Designing intelligent litigation support tools: The IKBALS perspective

In the legal domain, it is rare to find solutions to problems by simply applying algorithms or invoking deductive rules in some knowledge‐based program. Instead, expert practitioners often supplement domain‐specific knowledge with field experience. This type of expertise is often applied in the form of an analogy. This research proposes to combine both reasoning with precedents and reasoning with statutes and regulations in a way that will enhance the statutory interpretation task. This is being attempted through the integration of database and expert system technologies. Case‐based reasoning is being used to model legal precedents while rule‐based reasoning modules are being used to model the legislation and other types of causal knowledge. It is hoped to generalise these findings and to develop a formal methodology for integrating case‐based databases with rule‐based expert systems in the legal domain.     

ASHSD-II: A computational model for litigation support

Abstract: In this paper a hybrid knowledge-based system which exploits both rule-based reasoning (RBR) and case-based reasoning (CBR) is presented. The issues of RBR and CBR in general in the context of legal knowledge-based systems and legislation in rule form and previously-decided cases in an interconnected graph form are discussed. It is possible for the user to select either reasoning method (RBR or CBR), or indicate no preference. The rule base of this system consists of two types of rule. The first type of rule determines which options are legally applicable. The second type indicates how the courts are likely to act within the range of options available, which is determined by the first type of rule. When CBR is selected, the system uses the features of previously-decided cases to select the most similar cases to the situation that is described in the input and displays their details of decisions. In case of the selection of no preference option, the system applies RBR and CBR method separately, and then presents results based on an automated relative rating of the qualities of the RBR (based on the second type of rules) and CBR advice. These ideas have been implemented in a prototype system, known as A dvisory S upport for H ome S ettlement in D ivorce (ASHSD-II).     

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.     

Bankruptcy support system: taking advantage of information retrieval and case-based reasoning

This paper is a presentation of an on-going work in which we attempt to take advantage of information retrieval (IR) and artificial intelligence techniques combined. It is an application of case-based reasoning (CBR) with an automatic indexing IR component in the legal domain of bankruptcy law. The model is based on our intuition of how lawyers go about doing their legal research and reasoning tasks in case law. We take advantage of the built-in knowledge contained in the carefully prepared statute text in a front-end processor and classification component to the CBR system. Our end result is an IR–CBR bankruptcy support system (BanXupport).     

基于语义推理的DSS模型研究与应用

基于语义网理论建立基于语义推理的DSS模型。该模型引用W3C提出的资源描述框架（RDF）以描述业务领域中各要素,形成领域本体。并对领域中业务数据进行语义标注,构建事实库。在所构建事实库的基础上,系统根据预设规则进行推理,以达到决策支持的目的。最后基于以上方法对施工项目设备推荐领域进行实例建模,建立设备推荐系统。该系统将Semantic Web与Ontology理论引入到施工项目领域,以解决施工项目过程中存在的设备选型推荐问题,并作为语义Web理论在该领域内应用的一次尝试。     

一种CBR与RBR相结合的快速预案生成系统

将范例推理(case based reasoning,CBR)与规则推理(rule based reasoning,RBR)两种人工智能技术相结合,实现一种快速预案生成系统.它有效地解决了单纯RBR系统在预案生成过程中的时间延迟缺陷和知识库难以获取的瓶颈.通过CBR工具,能够把以前发生的紧急事件和解决方案生成预案.一旦新的事件发生,首先从预案库中进行案例的相似性检索,如果没有检索到预案或者检索到的预案匹配度很低,再采用RBR系统对紧急事件进行规则推理,然后把推理结果重新存入预案库.实验数据表明,这种方法对单纯RBR系统在时间响应上进行了有效的优化.另外,因为案例的获取比专家系统推理规则的获取容易得多,它同时解决了RBR系统推理规则难以获取的瓶颈.根据这种思想,实现了CBR与RBR结合的快速预案生成系统.目前,它已经应用到抗洪抢险的预案生成和城市应急联动的决策支持上,效果表明它在预案生成速度以及实际可操作性上都具有明显优势.     

Issue spotting in CHASER

For any system that uses previous experience to solve problems in new situations, it is necessary to identify the features in the situation that should match features in the previous cases through some process ofsituation analysis. In this paper, we examine this problem in the legal domain, where lawyers know it asissue spotting. In particular, we present an implementation of issue spotting in CHASER, a legal reasoning system that works in the domain of tort law.This approach is a compromise between generality and efficiency, and is applicable to a range of problems and domains besides legal reasoning. In particular, it presents a principled way to use multiple cases for a single problem by exploiting the inherent structure present in many domains.This work has been supported in part by the National Science Foundation, grant IRI-9110961.     

An approach to fuzzy default reasoning for function approximation

This paper discusses fuzzy reasoning for approximately realizing nonlinear functions by a small number of fuzzy if-then rules with different specificity levels. Our fuzzy rule base is a mixture of general and specific rules, which overlap with each other in the input space. General rules work as default rules in our fuzzy rule base. First, we briefly describe existing approaches to the handling of default rules in the framework of possibility theory. Next, we show that standard interpolation-based fuzzy reasoning leads to counterintuitive results when general rules include specific rules with different consequents. Then, we demonstrate that intuitively acceptable results are obtained from a non-standard inclusion-based fuzzy reasoning method. Our approach is based on the preference for more specific rules, which is a commonly used idea in the field of default reasoning. When a general rule includes a specific rule and they are both compatible with an input vector, the weight of the general rule is discounted in fuzzy reasoning. We also discuss the case where general rules do not perfectly but partially include specific rules. Then we propose a genetics-based machine learning (GBML) algorithm for extracting a small number of fuzzy if-then rules with different specificity levels from numerical data using our inclusion-based fuzzy reasoning method. Finally, we describe how our approach can be applied to the approximate realization of fuzzy number-valued nonlinear functions     

Autonomic self healing and recovery informed by environment knowledge

An important goal of autonomic computing is the development of computing systems that are capable of self healing with a minimum of human intervention. Typically, recovery from even a simple fault will require knowledge of the environment in which a computing system operates. To meet this need, we present an approach to self healing and recovery informed by environment knowledge that combines case based reasoning (CBR) and rule based reasoning. Specifically, CBR is used for fault diagnosis and rule based reasoning for fault remediation, recovery, and referral. We also show how automated information gathering from available sources in a computing system’s environment can increase problem solving efficiency and help to reduce the occurrence of service failures. Finally, we demonstrate the approach in an intelligent system for fault management in a local printer network.     

A model of legal reasoning with cases incorporating theories and values

Reasoning with cases has been a primary focus of those working in AI and law who have attempted to model legal reasoning. In this paper we put forward a formal model of reasoning with cases which captures many of the insights from that previous work. We begin by stating our view of reasoning with cases as a process of constructing, evaluating and applying a theory. Central to our model is a view of the relationship between cases, rules based on cases, and the social values which justify those rules. Having given our view of these relationships, we present our formal model of them, and explain how theories can be constructed, compared and evaluated. We then show how previous work can be described in terms of our model, and discuss extensions to the basic model to accommodate particular features of previous work. We conclude by identifying some directions for future work.     

Strategies in Human Nonmonotonic Reasoning

Although humans seem adept at drawing nonmonotonic conclusions, the nonmonotonic reasoning systems that researchers develop are complex and do not function with such ease. This paper explores people's reasoning processes in nonmonotonic problems. To avoid the problem of people's conclusions being based on knowledge rather than on some reasoning process, we developed a scenario about life on another planet. Problems were chosen to allow the systematic study of people's understanding of strict and nonstrict rules and their interactions. We found that people had great difficulty reasoning and we identified a number of negative factors influencing their reasoning. We also identified three positive factors which, if used consistently, would yield rational and coherent reasoning—but no subject achieved total consistency. (Another possible positive factor, specificity, was considered but we found no evidence for its use.) It is concluded that nonmonotonic reasoning is hard. When people need to reason in a domain where they have no preconceived ideas, the foundation for their reasoning is neither coherent nor rational. They do not use a nonmonotonic reasoning system that would work regardless of content. Thus, nonmonotonic reasoning systems that researchers develop are expected to do more reasoning than humans actually do!     

基于规则和事例混合推理的设计型专家系统模型研究

该文根据设计活动的特点，在综述设计事例表示，组织与检索模型基础上详细讨论了基于规则混合推理专家系统结构模型，以及在收音机起落架起落机设计系统ＬＥＤＥＳ上具体应用。     

Extraction and representation of facts from legal briefs

In knowledge-based consultation systems, the quality of the advice rendered depends on the techniques employed to represent the domain knowledge, the explanation generating capabilities, and the control strategies utilized during the consultative advice stage. The ability to understand the problem is more crucial in providing effective consultation. In this work, the emphasis is on understanding and the consequent formulation of a plausible internal representation of legal briefs. The system developed, SIFTER, reads the given input text from a legal practitioner's point of view and retrieves from it those facts that are relevant to the particular type of case on hand. In other words, it uses the domain specific knowledge to identify the type of case and to yank out the necessary information pertaining to the case. The SIFTER generates a noun-phrase processed form of the input which contains pseudo names for the proper-nouns, dates and time-intervals. The verbs in the processed input are used to check whether the case specific events have occurred or not and then the appropriate fact-containing noun-phrases are used to instantiate the relevant legal variables and, hence, to construct an internal representation of the given problem which can then be readily used by the consultative advice stage of a problem solver or analyzer. The implementation has been done in LISP culling the required domain knowledge from the Industrial Dispute Act of India.     

MEMORY ORGANIZATION AS THE MISSING LINK BETWEEN CASE-BASED REASONING AND INFORMATION RETRIEVAL IN BIOMEDICINE

Mémoire proposes a general framework for reasoning from cases in biology and medicine. Part of this project is to propose a memory organization capable of handling large cases and case bases as occur in biomedical domains. This article presents the essential principles for an efficient memory organization based on pertinent work in information retrieval (IR). IR systems have been able to scale up to terabytes of data taking advantage of large databases research to build Internet search engines. They search for pertinent documents to answer a query using term-based ranking and/or global ranking schemes. Similarly, case-based reasoning (CBR) systems search for pertinent cases using a scoring function for ranking the cases. Mémoire proposes a memory organization based on inverted indexes which may be powered by databases to search and rank efficiently through large case bases. It can be seen as a first step toward large-scale CBR systems, and in addition provides a framework for tight cooperation between CBR and IR.     

A legal case OWL ontology with an instantiation of Popov v. Hayashi

The paper provides an OWL ontology for legal cases with an instantiation of the legal case Popov v. Hayashi. The ontology makes explicit the conceptual knowledge of the legal case domain, supports reasoning about the domain, and can be used to annotate the text of cases, which in turn can be used to populate the ontology. A populated ontology is a case base which can be used for information retrieval, information extraction, and case based reasoning. The ontology contains not only elements for indexing the case (e.g. the parties, jurisdiction, and date), but as well elements used to reason to a decision such as argument schemes and the components input to the schemes. We use the Protégé ontology editor and knowledge acquisition system, current guidelines for ontology development, and tools for visual and linguistic presentation of the ontology.     

Enhancing the Effectiveness of Interactive Case-Based Reasoning with Clustering and Decision Forests

In interactive case-based reasoning, it is important to present a small number of important cases and problem features to the user at one time. This goal is difficult to achieve when large case bases are commonplace in industrial practice. In this paper we present our solution to the problem by highlighting the interactive user- interface component of the CaseAdvisor system. In CaseAdvisor, decision forests are created in real time to help compress a large case base into several small ones. This is done by merging similar cases together through a clustering algorithm. An important side effect of this operation is that it allows up-to-date maintenance operations to be performed for case base management. During the retrieval process, an information-guided subsystem can then generate decision forests based on users' current answers obtained through an interactive process. Possible questions to the user are carefully analyzed through information theory. An important feature of the system is that case-base maintenance and reasoning are integrated in a seamless whole. In this article we present the system architecture, algorithms as well as empirical evaluations.     

Incremental application of knowledge to continuously arriving time-oriented data

In our previous work, we introduced a computational architecture that effectively supports the tasks of continuous monitoring and of aggregation querying of complex domain meaningful time-oriented concepts and patterns (temporal abstractions), in environments featuring large volumes of continuously arriving and accumulating time-oriented raw data. Examples include provision of decision support in clinical medicine, making financial decisions, detecting anomalies and potential threats in communication networks, integrating intelligence information from multiple sources, etc. In this paper, we describe the general, domain-independent but task-specific problem-solving method underling our computational architecture, which we refer to as incremental knowledge-based temporal abstraction (IKBTA). The IKBTA method incrementally computes temporal abstractions by maintaining persistence and validity of continuously computed temporal abstractions from arriving time-stamped data. We focus on the computational framework underlying our reasoning method, provide well-defined semantic and knowledge requirements for incremental inference, which utilizes a logical model of time, data, and high-level abstract concepts, and provide a detailed analysis of the computational complexity of our approach.     

Comparing Alternatives in the law*

This paper argues the thesis that a particular style of reasoning, qualitative comparative reasoning (QCR), plays a role in at least three areas of legal reasoning that are central in AI and law research, namely legal theory construction, case-based reasoning in the form of case comparison, and legal proof. The paper gives an informal exposition of one particular way to deal with QCR, based on the author’s previous work on reason-based logic (RBL). Then it contains a substantially adapted formalisation of RBL, to make RBL suitable for dealing with QCR. The paper concludes with a brief discussion of related work. *This paper is based on the chapters 3 and 4 of Hage 2005.     

Visual analogy: Viewing analogical retrieval and mapping as constraint satisfaction problems

The core issue of analogical reasoning is the transfer of relational knowledge from a source case to a target problem. Visual analogical reasoning pertains to problems containing only visual knowledge. Holyoak and Thagard proposed that the retrieval and mapping tasks of analogy in general can be productively viewed as constraint satisfaction problems, and provided connectionist implementations of their proposal. In this paper, we reexamine the retrieval and mapping tasks of analogy in the context of diagrammatic cases, representing the spatial structure of source and target diagrams as semantic networks in which the nodes represent spatial elements and the links represent spatial relations. We use a method of constraint satisfaction with backtracking for the retrieval and mapping tasks, with subgraph isomorphism over a particular domain language as the similarity measure. Results in the domain of 2D line drawings suggest that at least for this domain the above method is quite promising.     

The role of context in case-based legal reasoning: teleological, temporal, and procedural

Computational models of relevance in case-based legal reasoning have traditionallybeen based on algorithms for comparing the facts and substantive legal issues of aprior case to those of a new case. In this paper we argue that robust models ofcase-based legal reasoning must also consider the broader social and jurisprudentialcontext in which legal precedents are decided. We analyze three aspects of legalcontext: the teleological relations that connect legal precedents to the socialvalues and policies they serve, the temporal relations between prior andsubsequent cases in a legal domain, and the procedural posture of legal cases,which defines the scope of their precedential relevance. Using real examples drawnfrom appellate courts of New York and Massachusetts, we show with the courts' ownarguments that the doctrine of stare decisis (i.e., similar facts should lead to similar results) is subject to contextual constraints and influences. For each of the three aspects of legal context, we outline an expanded computational framework for case-based legal reasoning that encompasses the reasoning of the examples, and provides a foundation for generating a more robust set of legal arguments.