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.     

Understanding Similarity: A Joint Project for Psychology, Case-Based Reasoning, and Law

Case-based Reasoning (CBR) began as a theory of human cognition, but has attracted relatively little direct experimental or theoretical investigation in psychology. However, psychologists have developed a range of instance-based theories of cognition and have extensively studied how similarity to past cases can guide categorization of new cases. This paper considers the relation between CBR and psychological research, focussing on similarity in human and artificial case-based reasoning in law. We argue that CBR, psychology and legal theory have complementary contributions to understanding similarity, and describe what each offers. This allows us to establish criteria for assessing existing CBR systems in law and to establish what we consider to be the crucial goals for further research on similarity, both from a psychological and a CBR perspective.     

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.     

一种新的多维稀疏规则模糊插值推理方法

经典的插值理论针对一维稀疏规则库的条件，提出了各种不同的插值方法，取得了很多很好的经验．但对多维稀疏规则条件的近似推理，研究很少，仅有的几种插值方法，存在着难以保证推理结果的凸性和正规性等问题．为了在多维稀疏规则条件下能得到好的插值推理结果。提出了一种基于几何相似的插值推理方法．该方法能较好地保证推理结果隶属函数的凸性和正规性，为智能系统中的模糊推理提供了一个十分有用的工具．     

模型检验中抽象技术研究综述

在模型检验中,抽象技术是解决状态空间爆炸问题的有效方法之一。论文描述了模型检验对抽象模型的基本要求,给出了抽象模型的定义及其评价指标,对抽象技术和自动化的抽象精化技术的主要方法及其研究进展作了比较深入、全面的综述,并讨论了抽象技术今后的发展方向。     

Characterisation of a Novel Indexing Technique for Case-Based Reasoning

In this paper, we investigate two novel indexing schemes called D-HS and D-HS+PSR(II) designed for use in case-based reasoning systems. D-HS is based on a matrix of cases indexed by their discretised attribute values. D-HS+PSR(II) extends D-HS by combining the matrix with an additional tree-like indexing structure to allow for solution reuse. D-HS+PSR(II)s novelty lies in its ability to improve retrieval efficiency over time by reusing previously encountered solution patterns. Benefits include simplicity, accuracy, speed, robustness to missing values and ability to facilitate efficient real time maintenance of retrieval knowledge as the size of the case-base grows. We present empirical results from an analyses of 20 case-bases and demonstrate the techniques to be of similar competency to C4.5 yet much more efficient. Performance advantages over C4.5 are shown to be especially apparent when tested on case-bases which grow in size over time or those with missing values.     

基于Vague聚类规则的双向近似推理

在智能系统的研究与开发中，推理方法的计算复杂性是一个很重要的问题．为了获得良好的推理效果和推理效率，就必须降低推理方法的计算复杂性．为此本文首先给出了一个新的vague集间的距离定义，然后给出了相似方向的概念及相似方向的判定方法．在此基础上，提出了对vague规则进行聚类，以及基于vague聚类规则的双向近似推理方法，该方法更好地利用了vague集信息的精确性，而且降低了推理的计算复杂性，从而提高了推理的精确性和适用性．并用实例验证了该方法的有效性．这为智能系统中的近似推理提供了一个十分有用的工具．     

基于正规三角模糊集相似度量的模糊推理

文章首先定义了正规模糊集相似度量的一般公式,然后给出了正规三角模糊集相似度量计算的简化公式,在正规三角模糊集相似度量的基础上,提出了一种新的模糊推理的方法——基于正规三角模糊集相似度量的模糊推理,并与Mamdani模糊推理方法做了比较。最后给出了模糊推理的仿真实现。     

基于实例推理的注塑模概念设计

基于实例的推理方法是使用已有的经验来解决新问题,该文尝试把它用于注塑模的概念设计。首先分析了基于CBR的系统在表达模具设计领域强经验弱理论知识的优势,然后给出了注塑模概念设计系统工作流程。提出了模具设计实例特征描述模型和实例的框架表示。最后讨论了基于模糊相似优先比的相似度量及实例检索过程。     

Derivational Analogy in PRODIGY: Automating Case Acquisition,Storage, and Utilization

Expertise consists of rapid selection and application of compiled experience. Robust reasoning, however, requires adaptation to new contingencies and intelligent modification of past experience. And novel or creative reasoning, by its real nature, necessitates general problem-solving abilities unconstrained by past behavior. This article presents a comprehensive computational model of analogical (case-based) reasoning that transitions smoothly between case replay, case adaptation, and general problem solving, exploiting and modifying past experience when available and resorting to general problem-solving methods when required. Learning occurs by accumulation of new cases, especially in situations that required extensive problem solving, and by tuning the indexing structure of the memory model to retrieve progressively more appropriate cases. The derivational replay mechanism is discussed in some detail, and extensive results of the first full implementation are presented. These results show up to a large performance improvement in a simple transportation domain for structurally similar problems, and smaller improvements when less strict similarity metrics are used for problems that share partial structure in a process-job planning domain and in an extended version of the strips robot domain.     

Legal Ontologies in Knowledge Engineering and Information Management

In this article we describe two core ontologies of law that specify knowledge that is common to all domains of law. The first one, FOLaw describes and explains dependencies between types of knowledge in legal reasoning; the second one, LRI-Core ontology, captures the main concepts in legal information processing. Although FOLaw has shown to be of high practical value in various applied European ICT projects, its reuse is rather limited as it is rather concerned with the structure of legal reasoning than with legal knowledge itself: as many other “legal core ontologies”, FOLaw is therefore rather an epistemological framework than an ontology. Therefore, we also developed LRI-Core. As we argue here that legal knowledge is based to a large extend on common-sense knowledge, LRI-Core is particularly inspired by research on abstract common-sense concepts. The main categories of LRI-Core are: physical, mental and abstract concepts. Roles cover in particular social worlds. Another special category are occurrences; terms that denote events and situations. We illustrate the use of LRI-Core with an ontology for Dutch criminal law, developed in the e-Court European project.     

基于内容二次聚类的关键帧提取算法

提出了一种基于内容二次聚类的关键帧提取算法。此算法在计算图像帧相似度时,通过分块给不同的块赋予不同的权值,以体现出图像在语义上的重点内容。同时,通过计算自适应阈值对视频进行初次聚类,并计算类间距离,在此基础上再进行二次聚类后得到最终类别,从每个最终类中选取距离类中心最近的图像帧作为关键帧。这种方法经过二次聚类后可克服一次聚类会出现冗余的缺点,实验证明,此算法提取的关键帧更全面、准确地体现了原视频的内容。     

Semantic Generalizations for Proving and Disproving Conjectures by Analogy

Taking an extension of resolution as a base calculus (though the same principles are applicable to other calculi) for searching proofs (refutations) and counterexamples (models), we introduce a new method able to find refutations and also models by analogy with refutations and models in a knowledge base. The source objects for the analogy process are generalizations of the refutations (models). They are included in the knowledge base, and then higher-order matching techniques for the choice of the relevant source objects as well as the building of a new proof or a model by analogy are used. Some comparisons with existing methods as well as two detailed running examples on generalization show evidence of the interest of our approach.     

An Abstraction Algorithm for the Verification of Level-Sensitive Latch-Based Netlists

High-performance hardware designs often intersperse combinational logic freely between level-sensitive latch layers (wherein each layer is transparent during only one clock phase), rather than utilizing master-slave latch pairs with no combinational logic between. While such designs may generally achieve much faster clock speeds, this design style poses a challenge to verification. In particular, unless the k-phase netlist N is abstracted to a full-cycle register-based netlist N, verification of N requires k times (or greater) as many state variables as would be necessary to obtain equivalent verification of N. We present algorithms to automatically identify and abstract k-phase netlists—i.e., to perform phase abstraction—by selectively eliminating latches. The abstraction is valid for model checking CTL* formulae which reason solely about latches of a single phase. This algorithm has been implemented in the model checker RuleBase, and used to enhance the model checking of IBM's Gigahertz Processor, which would not have been feasible otherwise due to computational constraints. This abstraction has furthermore allowed verification engineers to write properties and environments more efficiently.     

线条增强的建筑物图像抽象画生成

针对建筑物图像线条鲜明、轮廓清晰的特点,提出一种自动化的抽象画生成算法.首先通过双边滤波对原始图像的细节进行简化;然后利用快速的、带有误判控制的图像线段检测算法来获得建筑物的外轮廓和门窗等线条特征,通过该线条特征增强基于流的高斯差滤波器生成线条框架;最后将双边滤波处理结果与线条框架进行融合形成最终的抽象画效果.该算法既去掉了建筑物图像中多余的细节信息,使整个图像更趋抽象,同时又通过边缘信息保持了建筑物自身独有的风格,充分展现出富有艺术家手绘风格的建筑物效果.文中算法简单、易于实现,并通过实验充分证明了其有效性.     

Maintaining Retrieval Knowledge in a Case-Based Reasoning System

The knowledge stored in a case base is central to the problem solving of a case-based reasoning (CBR) system. Therefore, case-base maintenance is a key component of maintaining a CBR system. However, other knowledge sources, such as indexing and similarity knowledge for improved case retrieval, also play an important role in CBR problem solving. For many CBR applications, the refinement of this retrieval knowledge is a necessary component of CBR maintenance. This article focuses on optimization of the parameters and feature selections/weights for the indexing and nearest-neighbor algorithms used by CBR retrieval. Optimization is applied after case-base maintenance and refines the CBR retrieval to reflect changes that have occurred to cases in the case base. The optimization process is generic and automatic, using knowledge contained in the cases. In this article we demonstrate its effectiveness on a real tablet formulation application in two maintenance scenarios. One scenario, a growing case base, is provided by two snapshots of a formulation database. A change in the company's formulation policy results in a second, more fundamental requirement for CBR maintenance. We show that after case-base maintenance, the CBR system did indeed benefit from also refining the retrieval knowledge. We believe that existing CBR shells would benefit from including an option to automatically optimize the retrieval process.     

A Simulation Tool for Efficient Analogy Based Cost Estimation

Estimation of a software project effort, based on project analogies, is a promising method in the area of software cost estimation. Projects in a historical database, that are analogous (similar) to the project under examination, are detected, and their effort data are used to produce estimates. As in all software cost estimation approaches, important decisions must be made regarding certain parameters, in order to calibrate with local data and obtain reliable estimates. In this paper, we present a statistical simulation tool, namely the bootstrap method, which helps the user in tuning the analogy approach before application to real projects. This is an essential step of the method, because if inappropriate values for the parameters are selected in the first place, the estimate will be inevitably wrong. Additionally, we show how measures of accuracy and in particular, confidence intervals, may be computed for the analogy-based estimates, using the bootstrap method with different assumptions about the population distribution of the data set. Estimate confidence intervals are necessary in order to assess point estimate accuracy and assist risk analysis and project planning. Examples of bootstrap confidence intervals and a comparison with regression models are presented on well-known cost data sets.