On Resolving Conflicts Between Arguments

Argument systems are based on the idea that one can construct arguments for propositions—structured reasons justifying the belief in a proposition. Using defeasible rules, arguments need not be valid in all circumstances, therefore, it might be possible to construct an argument for a proposition as well as its negation. When arguments support conflicting propositions, one of the arguments must be defeated, which raises the question of which (sub‐) arguments can be subject to defeat. In legal argumentation, metarules determine the valid arguments by considering the last defeasible rule of each argument involved in a conflict. Since it is easier to evaluate arguments using their last rules, can a conflict be resolved by considering only the last defeasible rules of the arguments involved? We propose a new argument system where, instead of deriving a defeat relation between arguments, arguments for the defeat of defeasible rules are constructed. This system allows us to determine a set of valid (undefeated) arguments in linear time using an algorithm based on a JTMS, allows conflicts to be resolved using only the last rules of the arguments, allows us to establish a relation with Default Logic, and allows closure properties such as cumulativity to be proved. We propose an extension of the argument system based on a proposal for reasoning by cases in default logic.     

Self-defeating arguments

An argument is self-defeating when it contains defeaters for some of its own defeasible lines. It is shown that the obvious rules for defeat among arguments do not handle self-defeating arguments correctly. It turns out that they constitute a pervasive phenomenon that threatens to cripple defeasible reasoning, leading to almost all defeasible reasoning being defeated by unexpected interactions with self-defeating arguments. This leads to some important changes in the general theory of defeasible reasoning.     

Defeat among arguments: a system of defeasible inference

This paper presents a system of nonmonotonic reasoning with defeasible rules. The advantage of such a system is that many multiple extension problems can be solved without additional explicit knowledge; ordering competing extensions can be done in a natural and defeasible way, via syntactic considerations. The objectives closely resemble Poole's objectives.
But the logic is different from Poole's. The most important difference is that this system allows the kind of chaining that many other nonmonotonic systems allow. Also, the form in which the inference system is presented is quite unusual. It mimics an established system of inductive logic, and it treats defeat in the way of the epistemologist-philosophers.
The contributions are both of content and of form: (content) the kinds of defeat that are considered, and (form) the way in which defeat is treated in the rules of inference.     

Reconstructing Popov v. Hayashi in a framework for argumentation with structured arguments and Dungean semantics

In this article the argumentation structure of the court??s decision in the Popov v. Hayashi case is formalised in Prakken??s (Argument Comput 1:93?C124; 2010) abstract framework for argument-based inference with structured arguments. In this framework, arguments are inference trees formed by applying two kinds of inference rules, strict and defeasible rules. Arguments can be attacked in three ways: attacking a premise, attacking a conclusion and attacking an inference. To resolve such conflicts, preferences may be used, which leads to three corresponding kinds of defeat, after which Dung??s (Artif Intell 77:321?C357; 1995) abstract acceptability semantics can be used to evaluate the arguments. In the present paper the abstract framework is instantiated with strict inference rules corresponding to first-order logic and with defeasible inference rules for defeasible modus ponens and various argument schemes. The main techniques used in the formal reconstruction of the case are rule-exception structures and arguments about rule validity. Arguments about socio-legal values and the use of precedent cases are reduced to arguments about rule validity. The tree structure of arguments, with explicit subargument relations between arguments, is used to capture the dependency relations between the elements of the court??s decision.     

A metabolism–repair theory of by-products and side-effects

The plurality of process outputs is a genericity of Nature. In this paper, Natural Law receives a new mathematical formulation founded on two axioms: ‘Everything is a set.’ and ‘Every process is a set-valued mapping.’ I present a brief introduction to the algebraic theory of set-valued mappings, which culminates in two particular morphisms: the metabolism bundle and the imminence mapping. These are relations defined on the collection of processes of a natural system, and serve to characterize material entailment and functional entailment. Generalized metabolism is material entailment of (by-)products, and generalized repair is functional entailment of (side-)effects. Metabolism–Repair networks, hence equipped with set-valued processors, expand their role from models of biological entities to generic models of all natural systems.     

谈判代理开发典型案例的研究

本文通过两个典型案例说明一个灵活有效的谈判代理开发方法.使用状态图和可废止逻辑规则说明谈判策略,将其动态地插入到一个集状态解释器和可废止逻辑推理引擎于一体的代理壳中,就可以获得所需要的自动谈判代理.由于状态图和可废止逻辑有很强的适应性,当应用环境发生变化时,只需调整其标准和规则的集合.     

中文文本蕴含类型及语块识别方法研究

文本蕴含识别（RTE）是判断两个句子语义是否具有蕴含关系的任务.近年来英文蕴含识别研究取得了较大发展,但主要是以类型判断为主,在数据中精确定位蕴含语块的研究比较少,蕴含类型识别的解释性较低.从中文文本蕴含识别（CNLI）数据中挑选12 000个中文蕴含句对,人工标注引起蕴含现象的语块,结合语块的语言学特征分析归纳了7种具体的蕴含类型.在此基础上,将中文蕴含识别任务转化为7分类的蕴含类型识别和蕴含语块边界-类型识别任务,在深度学习模型上达到69.19%和62.09%的准确率.实验结果表明,所提出的方法可以有效发现中文蕴含语块边界及与之对应的蕴含类型,为下一步研究提供了可靠的基准方法.     

Abstract argumentation systems

In this paper, we develop a theory of abstract argumentation systems. An abstract argumentation system is a collection of “defeasible proofs”, called arguments, that is partially ordered by a relation expressing the difference in conclusive force. The prefix “abstract” indicates that the theory is concerned neither with a specification of the underlying language, nor with the development of a subtheory that explains the partial order. An unstructured language, without logical connectives such as negation, makes arguments not (pairwise) inconsistent, but (groupwise) incompatible. Incompatibility and difference in conclusive force cause defeat among arguments. The aim of the theory is to find out which arguments eventually emerge undefeated. These arguments are considered to be in force. Several results are established. The main result is that arguments that are in force are precisely those that are in the limit of a so-called complete argumentation sequence.     

Program verification,defeasible reasoning,and two views of computer science

In this paper I attempt to cast the current program verification debate within a more general perspective on the methodologies and goals of computer science. I show, first, how any method involved in demonstrating the correctness of a physically executing computer program, whether by testing or formal verification, involves reasoning that is defeasible in nature. Then, through a delineation of the senses in which programs can be run as tests, I show that the activities of testing and formal verification do not necessarily share the same goals and thus do not always constitute alternatives. The testing of a program is not always intended to demonstrate a program's correctness. Testing may seek to accept or reject nonprograms including algorithms, specifications, and hypotheses regarding phenomena. The relationship between these kinds of testing and formal verification is couched in a more fundamental relationship between two views of computer science, one properly containing the other.     

Modelling defeasible and prioritized support in bipolar argumentation

Cayrol and Lagasquie-Schiex introduce bipolar argumentation frameworks by introducing a second relation on the arguments for representing the support among them. The main drawback of their approach is that they cannot encode defeasible support, for instance they cannot model an attack towards a support relation. In this paper, we introduce a way to model defeasible support in bipolar argumentation frameworks. We use the methodology of meta-argumentation in which Dung??s theory is used to reason about itself. Dung??s well-known admissibility semantics can be used on this meta-argumentation framework to compute the acceptable arguments, and all properties of Dung??s classical theory are preserved. Moreover, we show how different contexts can lead to the alternative strengthening of the support relation over the attack relation, and converse. Finally, we present two applications of our methodology for modeling support, the case of arguments provided with an internal structure and the case of abstract dialectical frameworks.     

基于知识话题模型的文本蕴涵识别

该文分析了现有基于分类策略的文本蕴涵识别方法的问题,并提出了一种基于知识话题模型的文本蕴涵分类识别方法。 其假设是: 文本可看作是语义关系的组合,这些语义关系构成若干话题;若即若文本T蕴涵假设H,说明 T 和 H 具有相似的话题分布,反之说明T 和 H 不具有相似的话题分布。基于此,我们将 T 和 H 的蕴涵识别问题转化为相关话题的生成过程,同时将文本推理知识融入到抽样过程,由此建立一个面向文本蕴涵识别的话题模型。实验结果表明基于知识话题模型在一定程度上改进了文本蕴涵识别系统的性能。     

A formal approach to negotiating agents development

This paper presents a formal and executable approach to capture the behaviour of parties involved in a negotiation. A party is modeled as a negotiating agent composed of a communication module, a control module, a reasoning module, and a knowledge base. The control module is expressed as a statechart, and the reasoning module as a defeasible logic program. A strategy specification therefore consists of a statechart, a set of defeasible rules, and a set of initial facts. Such a specification can be dynamically plugged into an agent shell incorporating a statechart interpreter and a defeasible logic inference engine, in order to yield an agent capable of participating in a given type of negotiations. The choice of statecharts and defeasible logic with respect to other formalisms is justified against a set of desirable criteria, and their suitability is illustrated through concrete examples of bidding and multi-lateral bargaining scenarios.     

A modular translation from defeasible nets to defeasible logics

Abstract

The sceptical inheritance nets introduced in Horty et al. [Proceedings of AAAI-87 (1987):358-363] are translated into a version of Nute's defeasible logic. Moreover this translation is modular in the sense of Thomason and Horty [Non-Monotonic Reasoning. Springer-Verlag (1989):234]. Apart from the importance of relating two nonmonotonic reasoning formalisms, this result shows that the reasoning mechanisms underlying defeasible logic and defeasible nets are the same. Yet they were invented independently and set in totally different contexts. This is perhaps some evidence that the underlying nonmonotonic reasoning mechanism is mainly correct. We also observe that since defeasible logics can contain both absolute and defeasible rules, they provided a uniform setting for considering nets which contain both strict and defeasible arcs.     

An argument‐based decision support system for assessing natural language usage on the basis of the web corpus

The last decade bears witness to an exponential growth in the use of the World Wide Web. As a result, a huge number of documents are accessible online through search engines, whose pattern‐matching capabilities have turned out to be useful for mining the Web space as a particular kind of linguistic corpus, commonly known as the Web Corpus. This article presents a novel, argumentative approach to providing proactive assistance for language usage assessment on the basis of usage indices, which are good indicators of the suitability of an expression on the basis of the Web Corpus. The user preferences consist of a number of (possibly defeasible) rules and facts that encode different aspects of adequate language usage, defining the acceptability of different terms on the basis of the computed usage indices. A defeasible argumentation system determines if a given expression is ultimately acceptable by analyzing a defeasible logic program that encodes the user's preferences. © 2006 Wiley Periodicals, Inc. Int J Int Syst 21: 1151–1180, 2006.     

A complexity perspective on entailment of parameterized linear constraints

Extending linear constraints by admitting parameters allows for more abstract problem modeling and reasoning. A lot of focus has been given to conducting research that demonstrates the usefulness of parameterized linear constraints and implementing tools that utilize their modeling strength. However, there is no approach that considers basic theoretical tools related to such constraints that allow for reasoning over them. Hence, in this paper we introduce satisfiability with respect to polyhedral sets and entailment for the class of parameterized linear constraints. In order to study the computational complexities of these problems, we relate them to classes of quantified linear implications. The problem of satisfiability with respect to polyhedral sets is then shown to be co- $\mathbb{NP}$ hard. The entailment problem is also shown to be co- $\mathbb{NP}$ hard in its general form. Nevertheless, we characterize some subclasses for which this problem is in ?. Furthermore, we examine a weakening and a strengthening extension of the entailment problem. The weak entailment problem is proved to be $\mathbb{NP}$ complete. On the other hand, the strong entailment problem is shown to be co- $\mathbb{NP}$ hard.     

多特征文本蕴涵识别研究

文本蕴涵识别是解决自然语言中存在的同义异形问题的有效途径。虽然国内外学者已经提出了很多文本蕴涵识别模型,但影响文本蕴涵识别的因素错综复杂,识别准确率普遍不高。该文把文本蕴涵识别看作二元分类问题,抽取词汇特征、句法依存关系特征及FrameNet语义知识库特征的多种特征构造特征矩阵,训练SVM分类器,实现文本蕴涵识别。该方法在国际文本蕴涵识别技术评测RTE3的测试集上进行测试,蕴涵正例识别准确率达到了78.1%,高于RTE3评测2-ways的最高结果。     

A conditional logic for defeasible beliefs

This paper provides a framework for representing beliefs by distinguishing between (i) the defeasible principles of a belief system, (ii) the propositions that are beyond reasonable doubt in a belief state, and (iii) the propositions ‘favored’ on the basis of defeasible principles and those propositions that are beyond reasonable doubt. Defeasible principles are interpreted semantically by means of a Lewis-style ranking of worlds (without the assumption that the actual world is among the ‘innermost’, or most highly ranked, worlds). The ‘favored closure’ (F-closure) of a set of defeasible principles and reasonable propositions is non-monotonic. Yet, given the concept of ‘pruning’ the default ranking relative to a set of worlds (determined by what is beyond reasonable doubt in a particular belief state) we provide a formal characterization of the conditions under which in inference to a favored conclusion on the basis of defeasible rules and reasonable propositions, is warranted. The adequacy of our representation of defeasible principles can be tested by considering a number of valid formulas that we list. We show that our concept of defeasible principle parallels but is not identical to the concept of ‘relatively high conditional probability’. An example of application of the formal language and semantics is given, and the final parts of the paper contain u     

面向阅读理解的多对一中文文本蕴含问题研究

机器阅读理解作为一种微阅读模式近年来在自动问答领域受到广泛关注,针对机器阅读理解中多对一的文本蕴含问题,该文首先构造了8 000级别的多句—单句中文文本蕴含语料M2OCTE,其次采用了层级神经网络模型,有效融合多个句子之间的语义信息,将多对一的蕴含对表达成统一的形式,实现端到端的输出。该方法在高考现代文阅读理解蕴含数据集上的准确率为58.92%,比将多句—单句的文本蕴含转换为单句—单句之后进行处理的准确率要高。同时也在英文数据集上进行了测试,实验结果验证了该方法的有效性。     

Defeasible Logic on an Embedded Microcontroller

Defeasible logic is a system of reasoning in which rules have exceptions, and when rules conflict, the one that applies most specifically to the situation wins out. This paper reports a successful application of defeasible logic to the implementation of an embedded control system. The system was programmed in d-Prolog (a defeasible extension of Prolog), and the inferences were compiled into a truth table that was encoded on a low-end PIC microcontroller.Advantages of defeasible logic include conciseness and correct handling of the passage of time. It is distinct from fuzzy logic and probabilistic logic, addressing a different set of problems.     

文本蕴涵的推理模型与识别模型

该文首先介绍一个逼近文本蕴涵关系的推理模型,它由带有推理规则集的蕴涵型式知识库和相关的概率评价构成。接着介绍习得推理规则和蕴涵型式及其概率的几种方法,包括从平行或单一语料库中学习和从网络文件中学习。然后介绍基于词汇概率的蕴涵识别模型,包括通过构建词汇蕴涵的概率模型和基于词汇所指的语义匹配模型来逼近文本蕴涵的几种方法。最后介绍基于句法的语义分析模型,包括基于依存树节点匹配、论元结构或原子命题匹配等处理模型。