Assumption‐Based Argumentation Equipped with Preferences and its Application to Decision Making,Practical Reasoning,and Epistemic Reasoning

The existing approaches that map the given explicit preferences into standard assumption‐based argumentation (ABA) frameworks reveal some difficulties such as generating a huge number of rules. To overcome them, we present an assumption‐based argumentation framework equipped with preferences (p_ABA). It increases the expressive power of ABA by incorporating preferences between sentences into the framework. The semantics of p_ABA is given by extensions, which are maximal among extensions of ABA with regard to the extension ordering “lifted” from the given sentence ordering. As a theoretical contribution of this study, we show that prioritized logic programming can be formulated as a specific form of p_ABA. The advantage of our approach is that not only does p_ABA enable us to express different kinds of preferences such as preferences over rules, over goals, or over decisions by means of sentence orderings but we can also successfully obtain solutions from extensions of the p_ABA expressing the respective knowledge for various applications such as epistemic reasoning, practical reasoning, and decision making with preferences in a uniform and domain‐independent way without suffering from difficulties of the existing approaches.     

Defeasible Reasoning in Japanese Criminal Jurisprudence

Modeling legal argumentation is one of the most important research in AI and Law, and a lot of models have been proposed. However, most research has not treated value judgement and debate. In this paper, we introduce a legal reasoning model which covers various aspects of legalreasoning such as making argument, selecting argument and debate.Furthermore, we present how criminal law is described and reasoned inthis model.     

Reasoning paradigms in legal decision support systems

In this paper we discuss the strengths and weaknesses of a range of artificial intelligence approaches used in legal domains. Symbolic reasoning systems which rely on deductive, inductive and analogical reasoning are described and reviewed. The role of statistical reasoning in law is examined, and the use of neural networks analysed. There is discussion of architectures for, and examples of, systems which combine a number of these reasoning strategies. We conclude that to build intelligent legal decision support systems requires a range of reasoning strategies.     

范例推理系统中的范例库维护

在范例推理系统中，系统的学习会使范例库逐渐增大，一般来说范例库越大，知识越丰富，但也不能无限增加，否则会大大增加相似范例检索的时间，降低系统的总体性能。因此范例推理学习系统必须有维护功能，主要目的是限制范例库的无限膨胀，且能保持系统的性能。本文在给出一个改进的删除策略维护方案的同时，并从另一角度出发，提出一个基于范例增加的维护策略，以保证系统的性能不受影响，从而达到小范例库强功能的目的。     

基于案例系统的不确定案例索引和检索方法

将粗集理论引入基于案例的推理系统,充分利用案例库中冗冗余属性的简化,形成案例的多个索引,从而可根据不同问题按不同索引进行检索并得出结论。计算实例表明,该方法既能有效地解决不确定问题,又能提高系统的性能。     

基于案例推理的教学案例知识管理系统的设计

随着案例教学的研究成为我国教育的一个热点,与此同时,教学案例的管理问题被提上日程。文章阐述了利用案例推理技术（case-base Reasoning）,设计支持教师进行教学案例研究的平台——基于CBR的教学案例知识管理系统。最大限度地方便教师获取他们所需的教学案例知识。     

一种改进的案例推理分类方法研究

特征属性的权重分配和案例检索策略对案例推理（Case-based reasoning,CBR）分类的准确率有显著影响. 本文提出一种结合遗传算法、内省学习和群决策思想改进的CBR分类方法. 首先,利用遗传算法得到多组属性权重,再根据内省学习原理对每组权重进行迭代调整;然后,通过案例群检索策略得到满足大多数原则的群决策分类结果;最后,以典型分类数据集的对比实验证明了本文方法能进一步提高CBR分类的准确率. 这表明内省学习可以保证权重分配的合理性,案例群检索策略能充分利用案例库的潜在信息,对提升CBR的学习能力有显著作用.     

基于实例推理的储集层评价智能系统

针对不同地质环境其储集层评价参数的变异性大,难以建立通用的定量评价标准以及传统的评价方法的不足,提出利用实例推理技术建立储集层评价智能系统。并在传统的实例检索策略基础上,引入模糊相似优先检索策略进行分级检索控制,可有效地处理检索中的不精确性。文中从实例的表示与组织、实例检索方面对储集层评价过程进行了详细阐述。     

Try to See it My Way: Modelling Persuasion in Legal Discourse

In this paper I argue that to explain and resolve some kinds of disagreement we need to go beyond what logic alone can provide. In particular, following Perelman, I argue that we need to consider how arguments are ascribed different strengths by different audiences, according to how accepting these arguments promotes values favoured by the audience to which they are addressed. I show how we can extend the standard framework for modelling argumentation systems to allow different audiences to be represented. I also show how this formalism can explain how some disputes can be resolved while in others the parties can only agree to differ. I illustrate this by consideration of a legal example. Finally, I make some suggestions as to where these values come from, and how they can be used to explain differences across jurisdictions, and changes in views over time.     

范例推理在智能决策系统中的应用研究

范例推理是人工智能中重要的推理方法和机器学习技术,它也是智能系统中实用的技术之一。基于范例的决策是决策者认知心理的决策过程的一个合理描述,它提供了一种实现智能系统及决策的现实环境和技术方法。本文提出了基于范例推理的智能决策技术,给出应用模型,并进行了深入讨论。     

A Computational Framework for Practical Social Reasoning

This article describes a framework for practical social reasoning designed to be used for analysis, specification, and implementation of the social layer of agent reasoning in multiagent systems. Our framework, called the expectation strategy behavior (ESB) framework, is based on (i) using sets of update rules for social beliefs tied to observations (so‐called expectations), (ii) bounding the amount of reasoning to be performed over these rules by defining a reasoning strategy, and (iii) influencing the agent's decision‐making logic by means of behaviors conditioned on the truth status of current and future social beliefs. We introduce the foundations of ESB conceptually and present a formal framework and an actual implementation of a reasoning engine, which is specifically combined with a general (belief–desire–intention‐based) practical reasoning programming system. We illustrate the generality of ESB through select case studies, which show that it is able to represent and implement different typical styles of social reasoning. The broad coverage of existing social reasoning methods, the modularity that derives from its declarative nature, and its focus on practical implementation make ESB a useful tool for building advanced socially reasoning agents.     

基于事例的推理中相似事例选择的神经网络方法

在基于事例的推理（CBR）系统中，为解决采用最相邻近法选择相似事例时属性权值不易确定的问题，提出事例的前提相似度和结论相似度的概念，并采用一种人工神经网络结构进行属性权值的学习． 该方法从事例库中自动获取属性权值，并用最相邻近法和学习到的属性权值进行相似事例选择．最后，在光动力治疗（PDT）鲜红斑痣（PWS）的临床病例库中应用该方法进行了试验．     

基于事例推理的Web服务QoS动态预测研究

随着面向服务计算技术的发展,网络上出现了大量功能相同而服务质量(QoS)有很大差别的Wcb服务,QoS逐渐成为评价和选择Web服务的重要依据。目前常用Web服务历史QoS的算术平均值来近似服务的QoS,这种度量方法没有考虑Wcb服务QoS的动态性,不能准确地度量Wcb服务的QoS,从而造成被选择的Wcb服务以较大概率不能满足用户的QoS需求。针对这一问题,提出了一种基于事例推理(C13R)的QoS动态预测方法,该方法将Web服务的QoS与服务的外界环境、所处理的任务类型、任务大小关联起来,利用事例推理技术预测Web服务处理新任务时的QoS。实验结果表明,该预测方法能有效地提高Wcb服务QoS的准确度。     

An Infrastructure for Argumentative Agents

Multiagent systems are suitable for providing a framework that allows agents to perform collaborative processes in a social context. Furthermore, argumentation is a natural way of reaching agreements between several parties. However, it is difficult to find infrastructures of argumentation offering support for agent societies and their social context. Offering support for agent societies allows representation of more realistic environments to have argumentation dialogues. We propose an infrastructure to develop and execute argumentative agents in an open multiagent system. It offers tools to develop agents with argumentation capabilities. It also offers support for agent societies and their social context. The infrastructure is publicly available. Also, it has been implemented in an application scenario where argumentative agents try to reach an agreement about the best solution to solve a problem reported to the system.     

案例推理式园林植物病虫害诊断系统的实现

为减少园林植物养护管理对于病虫害专家的依赖,运用知识工程相关理论,进行了园林植物病虫害诊断系统的知识库、案例库及推理机制的设计。通过专家系统技术与主流计算机网络信息技术的结合,构建了服务于我国园林管理机构、面向我国数字化园林的新型网络智能系统。     

基于案例系统的一种案例标引和获取方法及算法

基于案例系统通过模拟人们分析和处理问题的方式,以达到辅助决策的目的。案例标引和获取是基于案例系统中的两个重要环节。针对已有方法的不足,提出一种新的案例标引和获取方法。     

基于框架和案例推理的应急预案表示和优选方法的研究设计

采用基于框架表示法的结构化技术表达静态预案,并基于关系模型设计预案库结构和索引。同时将基于案例推理方法和最近邻法相结合,通过计算突发事件与预案的相似度实现最优相似预案的搜索。     

基于实例的多推理机合作变型设计系统

产品变型设计的基本思想就是通过改进已有的系列产品或模块来适应新的设计需求.针对变型设计的基本特点,提出了以基于实例的推理为基础,综合运用规则推理和约束满足方法来完成产品的变型设计活动.结合所开发的“细纱机模块化变型设计系统”,从知识表达模式、推理流程等方面详细讨论了以基于实例的推理为核心方法的多推理机合作变型设计系统的实现.     

基于投影寻踪和MapReduce的并行案例推理模型

在大规模、高维度的数据环境下,传统的案例推理具有计算复杂度高、实时性差等缺点。为在大数据环境下进行案例推理,提出了一种基于投影寻踪和MapReduce的并行推理模型dpCBR。在数据预处理阶段,计算源案例到基准向量的一维投影距离并缓存,降低计算复杂度并减少重复计算开销。在案例检索阶段,先根据投影距离裁剪案例库,再进行相似度匹配,减少不必要的案例匹配开销。应用MapReduce进行分布式并行处理,使dpCBR具备对大规模案例库的推理能力。实验结果表明,dpCBR模型可以明显提高大数据环境下案例推理的效率。