On the associations between variables in expert systems including default relations

A central function of the knowledge base of an expert system is to represent the associations between variables relevant to the domain of the system. We suggest that an expert's lack of complete knowledge of the underlying domain requires more sophisticated methods to represent these associations than used in the past. We discuss the appropriateness of the theory of approximate reasoning as a tool for handling knowledge which is nonspecific. We look at a methodology, based upon the theory of approximate reasoning, for representing and reasoning with default knowledge.     

A methodology for designing systems to reason with legal cases using Abstract Dialectical Frameworks

This paper presents a methodology to design and implement programs intended to decide cases, described as sets of factors, according to a theory of a particular domain based on a set of precedent cases relating to that domain. We use Abstract Dialectical Frameworks (ADFs), a recent development in AI knowledge representation, as the central feature of our design method. ADFs will play a role akin to that played by Entity–Relationship models in the design of database systems. First, we explain how the factor hierarchy of the well-known legal reasoning system CATO can be used to instantiate an ADF for the domain of US Trade Secrets. This is intended to demonstrate the suitability of ADFs for expressing the design of legal cased based systems. The method is then applied to two other legal domains often used in the literature of AI and Law. In each domain, the design is provided by the domain analyst expressing the cases in terms of factors organised into an ADF from which an executable program can be implemented in a straightforward way by taking advantage of the closeness of the acceptance conditions of the ADF to components of an executable program. We evaluate the ease of implementation, the performance and efficacy of the resulting program, ease of refinement of the program and the transparency of the reasoning. This evaluation suggests ways in which factor based systems, which are limited by taking as their starting point the representation of cases as sets of factors and so abstracting away the particular facts, can be extended to address open issues in AI and Law by incorporating the case facts to improve the decision, and by considering justification and reasoning using portion of precedents.     

基于本体的应急救援知识库构建及其应用

针对专家系统在应急救援领域应用中存在的知识表示及推理等问题,采用基于本体的知识表示方法与基于Jena的规则推理引擎,参考简单知识工程方法论与Jena规则语法建立一个高速公路应急救援本体与推理规则,实现本体知识库的推理。将该知识库应用于高速公路应急救援系统中,结果表明其具备解决实际问题的能力;有利于领域知识的共享与重用;促进了专家系统在高速公路应急救援领域的发展。     

The IKBALS project: Multi-modal reasoning in legal knowledge based systems

In attempting to build intelligent litigation support tools, we have moved beyond first generation, production rule legal expert systems. Our work integrates rule based and case based reasoning with intelligent information retrieval.When using the case based reasoning methodology, or in our case the specialisation of case based retrieval, we need to be aware of how to retrieve relevant experience. Our research, in the legal domain, specifies an approach to the retrieval problem which relies heavily on an extended object oriented/rule based system architecture that is supplemented with causal background information. We use a distributed agent architecture to help support the reasoning process of lawyers.Our approach to integrating rule based reasoning, case based reasoning and case based retrieval is contrasted to the CABARET and PROLEXS architectures which rely on a centralised blackboard architecture. We discuss in detail how our various cooperating agents interact, and provide examples of the system at work. The IKBALS system uses a specialised induction algorithm to induce rules from cases. These rules are then used as indices during the case based retrieval process.Because we aim to build legal support tools which can be modified to suit various domains rather than single purpose legal expert systems, we focus on principles behind developing legal knowledge based systems. The original domain chosen was theAccident Compensation Act 1989 (Victoria, Australia), which relates to the provision of benefits for employees injured at work. For various reasons, which are indicated in the paper, we changed our domain to that ofCredit Act 1984 (Victoria, Australia). This Act regulates the provision of loans by financial institutions.The rule based part of our system which provides advice on the Credit Act has been commercially developed in conjunction with a legal firm. We indicate how this work has lead to the development of a methodology for constructing rule based legal knowledge based systems. We explain the process of integrating this existing commercial rule based system with the case base reasoning and retrieval architecture.     

A new use of an automated reasoning assistant: Open questions in equivalential calculus and the study of infinite domains

The field of automated reasoning is an outgrowth of the field of automated theorem proving. The difference in the two fields is not so much in the procedures on which they rest, but rather in the way the corresponding programs are used. Here we present a comprehensive treatment of the use of an automated reasoning program to answer certain previously open questions from equivalential calculus. The questions are answered with a uniform method that employs schemata to study the infinite domain of theorems deducible from certain formulas. We include sufficient detail both to permit the work to be duplicated and to enable one to consider other applications of the techniques. Perhaps more important than either the results or the methodology is the demonstration of how an automated reasoning program can be used as an assistant and a colleague. Precise evidence is given of the nature of this assistance.     

支持产生式系统并行推理的动态推理网

产生式系统作为人工智能领域的重要分支,已得到较为成熟的应用.同时也不断面临一些新的需求.针对产生式系统并行处理能力的需求,提出一种基于动态推理网(DRN)的知识库构造方法,并介绍整个知识库的描述与构造方式,以及相关算法的实现.在此基础上支持产生式系统的并行推理功能.     

基于知识Petri网和归结规则的推理方法

本文提出了一种基于知识Petri网和归结规则的推理方法.通过知识Petri网描述命题逻辑知识库,将归结规则映射到知识Petri网上,根据库所和变迁的连接关系,定义了知识Petri网中的归结结构.利用归结结构,给出了基于知识Petri网的归结推理算法和扩展知识库的推理算法,并利用Wumpus实例验证了推理算法.该推理方法是可靠且完备的,能够利用知识Petri网的网络结构降低计算复杂性.     

扩展的动态逻辑程序及其在产品个性化推荐中的应用

动态逻辑程序能很好的处理知识库更新问题, 但它不能描述和处理具有偏好的知识更新问题. 因此, 本文在动态逻辑程序的基础上, 提出了一种新的扩展的动态逻辑程序, 它通过对规则头部使用有序析取的方法使其能够描述和处理具有偏好的知识更新问题, 进一步增强了知识的表达和推理能力, 并且定义了其最优回答集语义. 同时将这种新的扩展的动态逻辑程序应用于产品推荐系统中, 使用户获得的推荐信息具有个性化特点, 达到个性化推荐的目的. 最后以一个产品个性化推荐实例讨论扩展的动态逻辑程序在产品个性化推荐中的应用.     

A hierarchical knowledge based system for airplane classification

Airplane classification is used as an application domain to illustrate how hierarchical reasoning on large knowledge bases can be implemented. The knowledge base is organized as a two-dimensional hierarchy: one dimension corresponds to the levels of complexity often seen in computer vision, and the other dimension corresponds to the complexity of hypothesis used in the reasoning process. Reasoning proceeds top-down, from more abstract levels with fewer details toward levels with more details. Whenever possible, with the help of domain knowledge, decision is taken at a higher level, which significantly reduces processing time. A software package called RuBICS (Rule-Based Image Classification System) is described, and some examples of airplane classification are shown     

Hybrid approach using case-based reasoning and rule-based reasoning for domain independent clinical decision support in ICU

This paper presents a hybrid approach of case-based reasoning and rule-based reasoning, as an alternative to the purely rule-based method, to build a clinical decision support system for ICU. This enables the system to tackle problems like high complexity, low experienced new staff and changing medical conditions. The purely rule-based method has its limitations since it requires explicit knowledge of the details of each domain of ICU, such as cardiac domain hence takes years to build knowledge base. Case-based reasoning uses knowledge in the form of specific cases to solve a new problem, and the solution is based on the similarities between the new problem and the available cases. This paper presents a case-based reasoning and rule-based reasoning based model which can provide clinical decision support for all domains of ICU unlike rule-based inference models which are highly domain knowledge specific. Experiments with real ICU data as well as simulated data clearly demonstrate the efficacy of the proposed method.     

Comparison of a Deductive Database with a Semantic Web reasoning engine

Knowledge engineering is a discipline concerned with constructing and maintaining knowledge bases to store knowledge of various domains and using the knowledge by automated reasoning techniques to solve problems in domains that ordinarily require human logical reasoning. Therefore, the two key issues in knowledge engineering are how to construct and maintain knowledge bases, and how to reason out new knowledge from known knowledge effectively and efficiently. The objective of this paper is the comparison and evaluation of a Deductive Database system (ConceptBase) with a Semantic Web reasoning engine (Racer). For each system a knowledge base is implemented in such a way that a fair comparison can be achieved. Issues such as documentation, feasibility, expressiveness, complexity, distribution, performance and scalability are investigated in order to explore the advantages and shortcomings of each system.     

面向开放文本的逻辑推理知识抽取与事件影响推理探索

开放文本中蕴含着大量的逻辑性知识,以刻画事物之间逻辑传导关系的逻辑类知识库是推动知识推理发展的重要基础,研发大规模逻辑推理知识库有助于支持由实体或事件等传导驱动的决策任务。该文围绕逻辑推理知识库,论述了知识库的概念、类别和基本构成,提出了一种面向大规模开放文本的实体描述、事件因果逻辑知识快速抽取方法;面向金融领域,探索了一套基于逻辑推理知识库的可解释性路径推理方法和金融实体影响生成系统。算法模型和系统均取得了不错的效果。     

Using analogical reasoning to design buildings

This paper presents a methodology for identifying the relevant design elements for the synthesis of new structural designs using previous design situations and their corresponding solutions. The study is a reflection of the observation that engineers use related experience when solving new problems. The methodology is an application of transformational analogy, a form of analogical reasoning.A prototype system STRUPLE has been designed to implement the methodology, making use of knowledge based expert systems techniques. The emphasis of STRUPLE differs from that of traditional expert systems in that the latter only use formalized o or compiled knowledge, whereas STRUPLE uses an experience data base as a knowledge supplement.     

Redundancy detection in semistructured case bases

With the dramatic proliferation of case-based reasoning systems in commercial applications, many case bases are now becoming legacy systems. They represent a significant portion of an organization's assets, but they are large and difficult to maintain. One of the contributing factors is that these case bases are often large and yet unstructured or semistructured; they are represented in natural language text. Adding to the complexity is the fact that the case bases are often authored and updated by different people from a variety of knowledge sources, making it highly likely for a case base to contain redundant and inconsistent knowledge. We present methods and a system for maintaining large and semistructured case bases. We focus on a difficult problem in case base maintenance: redundancy detection. This problem is particularly pervasive when one deals with a semistructured case base. We discuss an information retrieval-based algorithm and an implemented system for solving this problem. As the ability to contain the knowledge acquisition problem is of paramount importance, our method allows one to express relevant domain expertise for detecting redundancy naturally and effortlessly. Empirical evaluations of the system demonstrate the effectiveness of the methods in several large domains     

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.     

基于本体的分布式实例推理技术研究

为了克服单一实例库知识的局限性,实现分布式环境下多数据源的知识重用和共享,提出了一个分布式实例推理系统框架.系统通过本体服务器建立和维护实例库之间的本体知识,其中基本本体为知识的表示提供了全局约束和基础,实例推理服务器可以在基本本体框架下定义领域本体来灵活表达各自的领域知识,而本体目录则为知识的检索提供了向导.本体的引入解决了不同实例库之间知识的互理解和互操作性,能够有效地实现多实例库的协同推理.系统采用Web Service技术构建,是一个开放的系统框架,具有很强的可扩展性.     

Developing maintainable expert systems using case-based reasoning

Abstract: Maintainability problems associated with traditional software systems are exacerbated in rule-based systems. The very nature of that approach — separation of control knowledge and data-driven execution — hampers maintenance. While there are widely accepted techniques for maintaining conventional software, the same is not true for rule-based systems. In most situations, both a knowledge engineer and a domain expert are necessary to update the rules of a rule-based system. This paper presents, first, an overview of the software engineering techniques and object-oriented methods used in maintaining rule-based systems. It then discusses alternate paradigms for expert system development. The benefits of using case-based reasoning (from the maintenance point of view) are illustrated through the implementation of a case-based scheduler. The main value of the scheduler is that its knowledge base can be modified by the expert without the assistance of a knowledge engineer. Since changes in application requirements can be given directly to the system by the expert, the effort of maintaining the knowledge base is greatly reduced.     

Probabilistic knowledge representation using the principle of maximum entropy and Gröbner basis theory

An often used methodology for reasoning with probabilistic conditional knowledge bases is provided by the principle of maximum entropy (so-called MaxEnt principle) that realises an idea of least amount of assumed information and thus of being as unbiased as possible. In this paper we exploit the fact that MaxEnt distributions can be computed by solving nonlinear equation systems that reflect the conditional logical structure of these distributions. We apply the theory of Gröbner bases that is well known from computational algebra to the polynomial system which is associated with a MaxEnt distribution, in order to obtain results for reasoning with maximum entropy. We develop a three-phase compilation scheme extracting from a knowledge base consisting of probabilistic conditionals the information which is crucial for MaxEnt reasoning and transforming it to a Gröbner basis. Based on this transformation, a necessary condition for knowledge bases to be consistent is derived. Furthermore, approaches to answering MaxEnt queries are presented by demonstrating how inferring the MaxEnt probability of a single conditional from a given knowledge base is possible. Finally, we discuss computational methods to establish general MaxEnt inference rules.