A Goal-Dependent Abstraction for Legal Reasoning by Analogy

This paper presents a new algorithm to find an appropriate similarityunder which we apply legal rules analogically. Since there may exist a lotof similarities between the premises of rule and a case in inquiry, we haveto select an appropriate similarity that is relevant to both thelegal rule and a top goal of our legal reasoning. For this purpose, a newcriterion to distinguish the appropriate similarities from the others isproposed and tested. The criterion is based on Goal-DependentAbstraction (GDA) to select a similarity such that an abstraction basedon the similarity never loses the necessary information to prove the ground (purpose of legislation) of the legal rule. In order to cope withour huge space of similarities, our GDA algorithm uses some constraintsto prune useless similarities.     

Semantics in analogical reasoning

Abstract

To have broad application or approach the capacity of ordinary human thinking, analogical reasoning programs must become more complex or their semantics must become richer, or both. Little research is being done to discover what a broad collection of semantics can contribute to general-purpose analogical reasoning. From an analysis of a small collection of words, two classes containing over 300 categories are determined representing the semantics for understanding metaphors. A broad-based collection of simple target is source metaphors is sampled with each target and source represented in terms of these categories without known influence from the respective metaphor. A computational model is developed drawing on several disciplines while using rules for the recognition and elaboration phases of reasoning. Recognition primarily involves finding and reorganizing relevant schemata within the source. Elaboration involves reorganizing and possibly adding to the target. Given each metaphor, the TisS computer program creates a new representation of each target and source including several meanings for each metaphor. A test with human subjects making aptness and agreement judgements on the generated statements is discussed, suggesting a methodology for using a rich semantic base in analogical 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.     

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．     

一种基于实例推理的概念学习方法

特征结构是一种新的实例表示方法,结合本体提供的术语和类别层次关系特别易于表达复杂的关系结构型实例,首先讨论了一种基于特征结构实例推理的概念学习方法:C-LID算法,在提出基于特征结构实例推理解决概念学习问题的相似原则和语义包含原则基础上分析了C-LID算法的缺点,进一步提出了基于豪斯多夫距离和K-S相似度的消极概念学习方法R-LID,一方面R-LID是C-LID算法在相似原则下的扩展,另一方面R-LID避免了最多最优偏置下近邻选取不当造成的误差。将R-LID用于化合物致癌等级划分的开放问题上,结果表明R-LID算法具有更好的性能。     

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.     

加强概率推理模型在人脸识别中的应用

在很多应用中,组合使用多个分类器可以降低分类错误率。该文就是基于这个思想提出了新的人脸识别算法,即加强概率推理模型。在该算法中,将分类任务划分成多个子分类器,每个子分类器集中于一些难分类的样本,然后组合这些子分类器形成一个强的分类器。试验结果表明算法的识别率比原来的概率推理模型的识别率提高了1.8％。     

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。     

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.     

产品的相似性推理:一种描述逻辑的方法

采用产品本体理论对设计进行本体标注,得到对应产品设计的形式化概念,在此基础上通过本体的推理——基于描述逻辑的知识推理,对产品进行分类．文中分别就设计的相似性和基于实例的设计中实例间的相似性推理提出了明确的算法,并通过实例进行了说明．     

基于本体的语义虚拟环境查询与推理模型

当前将本体引入到语义虚拟环境的研究,只是将领域本体的可视化信息用本体表示,并未发挥本体本身具有的优势。为此,提出一种基于本体的语义虚拟环境查询与推理模型。利用OWL语言统一描述虚拟场景图形内容与语义信息,并分别对两者进行查询,在图形内容查询过程中引入本体的推理方法推理出隐含的图形内容信息,然后查询需要的信息。在语义信息查询时引入语义搜索方法,利用基于语义距离计算本体概念相似度的方法计算语义虚拟环境本体中类之间的相似度,搜索与被查询实例语义相似度最大的实例,并借助推理找出其间的关系。对语义虚拟家具商店进行本体的查询与推理,结果证明了该模型的可行性。     

System LS: A Three-Tiered Nonmonotonic Reasoning System

In this paper, a formal system of nonmonotonic reasoning is developed, which takes as its inspiration the manner in which some people make logically justifiable conclusions about nonmonotonic reasoning problems. The people, when asked about individuals, compare the logical strength of the arguments relating any sets to which the individual belongs, to other sets. A three-tiered system of rules including rules of System P as well as Transitivity and Monotonicity is developed. The system, known as System LS for logical strength, deals with three levels of non-strict relationships:

• α ₁β  α are normally β ( more than half of the α are β ) ;

       
  
  

Analogical model formulation for transfer learning in AP Physics

Transfer learning is the ability to apply previously learned knowledge to new problems or domains. In qualitative reasoning, model formulation is the process of moving from the unruly, broad set of concepts used in everyday life to a concise, formal vocabulary of abstractions, assumptions, causal relationships, and models that support problem-solving. Approaching transfer learning from a model formulation perspective, we found that analogy with examples can be used to learn how to solve AP Physics style problems. We call this process analogical model formulation and implement it in the Companion cognitive architecture. A Companion begins with some basic mathematical skills, a broad common sense ontology, and some qualitative mechanics, but no equations. The Companion uses worked solutions, explanations of example problems at the level of detail appearing in textbooks, to learn what equations are relevant, how to use them, and the assumptions necessary to solve physics problems. We present an experiment, conducted by the Educational Testing Service, demonstrating that analogical model formulation enables a Companion to learn to solve AP Physics style problems. Across six different variations of relationships between base and target problems, or transfer levels, a Companion exhibited a 63% improvement in initial performance. While already a significant result, we describe an in-depth analysis of this experiment to pinpoint the causes of failures. Interestingly, the sources of failures were primarily due to errors in the externally generated problem and worked solution representations as well as some domain-specific problem-solving strategies, not analogical model formulation. To verify this, we describe a second experiment which was performed after fixing these problems. In this second experiment, a Companion achieved a 95.8% improvement in initial performance due to transfer, which is nearly perfect. We know of no other problem-solving experiments which demonstrate performance of analogical learning over systematic variations of relationships between problems at this scale.     

A Fuzzy Integral Based Query Dispatching Model in Collaborative Case-Based Reasoning

In a collaborative (distributed) Case-Based Reasoning (CBR) environment, an input query case could be compared with the old cases that are resided in many different CBR agents in the network. How to obtain the best solution effectively and efficiently from this distributed CBR network depends on a carefully designed query dispatching strategy. In this paper, we propose a fuzzy integral based approach to measure the competence of different CBR agents in the network and suggest three query dispatching policies which could be used to fulfill this task. They are: To-Top policy, Strong-Strong policy and Best-Committee policy. The experimental result shows that our proposed policies are comparatively better than the existing ones developed by Plaza and Ontañón.     

一个基于相似度计算的动态多维概念映射算法

本体作为一种领域知识结构化描述和推理的基础已经获得广泛认可．然而，本体本身是异构的．特别在多Agent系统、语义网、知识管理等开放环境下，如何协调不同领域的本体，甚至是同领域的本体的语义差异是一个基本问题．本文以相似度计算为基本思想提出了一个多维动态的概念映射算法S-Match．该算法可以根据不同的灵活性和准确性需求，在语言级、结构级、实例级和推理级四个维度上动态地进行本体概念映射．初步试验结果表明，S-Match算法在查全率和查准率方面要优于H-Match算法，并且比GLUE方法要求更少的专业知识支持．     

Reasoning situated in time I: basic concepts*

Abstract

The needs of a real-time reasoner situated in an environment may make it appropriate to view error-correction and non-monotonicity as much the same thing. This has led us to formulate situated (or step) logic, an approach to reasoning in which the formalism has a kind of real-time self-reference that affects the course of deduction itself. Here we seek to motivate this as a useful vehicle for exploring certain issues in commonsense reasoning. In particular, a chief drawback of more traditional logics is avoided: from a contradiction we do not have all wffs swamping the (growing) conclusion set. Rather, we seek potentially inconsistent, but nevertheless useful, logics where the real-time self-referential feature allows a direct contradiction to be spotted and corrective action taken, as part of the same system of reasoning. Some specific inference mechanisms for real-time default reasoning are suggested, notably a form of introspection relevant to default reasoning. Special treatment of ‘now’ and of contradictions are the main technical devices here. We illustrate this with a computer-implemented real time solution to R. Moore's Brother Problem.     

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．