包含演绎支持关系和必要支持关系的论辩框架

对于包含支持关系的论辩框架,已有研究中存在对攻击的定义繁琐、外延求解复杂等问题。该文用演绎支持关系和必要支持关系来扩展抽象论辩框架,并采用一种基于强度的方法来定义该框架中的攻击关系,在此基础上提出一种更简洁且更有表达力的基于外延的语义。该文将一种基于等式的方法运用于这种论辩框架,为其提供一种基于标记的语义。最后,该文证明在这种论辩框架下,基于等式的方法和基于外延的语义之间存在对应关系。     

一种渐进式论辩语义的论证博弈模型

在可计算论辩模型中,论辩语义的证明理论解决如何判定给定论辩语义中某个论证的证成状态的问题,这通常需要建构与之对应的论证博弈模型。论证博弈发生在正方和反方的论证交互过程中,正反双方都是通过给出攻击论证来质疑对方的论证和辩护己方的论证,正方只有在论证博弈中获胜才能使其初始论证获得确定的证成状态。文中定义了一种被称为BRD-论辩语义的渐进式论辩语义,不同于Dung的抽象论辩语义,它是在结构化论辩框架ASPIC+中嵌入了一种用于计算论证的强度和证成度的循环语义。为了给出该语义的证明理论,建构了与之对应的论证博弈模型。     

基于简单访问接口的GML数据解析方法

根据地理标记语言(GML)的应用需求,设计一种基于简单访问接口的GML数据解析方法。定义GML应用语义块,借助Xerces软件包分析GML模式,通过递归算法对GML标签进行语义判读。基于Adapter模式构建事件解析驱动,实现GML语义块数据的读取。实验结果表明,该方法可实现GML数据的查询、索引和可视化。     

RCC的可数核心模型

空间逻辑是人工智能领域中的研究热点,RCC系统(GRCC系统)是其中最受关注的一个形式化系统。从连接关系的冗余和非冗余性质出发,给出了核心模型的定义,并且证明了核心模型的存在性定理。讨论了RCC模型的个体(相当于空间中的物体)内部连通性,证明了该内部连通性是一阶语义可定义的。基于内部连通性,证明了核心模型的外延定理。     

基于映射机制的GML文档合法性校验

合法性校验是GML文档应用的一个重要环节,将GML3.1的核心模式和GML应用模式映射为关系模式,用正则表达式描述GML模式和文档中的模型（model）采用堆栈机制读取GML文档数据对其进行合法性校验.同时,生成GML3.1核心模式的语义库与语法库,为GML应用文档的解析、索引机制和查询等的研究奠定基础.     

基于n值关系语义的命题模态逻辑系统研究

传统的多值模态逻辑系统将关系语义中的状态及状态间的关系进行了多值化处理。然而,实际应用中状态间的关系往往是确定的,无需多值化。针对这种情况,基于?ukasiewicz代数系统提出了一种新的命题模态逻辑n值关系语义。在所提出的n值关系语义中,针对状态进行了多值化处理,同时保持了状态间关系的确定性。通过对逻辑公式的形式化定义以及可满足性和有效性的分析,证明了n值关系语义下经典命题模态逻辑系统K,T,S4和S5的正确性。进一步地,给出了极大一致集与典范模型在n值关系语义下的定义,并完成了上述经典命题模态逻辑系统的完备性证明。上述结论表明基于n值关系语义的命题模态逻辑系统能够涵盖并捕捉到经典逻辑系统中的所有有效命题。综上所述,所提出的基于?ukasiewicz代数系统的n值关系语义提供了一种在实际应用中处理多值状态及确定的状态间关系的方法。这种方法在扩展命题模态逻辑系统的形式化定义与关系语义是可行且有效的。     

处理不确定性的外延方法和内涵方法的比较

1.引言从语义的角度分类,研究不确定性的方法分为两种:外延方法和内涵方法。前者也称为产生式系统、基于规则的系统、基于过程的系统。MYCIN,PROSPEC-TOR是典型的外延方法。外延方法把不确定性作为一般化(generalized)的真值附着在公式上,依照传统的经典逻辑,任何公式的不确定性都是其子公式不确定性的函数,因此公式间的联系就是选择合适的组合函数。例如,合取式A∧B的不确定性值是A的不确定性值和B的不确定性值的函数,如min函数。内涵方法也称为陈述系统(declarative system)或基于模型的系统。不确定性附着于事件的状态集或可能世界的子集,子集间的联系通过集合上的操作完成。例如,p(A∧B)是包含A为真和B为真的可能世界子集的概率值,不能从个体概率p(A)和p(B)中得出。外延方法和内涵方法的优缺点针锋相对,外延方法计算简便,但语义不系统(sloppy);内涵方法语义清晰,但计算复杂。     

任务网络到时间自动机的等价模型验证

针对实时调度理论模型缺乏形式化语义的问题,提出将结果行为的语义转换成系统模型的形式化方法。结合定时分析技术,基于已有的任务网络形式方法,证明任务网络模型与候选型体系结构(CTA)模型稳态的等效性,将任务网络模型映射到语义相同的CTA模型,并验证该映射的语义等效性。将该方法应用于实例中,结果表明,该方法能代替调度模型,高效地应用于实时调度系统。     

基于语义贝叶斯网络的乳腺X线影像分级研究

针对乳腺X线诊断的不确定性和BI-RADS分级的广泛应用,提出将基于语义的贝叶斯网络应用于乳腺X线影像的BI-RADS分级.系统利用专家知识建立网络结构,机器学习确定网络参数.实验结果表明,针对包含缺失数据的100个病例样本,分级结果与资深乳腺放射医生相比较达到了87%的准确率.该模型直观地表达了乳腺X线图像的主要征象知识,为医生尤其是年轻医生提供了一个乳腺X线影像分级的客观参考.     

Web语义关联的一种分级方法

针对Web查询中查询路径分级不精确问题,文中基于RDF和XML技术,通过在语义查询路径上增加语义关联的影响因子,并根据路径上各个实体或属性的影响因子综合计算出一条路径的等级,按照级别高低把查询结果较准确地提供给用户,从而在一定程度上减少用户的分析工作。与一般的分级方法相比,增加语义关联的影响因子后,能够较好地实现基于语义的Web查询。     

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.     

一种新的辩论推理模式及其应用

文中定义了一个新的辩论推理模式,建立了一个形式化的知识表示框架,并把它应用于研究扩展逻辑程序类的说明语义,结果表明,新语义克服了择优语义的不足。作者还根据上述研究结果实现了逻辑程序设计风格下的知识框架。     

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.     

一种基于时态的扩展值辩论框架

时间是用来描述辩论过程以及辩论活动变化的一个重要因素,在辩论框架中加入时间因素是积极的。本文结合Dung的标准辩论框架以及Bench-Capon的基于值的辩论框架,提出了基于时态的扩展值辩论框架。首先分析了Dung的辩论框架以及Bench-Capon的值辩论框架在时态以及辩论值方面描述的局限性及需求,然后结合这些需求提出了基于时态的扩展值辩论框架,给出了完整的框架结构和语义描述,证明了基于时态的扩展值辩论框架满足Dung提出的标准辩论框架的一些基本定理。     

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。     

The computational complexity of ideal semantics

We analyse the computational complexity of the recently proposed ideal semantics within both abstract argumentation frameworks (afs) and assumption-based argumentation frameworks (abfs). It is shown that while typically less tractable than credulous admissibi-lity semantics, the natural decision problems arising with this extension-based model can, perhaps surprisingly, be decided more efficiently than sceptical preferred semantics. In particular the task of finding the unique ideal extension is easier than that of deciding if a given argument is accepted under the sceptical semantics. We provide efficient algorithmic approaches for the class of bipartite argumentation frameworks and, finally, present a number of technical results which offer strong indications that typical problems in ideal argumentation are complete for the class of languages decidable by polynomial time algorithms allowed to make non-adaptive queries to a C oracle, where C is an upper bound on the computational complexity of deciding credulous acceptance: C=np for afs and logic programming (lp) instantiations of abfs; for abfs modelling default theories.     

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.     

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.     

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.     

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.