Consistency-based and abductive diagnoses as generalised stable models

If realistic systems are to be successfully modelled and efficiently diagnosed using model-based techniques, a more expressive language than classical logic is required. In this paper, we present a definition of diagnosis which allows the use of a nonmonotonic construct, negation as failure, in the modelling language. This definition is based on thegeneralised stable model semantics of abduction. Furthermore, we argue that, if negation as failure is permitted in the modelling language, the distinction between abductive and consistency-based diagnosis is no longer clear. Our definition allows both forms of diagnosis to be expressed in a single framework. It also allows a single interference procedure to perform abductive or consistency-based diagnosis, as appropriate.This paper is an extended and revised version of ref. [29].     

基于LabWindows/CVI的ATS软件框架

基于LabWindows/CVI的自动测试系统软件框架使用配置文件实现软、硬件模块之间信息交换和资源的动态配置。在同一个软件主框架下实现了多个UUT测试程序的开发和调试。提供良好的调试维护界面,实现测试软件的模块化、TPS的可移植,大大提高了开发和调试的效率。TPS通过调用格式化类驱动间接调用物理仪器驱动,实现了仪器的可互换。     

Keyword Query over Error-Tolerant Knowledge Bases

With more and more knowledge provided by WWW, querying and mining the knowledge bases have attracted much research attention. Among all the queries over knowledge bases, which are usually modelled as graphs, a keyword query is the most widely used one. Although the problem of keyword query over graphs has been deeply studied for years, knowledge bases, as special error-tolerant graphs, lead to the results of the traditional defined keyword queries out of users’ satisfaction. Thus, in this paper, we define a new keyword query, called confident r-clique, specific for knowledge bases based on the r-clique definition for keyword query on general graphs, which has been proved to be the best one. However, as we prove in the paper, finding the confident r-cliques is #P-hard. We propose a filtering-and-verification framework to improve the search efficiency. In the filtering phase, we develop the tightest upper bound of the confident r-clique, and design an index together with its search algorithm, which suits the large scale of knowledge bases well. In the verification phase, we develop an efficient sampling method to verify the final answers from the candidates remaining in the filtering phase. Extensive experiments demonstrate that the results derived from our new definition satisfy the users’ requirement better compared with the traditional r-clique definition, and our algorithms are efficient.     

产品配置的知识表示

为按照用户的需要定制售卖产品的公司提供支持的基于知识的配置系统,产品配置器是人工智能技术的一个重要应用.本文首先描述了配置问题及其相关概念,然后分析了某些配置知识表示的优缺点,在此基础上提出了产品配置知识的图形表示方法,最后讨论了目前的一些热点研究问题.     

刻画基于模型的中心诊断*

虽然对基于模型的诊断存在一系列不同的逻辑定义,但所幸的是存在一个统一的抽象定义,它概括了以往的不同定义.在该定义基础上提出了基于模型的中心诊断的概念.通过刻画基于模型的中心诊断过程,论证了基于模型的中心诊断与本原蕴含/蕴含式的直接关系,从而将其理论结果与ATMS(assumption-based truth maintenance system)这类算法联系起来.进一步指出,对基于一致性中心诊断的刻画仅仅是文中所给出的刻画的一个特殊情形.     

基于结构的弱诊断模型

本文首先给出一种弱诊断模型，利用系统的结构信息，快速得到系统的弱诊断，在不丢失解的情况下缩小诊断空间，以提高诊断效率。对仅知道系统的整体功能和物理结构系统，无法进行一致性诊断或更强的诊断，我们仍然可以对其进行弱诊断。其次，我们给出了弱诊断、一致诊断、溯因诊断间的关系。最后，根据诊断和真正故障元件集合间的关系，定义了诊断的可采纳性，并证明了弱诊断、一致诊断是可采纳的。     

Discovering regularities from knowledge bases

Knowledge bases open new horizons for machine learning research. One challenge is to design learning programs to expand the knowledge base using the knowledge that is currently available. This article addresses the problem of discovering regularities in large knowledge bases that contain many assertions in different domains. the article begins with a definition of regularities and gives the motivation for such a definition. It then outlines a framework that attempts to integrate induction with knowledge. Although the implementation of the framework currently uses only a statistical method for confirming hypotheses, its application to a real knowledge base has shown some encouraging and interesting results. © 1992 John Wiley & Sons, Inc.     

基于一致性的中心诊断及中心溯因诊断

本文对溯因诊断的过程和中心溯因诊断的过程进行了刻画。将求中心溯因诊断的过程清晰地分为与领域有关的冲突识别及与领域无关的候选产生两大步骤。不仅指出了基于一致性的中心诊断与中心溯因诊断之间的关系，而且论证了中心溯因诊断与本原蕴含／蕴含式的直接关系。显示出基于一致性的中心诊断空间和中心溯因诊断空间不仅可同时计算，而且可用ＡＴＭＳ这类算法来计算，从而将我们的理论结果与实现联系起来。     

基于计算解语义的逻辑程序测试和调试框架

逻辑程序开发过程中需要花费大量的时间用以程序调试,原因之一是调试通常包含大量的用户交互.减少对调试过程不必要的调用能够提高软件开发的效率.程序测试中得到的由同一个错误引发的多个症状是引发对调试过程不必要调用的因素之一.给出了一种逻辑程序测试和调试框架,其中测试用例的生成、症状的发现和调试(包括诊断和改错)交叉进行,由同一个错误引发的症状只有一个可引发调试过程执行,并以此方式避免了对调试过程不必要的调用.然后,利用一种基于约束的Prolog计算解语义,该框架被实例化为一种Prolog程序的测试和调试算法,本实例表明了该算法的应用.     

基于一致性的最小正常诊断及其应用

本文给出了基于一致性的最小正常诊断的概念，并将它与基于一致性的最小反常诊断加以区别。证明了若将系统描述限制在故障理论或在故障理论中扩展有限的正常行为知识，那么使用最小正常诊断能够刻画基于一致性的诊断空间，即最小正常诊断假设成立。本文还指出：使用最小正常诊断可以缩小只针对正常行为模型的最小反常诊断所产生的诊断空间，帮助我们找到真正的故障。文中最后还指出了最小正常诊断所适合的诊断任务及诊断领域。     

Integrated Vehicle Configuration System—Connecting the domains of mass customization

Configuration design for mass customized vehicles necessitates the coordination of customer requirements, product characteristics, production processes, and logistics networks, in order to achieve rapid response to customer orders. Existing product configurators are mainly used as sales tools, and fail to account for the requirements of the entire customer order fulfillment process. In this regard, this paper proposes an Integrated Vehicle Configuration System (IVCS) to facilitate customer order processing based on information from multiple domains in a mass customization environment. An IVCS business model is proposed to incorporate the decision factors for configuration design related to different planning stages. The business model is supported by a comprehensive ontology framework, which enhances communications between different stakeholders involved in the order fulfillment process. The configuration approach is based on combinations of selective and generative rules and can be integrated with existing ERP systems. It also provides mechanisms to handle configuration rules that allow users to convert soft preferences into product specifications, bill-of-materials, and, furthermore, into logistics configurations. An example of a computerized configuration system showcases the process from customer engineering to design and production.     

Model-based diagnosis of spreadsheet programs: a constraint-based debugging approach

Spreadsheet programs are probably the most successful example of end-user software development tools and are used for a variety of purposes. Like any type of software, they are prone to error, in particular as they are usually developed by non-programmers. While various techniques exist to support the developer in finding errors in procedural programs, the tool support for spreadsheet debugging is still limited. In this paper, we show how techniques from model-based diagnosis can be applied and extended for spreadsheet debugging by translating the relevant parts of a spreadsheet to a constraint satisfaction problem. We additionally propose both problem-specific and generalizable extensions to the classical diagnosis algorithms which help to detect potential problems in a spreadsheet based on user-provided test cases more efficiently. The proposed techniques were integrated into a modular framework for spreadsheet debugging and evaluated with respect to scalability based on a number of real-world and artificially created spreadsheets. An additional error detection exercise involving 24 subjects was performed to assess the general applicability of such advanced spreadsheet debugging techniques for end users.     

从经典逻辑知识构建ASP知识库的新方法

回答集程序设计(ASP)是一种主流的非单调知识表示工具。为了能够在利用ASP求解问题过程中使用现有的以经典逻辑表示的知识,给出了一种把以谓词逻辑公式表示的约束型知识和定义型知识转化为ASP程序或知识库的新方法,并以实例说明了其有效性。该方法满足转化后ASP程序的回答集与原公式集的模型具有一一对应关系。在实际应用中,该方法提供了一项从现存的以谓词逻辑为表示语言的知识库,构建以ASP为知识表示语言的非单调知识库的技术。     

Reasoning in inconsistent knowledge bases

Databases and knowledge bases could be inconsistent in many ways. For example, during the construction of an expert system, we may consult many different experts. Each expert may provide us with a group of rules and facts which are self-consistent. However, when we coalesce the facts and rules provided by these different experts, inconsistency may arise. Alternatively, knowledge bases may be inconsistent due to the presence of some erroneous information. Thus, a framework for reasoning about knowledge bases that contain inconsistent information is necessary. However, existing frameworks for reasoning with inconsistency do not support reasoning by cases and reasoning with the law of excluded middle (“everything is either true or false”). In this paper, we show how reasoning with cases, and reasoning with the law of excluded middle may be captured. We develop a declarative and operational semantics for knowledge bases that are possibly inconsistent. We compare and contrast our work with work on explicit and non-monotonic modes of negation in logic programs and suggest under what circumstances one framework may be preferred over another     

A fuzzy rule-based meta-scheduler with evolutionary learning for grid computing

Grid computing is increasingly emerging as a promising platform for large-scale problems solving in science, engineering and technology. Nevertheless, a major effort is still required to harness the high potential performance of such computational framework and in this sense, an important challenge is to develop new strategies that efficiently address scheduling on the distributed, heterogeneous and shared environment of grids. Fuzzy rule-based systems (FRBSs) models are dynamic and are currently attracting the interest of scheduling research community to obtain near-optimal solutions on grids. However, FRBSs performance is strongly related to the quality of their knowledge bases and thus, with the knowledge acquisition process. Due to the inherent dynamic nature and the typical complex search spaces of grids, automatically finding a high-quality knowledge base that accurately describes the fuzzy system is extremely relevant. In this work, we propose a scheduling system for grids considering a novel learning strategy inspired by Michigan and Pittsburgh approaches that applies genetic algorithms (GAs) to evolve the fuzzy rule bases and improves the classical learning strategies in terms of computational effort and convergence behaviour. In addition, experimental results show that the proposed schema significantly outperforms other extensively used scheduling strategies.