基于动态规划的优先语义扩充求解算法

抽象辩论框架中的优先语义是判断争议可接受程度的最重要语义。现有优先扩充求解方法多用标记映射求解,依赖于标记的定义、转换规则、相邻争议的标记。算法每次迭代会产生一个新的抽象辩论框架导致时间、空间复杂度较高。提出一种基于动态规划的优先扩充算法,在动态规划中加入争议可接受性判断,求出辩论框架中极大可容许集得到优先扩充。在基于随机抽象辩论框架与ICCMA提供的数据集进行实验,同Heureka、ArgSemSAT等算法进行对比。结果表明,求解相同数量的优先扩充,算法耗时较少,时间、空间复杂度有所降低。     

A logic of argumentation for specification and verification of abstract argumentation frameworks

In this paper, we propose a logic of argumentation for the specification and verification (LA4SV) of requirements on Dung??s abstract argumentation frameworks. We distinguish three kinds of decision problems for argumentation verification, called extension verification, framework verification, and specification verification respectively. For example, given a political requirement like ??if the argument to increase taxes is accepted, then the argument to increase services must be accepted too,?? we can either verify an extension of acceptable arguments, or all extensions of an argumentation framework, or all extensions of all argumentation frameworks satisfying a framework specification. We introduce the logic of argumentation verification to specify such requirements, and we represent the three verification problems of argumentation as model checking and theorem proving properties of the logic. Moreover, we recast the logic of argumentation verification in a modal framework, in order to express multiple extensions, and properties like transitivity and reflexivity of the attack relation. Finally, we introduce a logic of meta-argumentation where abstract argumentation is used to reason about abstract argumentation itself. We define the logic of meta-argumentation using the fibring methodology in such a way to represent attack relations not only among arguments but also among attacks. We show how to use this logic to verify the requirements of argumentation frameworks where higher-order attacks are allowed [A preliminary version of the logic of argumentation compliance was called the logic of abstract argumentation?(2005).]     

Argument-based agreements in agent societies

In this paper, we present an abstract argumentation framework for the support of agreement processes in agent societies. It takes into account arguments, attacks among them, and the social context of the agents that put forward arguments. Then, we define the semantics of the framework, providing a mechanism to evaluate arguments in view of other arguments posed in the argumentation process. We also provide a translation of the framework into a neural network that computes the set of acceptable arguments and can be tuned to give more or less importance to argument attacks. Finally, the framework is illustrated with an example in a real domain of a water-rights transfer market.     

Reasoning about preferences in argumentation frameworks

The abstract nature of Dung's seminal theory of argumentation accounts for its widespread application as a general framework for various species of non-monotonic reasoning, and, more generally, reasoning in the presence of conflict. A Dung argumentation framework is instantiated by arguments and a binary conflict based attack relation, defined by some underlying logical theory. The justified arguments under different extensional semantics are then evaluated, and the claims of these arguments define the inferences of the underlying theory. To determine a unique set of justified arguments often requires a preference relation on arguments to determine the success of attacks between arguments. However, preference information is often itself defeasible, conflicting and so subject to argumentation. Hence, in this paper we extend Dung's theory to accommodate arguments that claim preferences between other arguments, thus incorporating meta-level argumentation based reasoning about preferences in the object level. We then define and study application of the full range of Dung's extensional semantics to the extended framework, and study special classes of the extended framework. The extended theory preserves the abstract nature of Dung's approach, thus aiming at a general framework for non-monotonic formalisms that accommodate defeasible reasoning about as well as with preference information. We illustrate by formalising argument based logic programming with defeasible priorities in the extended theory.     

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.     

Extending abstract argumentation systems theory

In this paper, we extend the theory of abstract argumentation systems proposed by Vreeswijk (1997). This framework stands at a high abstraction level and provides a general model for argumentation activity. However, the theory reveals an inherent limitation in that the premises of the argumentation process are assumed to be indefeasible, and this introduces the need of an implicit constraint on the strength of the arguments, in order to preserve correctness. In many application contexts the information available to start reasoning is not guaranteed to be completely reliable, therefore it is natural to assume that premises can be discarded during the argumentation process. We extend the theory by admitting that premises can be defeated and relaxing the implicit assumption about their strength.Besides fixing the technical problems related to this hidden assumption (e.g., ensuring that warranted arguments are compatible), our proposal provides an integrated model for belief revision and defeasible reasoning, confirming the suitability of argumentation as a general model for the activity of intelligent reasoning in presence of various kinds of uncertainty.     

Using arguments for making and explaining decisions

Arguments play two different roles in day life decisions, as well as in the discussion of more crucial issues. Namely, they help to select one or several alternatives, or to explain and justify an already adopted choice.This paper proposes the first general and abstract argument-based framework for decision making. This framework follows two main steps. At the first step, arguments for beliefs and arguments for options are built and evaluated using classical acceptability semantics. At the second step, pairs of options are compared using decision principles. Decision principles are based on the accepted arguments supporting the options. Three classes of decision principles are distinguished: unipolar, bipolar or non-polar principles depending on whether i) only arguments pros or only arguments cons, or ii) both types, or iii) an aggregation of them into a meta-argument are used. The abstract model is then instantiated by expressing formally the mental states (beliefs and preferences) of a decision maker. In the proposed framework, information is given in the form of a stratified set of beliefs. The bipolar nature of preferences is emphasized by making an explicit distinction between prioritized goals to be pursued, and prioritized rejections that are stumbling blocks to be avoided. A typology that identifies four types of argument is proposed. Indeed, each decision is supported by arguments emphasizing its positive consequences in terms of goals certainly satisfied and rejections certainly avoided. A decision can also be attacked by arguments emphasizing its negative consequences in terms of certainly missed goals, or rejections certainly led to by that decision. Finally, this paper articulates the optimistic and pessimistic decision criteria defined in qualitative decision making under uncertainty, in terms of an argumentation process. Similarly, different decision principles identified in multiple criteria decision making are restated in our argumentation-based framework.     

Combining Argumentation and Bayesian Nets for Breast Cancer Prognosis

We present a new framework for combining logic with probability, and demonstrate the application of this framework to breast cancer prognosis. Background knowledge concerning breast cancer prognosis is represented using logical arguments. This background knowledge and a database are used to build a Bayesian net that captures the probabilistic relationships amongst the variables. Causal hypotheses gleaned from the Bayesian net in turn generate new arguments. The Bayesian net can be queried to help decide when one argument attacks another. The Bayesian net is used to perform the prognosis, while the argumentation framework is used to provide a qualitative explanation of the prognosis.     

Characterizing strong equivalence for argumentation frameworks

Since argumentation is an inherently dynamic process, it is of great importance to understand the effect of incorporating new information into given argumentation frameworks. In this work, we address this issue by analyzing equivalence between argumentation frameworks under the assumption that the frameworks in question are incomplete, i.e. further information might be added later to both frameworks simultaneously. In other words, instead of the standard notion of equivalence (which holds between two frameworks, if they possess the same extensions), we require here that frameworks F and G are also equivalent when conjoined with any further framework H. Due to the nonmonotonicity of argumentation semantics, this concept is different to (but obviously implies) the standard notion of equivalence. We thus call our new notion strong equivalence and study how strong equivalence can be decided with respect to the most important semantics for abstract argumentation frameworks. We also consider variants of strong equivalence in which we define equivalence with respect to the sets of arguments credulously (or skeptically) accepted, and restrict strong equivalence to augmentations H where no new arguments are raised.     

Dynamics of argumentation systems: A division-based method

The changing of arguments and their attack relation is an intrinsic property of a variety of argumentation systems. So, it is very important to efficiently figure out how the status of arguments in a system evolves when the system is updated. However, unlike other areas of argumentation that have been deeply explored, such as argumentation semantics, proof theories, and algorithms, etc., dynamics of argumentation systems has been comparatively neglected. In this paper, we formulate a general theory (called a division-based method) to cope with this problem based on a new concept: the division of an argumentation framework. When an argumentation framework is updated, it is divided into three parts: an unaffected, an affected, and a conditioning part. The status of arguments in the unaffected sub-framework remains unchanged, while the status of the affected arguments is computed in a special argumentation framework (called a conditioned argumentation framework, or briefly CAF) that is composed of an affected part and a conditioning part. We have proved that under a certain semantics that satisfies the directionality criterion (complete, preferred, ideal, or grounded semantics), the extensions of the updated framework are equal to the result of a combination of the extensions of an unaffected sub-framework and sets of the extensions of a set of assigned CAFs. Due to the efficiency of the division-based method, it is expected to be very useful in various kinds of argumentation systems where arguments and attacks are dynamics.     

Argumentation frameworks as constraint satisfaction problems

Argumentation is a promising approach for defeasible reasoning. It consists of justifying each plausible conclusion by arguments. Since the available information may be inconsistent, a conclusion and its negation may both be justified. The arguments are thus said to be conflicting. The main issue is how to evaluate the arguments. Several semantics were proposed for that purpose. The most important ones are: stable, preferred, complete, grounded and admissible. A semantics is a set of criteria that should be satisfied by a set of arguments, called extension, in order to be acceptable. Different decision problems related to these semantics were defined (like whether an argumentation framework has a stable extension). It was also shown that most of these problems are intractable. Consequently, developing algorithms for these problems is not trivial and thus the implementation of argumentation systems not obvious. Recently, some solutions to this problem were found. The idea is to use a reduction method where a given problem is translated in another one like SAT or ASP. This paper follows this line of research. It studies how to encode the problem of computing the extensions of an argumentation framework (under each of the previous semantics) as a constraint satisfaction problem (CSP). Such encoding is of great importance since it makes it possible to use the very efficient solvers (developed by the CSP community) for computing the extensions. Our encodings take advantage of existing reductions to SAT problems in the case of Dung’s abstract framework. Among the various ways of translating a SAT problem into a CSP one, we propose the most appropriate one in the argumentation context. We also provide encodings in case two other families of argumentation frameworks: the constrained version of Dung’s abstract framework and preference-based argumentation framework.     

Computing Arguments and Attacks in Assumption-Based Argumentation

Most computational frameworks for argumentation are based on abstract argumentation, which determines an argument's acceptability on the basis of its ability to counterattack all arguments attacking it. However, this view of argumentation doesn't address how to find arguments, identify attacks, and exploit premises. Assumption-based argumentation addresses these three issues. It's a refinement of abstract argumentation but remains general purpose, nonetheless. Rather than considering arguments to be a primitive concept, assumption-based argumentation defines them as backward deductions (using sets of rules in an underlying logic) supported by sets of assumptions. This approach reduces the notion of an attack against an argument to that of deduction of a contrary of an assumption.     

分级论辩系统的逻辑研究

近年来,形式论证已逐渐成为人工智能领域的研究热点之一。自Dung于1995年提出抽象辩论框架起,学术界普遍认为论辩的核心任务是在各种基于外延的语义下对论点集进行评估,以确定其辩护状态。分级论辩系统(Graded Argumentation System,GAS)是对经典Dung型论辩系统(Dung-style Argumentation System,DAS)的推广,通过一般化DAS语义的两个核心性质,即无冲突性和可接受性,来提供更细化的论点状态概念。当前的论辩系统语义等效性研究主要集中在框架和论点层次上,可为其结构约简提供有力的保证。针对两个不同分级论辩系统中论点的语义等效问题,首先运用分级模态逻辑(Graded Modal Logic,GML)形式化分级论辩系统的片段,然后建立并证明了分级论辩系统基于外延的语义和GML公式之间的一一对应关系,最后定义分级互模拟关系并证明其蕴含分级论辩系统的4个重要的语义等价性。     

Algorithms and complexity results for persuasive argumentation

The study of arguments as abstract entities and their interaction as introduced by Dung (1995) [1] has become one of the most active research branches within Artificial Intelligence and Reasoning. A main issue for abstract argumentation systems is the selection of acceptable sets of arguments. Value-based argumentation, as introduced by Bench-Capon (2003) [8], extends Dung?s framework. It takes into account the relative strength of arguments with respect to some ranking representing an audience: an argument is subjectively accepted if it is accepted with respect to some audience, it is objectively accepted if it is accepted with respect to all audiences.Deciding whether an argument is subjectively or objectively accepted, respectively, are computationally intractable problems. In fact, the problems remain intractable under structural restrictions that render the main computational problems for non-value-based argumentation systems tractable. In this paper we identify nontrivial classes of value-based argumentation systems for which the acceptance problems are polynomial-time tractable. The classes are defined by means of structural restrictions in terms of the underlying graphical structure of the value-based system. Furthermore we show that the acceptance problems are intractable for two classes of value-based systems that where conjectured to be tractable by Dunne (2007) [12].     

Handling threats,rewards, and explanatory arguments in a unified setting

Current logic‐based handling of arguments has mainly focused on explanation or justification‐oriented purposes in presence of inconsistency. So only one type of argument has been considered, and several argumentation frameworks have then been proposed for generating and evaluating such arguments. However, recent works on argumentation‐based negotiation have emphasized different other types of arguments such as threats, rewards, and appeals. The purpose of this article is to provide a logical setting that encompasses the classical argumentation‐based framework and handles the new types of arguments. More precisely, we give the logical definitions of these arguments and their weighting systems. These definitions take into account that negotiation dialogues involve not only agents' beliefs (of various strengths), but also their goals (having maybe different priorities), as well as the beliefs on the goals of other agents. In other words, from the different beliefs and goals bases maintained by agents, all the possible threats, rewards, explanations, and appeals that are associated with them can be generated. It may also happen that an intended threat, or reward, is not perceived as such by the addressee and thus misses its target because the addresser misrepresents the addressee's goals. The proposed approach accounts for that phenomenon. Finally, we show how to evaluate conflicting arguments of different types. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 1195–1218, 2005.     

Using argument strength for building dialectical bonsai

Argumentation in AI provides an inconsistency-tolerant formalism capable of establishing those pieces of knowledge that can be accepted despite having information in contradiction. Computation of accepted arguments tends to be expensive; in order to alleviate this issue, we propose a heuristics-based pruning technique over argumentation trees. Empirical testing shows that in most cases our approach answers queries much faster than the usual techniques, which prune with no guide. The heuristics is based on a measure of strength assigned to arguments. We show how to compute these strength values by providing the corresponding algorithms, which use dynamic programming techniques to reutilise previously computed trees. In addition to this, we introduce a set of postulates characterising the desired behaviour of any strength formula. We check the given measure of strength against these postulates to show that its behaviour is rational. Although the approach presented here is based on an abstract argumentation framework, the techniques are tightly connected to the dialectical process rather than to the framework itself. Thus, results can be extrapolated to other dialectical-tree-based argumentation formalisms with no additional difficulty.     

On the merging of Dung's argumentation systems

In this paper, the problem of deriving sensible information from a collection of argumentation systems coming from different agents is addressed. The underlying argumentation theory is Dung's one: each argumentation system gives both a set of arguments and the way they interact (i.e., attack or non-attack) according to the corresponding agent. The inadequacy of the simple, yet appealing, method which consists in voting on the agents' selected extensions calls for a new approach. To this purpose, a general framework for merging argumentation systems from Dung's theory of argumentation is presented. The objective is achieved through a three-step process: first, each argumentation system is expanded into a partial system over the set of all arguments considered by the group of agents (reflecting that some agents may easily ignore arguments pointed out by other agents, as well as how such arguments interact with her own ones); then, merging is used on the expanded systems as a way to solve the possible conflicts between them, and a set of argumentation systems which are as close as possible to the whole profile is generated; finally, voting is used on the selected extensions of the resulting systems so as to characterize the acceptable arguments at the group level.     

Coalitions of arguments: A tool for handling bipolar argumentation frameworks

Bipolar argumentation frameworks enable to represent two kinds of interaction between arguments: support and conflict. In this paper, we turn a bipolar argumentation framework into a meta‐argumentation framework where conflicts occur between sets of arguments, characterized as coalitions of supporting arguments. So, Dung's well‐known semantics can be used on this meta‐argumentation framework to select the acceptable arguments. © 2009 Wiley Periodicals, Inc.     

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.     

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.