｇｒａｍｍｉｎｇｌａｎｇｕａｇｅｓｔｏｂｅｃｏｍｅｓａｔｉｓｆａｃｔｏｒｙｔｏｏｌｓｆｏｒｋｎｏｗｌｅｄｇｅｒｅｐｒｅｓｅｎｔａｔｉｏｎ,ｍａｎｙｅｆｆｏｒｔｓｈａｖｅｂｅｅｎｍａｄｅｔｏｄｅｆｉｎｅｖａｒｉｏｕｓｓｕｉｔａｂｌｅｓｅｍａｎｔｉｃｓｆｏｒｄｉｓｊｕｎｃｔｉｖｅｌｏｇｉｃｐｒｏｇｒａｍｍｉｎｇ(ＤＬＰ)[1].Ｈｏｗｅｖｅｒ,ｔｈｅｓｅａｐｐｒｏａｃｈｅｓｂｅａｒｍａｎｙｄｒａｗｂａｃｋｓ,ｉｎｃｌｕｄｉｎｇｕｎｉｎｔｕｉｔｉｖｉｔｙ,ｉｎｃｏｍ…     

清江发电厂一期工程水资源论证简述

结合重安江流域水资源量、水质及开发利用现状,对取用水合理性、取水的影响、退水的影响以及水资源保护措施等内容进行了详细的分析.论证结果表明,清江发电厂一期工程重安江取水方案的取水量、水质等能得到保障,取退水不会对生态环境和第三者产生较大影响,工程的建设将极大提高项目区工业水平和人民生活水平,建议尽快实施.     

一种扩展双极辩论模型

提出一种扩展双极辩论模型EBAF(extended bipolar argumentation framework).该模型不仅包括攻击和支援两种独立的语义关系,还允许攻击和支援的递归交互,即对攻击和支援关系进行攻击或支援,且递归次数不受限制.围绕该模型的可接受集合的确定问题,首先将该模型中的攻击和支援关系进行分离,得到攻击辩论框架和支援辩论框架;然后将攻击关系和支援关系作为实体,把递归攻击和递归支援转化为关系视角下的攻击和支援.在此基础上,定义了EBAF的基本语义概念和可接受集合,并给出了可接受集合的确定算法.最后将EBAF与其他相关辩论模型进行了比较.     

A dialectical model of assessing conflicting arguments in legal reasoning

Inspired by legal reasoning, this paper presents a formal framework for assessing conflicting arguments. Its use is illustrated with applications to realistic legal examples, and the potential for implementation is discussed. The framework has the form of a logical system for defeasible argumentation. Its language, which is of a logic-programming-like nature, has both weak and explicit negation, and conflicts between arguments are decided with the help of priorities on the rules. An important feature of the system is that these priorities are not fixed, but are themselves defeasibly derived as conclusions within the system. Thus debates on the choice between conflicting arguments can also be modelled.The proof theory of the system is stated in dialectical style, where a proof takes the form of a dialogue between a proponent and an opponent of an argument. An argument is shown to be justified if the proponent can make the opponent run out of moves in whatever way the opponent attacks. Despite this dialectical form, the system reflects a declarative, or relational approach to modelling legal argument. A basic assumption of this paper is that this approach complements two other lines of research in AI and Law, investigations of precedent-based reasoning and the development of procedural, or dialectical models of legal argument.Supported by a research fellowship of the Royal Netherlands Academy of Arts and Sciences, and by Esprit WG 8319 Modelage.     

Abstract argumentation

In this paper we explore the thesis that the role of argumentation in practical reasoning in general and legal reasoning in particular is to justify the use of defeasible rules to derive a conclusion in preference to the use of other defeasible rules to derive a conflicting conclusion. The defeasibility of rules is expressed by means of non-provability claims as additional conditions of the rules.We outline an abstract approach to defeasible reasoning and argumentation which includes many existing formalisms, including default logic, extended logic programming, non-monotonic modal logic and auto-epistemic logic, as special cases. We show, in particular, that the admissibility semantics for all these formalisms has a natural argumentation-theoretic interpretation and proof procedure, which seem to correspond well with informal argumentation.In the admissibility semantics there is only one way for one argument to attack another, namely by undermining one of its non-provability claims. In this paper, we show how other kinds of attack between arguments, specifically how rebuttal and priority attacks, can be reduced to the undermining of non-provability claims.     

基于YOLO-tiny-RFB模型的电站旋钮开关状态识别

针对多类别目标检测在特定场景中数据样本有限的情况,为进一步提高机器人系统中轻量级神经网络对小型物体识别的准确率和稳定性,提出了一种基于机器人操作系统（ROS）的目标状态识别模块。首先,考虑到嵌入式设备的算力限制,目标识别模型采用轻量级的网络YOLO-tiny作为主要架构,并在YOLO-tiny中引入RFB,提出了YOLO-tiny-RFB模型。随后,基于MobileNet对旋钮开关的多种状态实现精准分类。最后,设计数据关联规则,通过图像配准及交并比（IOU）计算等算法使识别模块完成同一场景多次识别结果的融合,从而使用户能够对不同时刻各表计的状态进行追踪。实验结果表明,相较于YOLO-tiny,YOLO-tiny-RFB模型在少量增加模型计算量的情况下,在构建的电站仪器识别数据集上,其目标识别平均精度均值（mAP）提升了17.9%,达到了82.4%。在旋钮数据分布极端不均衡的情况下,通过引入多种数据增广方法使模型的平均准确率达到了90.7%。所提出的目标检测模块和状态识别网络模型能够有效、准确地完成各类仪器的状态识别,同时能够对仪器状态的识别结果在时间跨度上进行融合。