Urban planning and artificial intelligence: The Urbys system

The subject of this paper is the complementary use of both a data base and an expert system in the analysis of urban areas in territorial planning.After a discussion of urban reasoning, this paper then states the fundamental principles of the URBYS system: —storage of the urban planner's knowledge; —the use of this knowledge by the expert system to assist in decision-making; —output of information from the urban data base to the expert system.A special effort was made to facilitate the use of this system and its closeness to the expert method by an easy modification of URBYS' knowledge without the overall coherence being affected.     

Knowledge organization and its role in representation and interpretation for time-varying data: the ALVEN system

The so-called "first generation" expert systems were rule-based and offered a successful framework for building applications systems for certain kinds of tasks. Spatial, temporal, and causal reasoning, knowledge abstractions, and structuring are among topics of research for "second generation" expert systems. It is proposed that one of the keys for such research is knowledge organization. Knowledge organization determines control structure design, explanation and evaluation capabilities for the resultant knowledge base, and has strong influence on system performance. We are exploring a framework for expert system design that focuses on knowledge organization, for a specific class of input data, namely, continuous, time-varying data (image sequences or other signal forms). Such data are rich in temporal relationships as well as temporal changes of spatial relations, and are thus a very appropriate testbed for studies involving spatio-temporal reasoning. In particular, the representation formalism specifies the semantics of the organization of knowledge classes along the relationships of generalization/specialization, decomposition/aggregation, temporal precedence, instantiation, and expectation-activated similarity. Á hypothesize-and-test control structure is driven by the class organizational principles, and includes several interacting dimensions of search (data-driven, model-driven, goal-driven temporal, and failure-driven search). The hypothesis ranking scheme is based on temporal cooperative computation, with hypothesis "fields of influence" being defined by the hypothesis' organizational relationships. This control structure has proven to be robust enough to handle a variety of interpretation tasks for continuous temporal data. A particular incarnation, the ALVEN system, for left ventricular performance assessment from X-ray image sequences, will be summarized in this paper.     

动态系统的模糊时序知识表达、推理及其应用

本文以工业动态系统的知识表达和问题求解为背景,提出了一个描述动态系统行为的模 糊时序模型、相应的时序匹配与数值匹配和推理方法.并将它们应用于建立催化裂化装置的 故障诊断专家系统.这一研究有利于沟通人工智能与控制理论的联系.     

实时数据及时态知识的表示

在实时专家系统中引入时间概念后，涉及时间的知识的表示成为值得注意的问题，本文在分析一些典型事例的基础上，着重探讨以下３个问题，如时间数据的有效表示；知识中时间特征的表示；推理中知识的时间一致性检验等.     

A fuzzy expert database system

Fuzzy concepts always exist in much of human reasoning as well as decision making. This paper presents a fuzzy expert database system which is an integration of a fuzzy expert system building tool called SYSTEM Z-II and a database management system called Rdb/VMS. This system is able to extract fuzzy data and terms stored in a database and used in the fuzzy reasoning in an expert system. It can also retrieve information by fuzzy database-queries which are generated by the expert system automatically. Many expert systems in different domain areas such as decision making can be constructed. Sample applications are described to demonstrate the flexibility and power of this system. The fuzzy query language defined and used in the system can also be used independently as a fuzzy enquiry tool in database applications.     

Extending CLIPS to support temporal representation and reasoning

Applications using expert systems for monitoring and control problems often require the ability to represent temporal knowledge and to apply reasoning based on that knowledge. Incorporating temporal representation and reasoning into expert systems leads to two problems in development: dealing with an implied temporal order of events using a non-procedural tool; and maintaining the large number of temporal relations that can occur among facts in the knowledge base. In this paper we explore these problems by using an expert system shell, CLIPS (C Language Integrated Production System), to create temporal relations using common knowledge-based constructs. We also build an extension to CLIPS through a user-defined function which generates the temporal relations from those facts. We use the extension to create and maintain temporal relations in a workflow application that monitors and controls an engineering design change review process. We also propose a solution to ensure truth maintenance among temporally related facts that links our temporal extension to the CLIPS facility for truth maintenance.     

TQuery: a context-sensitive temporal query language.

Users of electronic medical databases request pertinent information by recasting their clinical questions into a formal database query language. Because the query language is the user's only access to the data, the query language must be powerful enough to enable users to express their data requirements. However, a competing need is for the query language to be restrictive enough so that queries can have unambiguous semantics and the query processor can generate correct answers. We describe a query language, called TQuery , that was designed specifically to formulate database queries that are dependent on temporal and contextual relationships. TQuery specifications express contextual constraints without the need to explicitly reference calendar dates. TQuery is the database query language used to retrieve patient data from an object-oriented electronic patient medical-record system called the temporal network (TNET). TNET and TQuery were developed to support the real-time temporal reasoning and representation needs of a LISP workstation-based medical expert system.     

Extrapolation of fuzzy values from incomplete data bases

This paper presents different approaches which enable a data base management system to obtain a plausible fuzzy estimate for an attribute value of an item for which the information is not explicitly stored in the data base. This can be made either by a kind of analogical reasoning from information about particular items or by means of expert rules which specify the (fuzzy) sets of possible values of the attribute under consideration, for various classes of items. Another kind of expert rules enables the system to compute an estimate from the attribute value of another item provided that, in other respects, this latter item sufficiently resembles the item, the value of which we are interested in; then these expert rules are used either for controlling the analogical reasoning process or for enlarging the scope of application of the first kind of expert rules. The different approaches are discussed in the framework of possibility theory.     

基于模糊神经网络的单兵装备效能评估专家系统

本文提出了一种客观评价单兵装备效能的方法,在模糊神经网络算法的基础上开发了一套专家系统。对测得的样本数据进行实验分析,证明此系统具有推理效率及准确性较高的特点。     

注塑件CAD建模与CAE智能处理的集成化研究

CAD／CAE技术的集成化研究是当前的一个研究热点，对于注塑件／模而言，CAD信息向CAE传输中存在信息丢失和少智能问题，而CAE的结果向CAD信息的反馈过程中也存在信息缺乏衔接和少智能化的问题，利用CBR（基于事件的推理）对工艺参数进行智能推理，然后结合推理出的参数值以及CAD中过滤出的信息，输入到CAE进行分析，对CAD中的特征信息与CAE后处理的信息进行合理的匹配，建立对后处理结果的缺陷解释专家系统。重点对工艺参数优选的CBR及后处理的专家解释系统和数据流的流动过程进行了深入的研究，提出CAD／CAE并行环思想，从而方便了注塑件特征造型与CAE智能处理的集成。     

Professional negligence and financial‐legal expert systems: Architectures to enable the reasonableness defence

The legal implications of professionals relying on expert systems have been extensively studied by analysts who have considered the ‘macro’ issues such as whether products liability can be invited for loss resulting from errors in expert systems, and apportionment of liability. These macro‐analyses do not distinguish programming errors from what we call ‘errors of reasoning’. The latter errors may be considered to be advertent on the part of developers and/or user‐professionals; may potentially be directly compared with the reasoning ascribable to a reasonably competent professional; and cannot be removed merely by better quality assurance in the sense prevalent in software engineering. In this paper we consider the mechanisms by which a court may examine errors of reasoning in expert systems used to assist professionals working in certain areas of financial services ('the financial‐legal domain'). Our analysis of the ‘micro’ issues of liability shows that the advertent nature of errors of reasoning makes it difficult to argue that using the best possible quality assurance methods during development is sufficient to make the reliance on an expert system reasonable. However, certain valuable defences that are available to professionals acting unaided can be made out even when expert systems are employed as part of the reasoning process, though these defences will be disabled if certain conceptual structures of the knowledge of professionals are not distinctively maintained in the knowledge base. As such, opacity of a knowledge base assists, rather than hinders, a plaintiff in establishing professional negligence. The results of an analysis of the micro‐issues of liability have been applied by us in the design of a system for financial/tax planning. A case‐based reasoning approach is employed, deductive rules being employed strictly in a subsidiary role. Consequently, the primary justification for a decision is a ‘case’ ‐ an instance of problem‐solving by a professional ‐ that is likely to be accepted by the court as being representative of the actions of reasonably competent professionals.     

Ecological applications using a novel expert system shell

Much of the information used by ecologists in modelling and decision making is imprecise. The imprecision arises both from data that are inexact or incomplete and from the use of ecological principles that are sometimes less than fully reliable and may be conflicting. Nevertheless, expert ecologists are able to construct usable models and make decisions that are used to manage and control ecological resources. This paper describes a unique expert system shell, developed in conjunction with user ecologists, which incorporates features enabling ecologists to represent knowledge and uncertainty in their expert systems in a way that is natural and appropriate. The reasoning mechanism was similarly developed in conjunction with user ecologists. It produces solutions to a class of expert level problems along with explanatory mechanisms and an appropriate analysis of the reasoning process. Three expert systems have been constructed by ecologists using this expert system shell. This enabled the shell designers to evaluate features for inclusion in the shell. The successful use of the shell by the ecologists has shown that significant economies arise when expert system shell design is tailored to use by a specific class of experts, in this case ecologists.     

煤矿瓦斯预测专家系统中基于粗集的知识获取方法

针对现有煤矿瓦斯预测专家系统因没有新知识获取措施及知识自更新功能而预测效果不佳的问题,提出了基于粗集的知识获取方法。该方法首先建立瓦斯数据与瓦斯突出强度之间关系的预测样本集;然后运用粗糙集的连续属性离散化、属性约简以及规则提取算法,从大量的预测样本集中自动获取预测知识,并将预测知识存储于专家系统知识库中;最后基于推理机实现煤矿瓦斯突出的实时预测。实例分析验证了该方法在煤矿瓦斯突出预测专家系统知识获取中的有效性和实用性。     

Towards a temporal coherence management in real-time knowledge-based systems

The multi-agent system paradigm emerges as an interesting approach in the Knowledge-Based System (KBS) field when distributed problem-solving techniques are required. On the other hand, temporal representation and reasoning problems arise in a wide range of KBS application areas where time plays a crucial role. In this paper, we show that when agents run concurrently and access common temporal data, some problems of coherence arise. We analyse the different cases in which an incoherence in temporal information can occur and provide a method to tackle this problem. In this method, conflict management is handled by means of exception handlers and control rules allowing the users to explicitly define their own strategy for temporal coherence solving.     

CASE-BASED REASONING IN ENVIRONMENTAL MONITORING APPLICATIONS

Environmental monitoring is usually based on large volumes of data, while in general, environmental decision making is a complex problem, has a high degree of uncertainty, and involves diverse areas of expertise. Environmental decision-support systems are therefore good candidates for application of artificial intelligence (AI) techniques. In this paper it is argued that a suitable approach for building these systems is the use of case-based reasoning or analogical reasoning techniques, which offer more adaptability and better explanation facilities than other AI paradigms. As an example, the development stages, the architecture, and the operational characteristics of the expert system Air Quality Predictor (AIRQUAP), developed to predict air pollution levels in Athens, Greece, are described. AIRQUAP helps users retrieve historical data intelligently and can predict air pollution levels, useful for management of air pollution episodes. The performance of the system is also compared with other techniques used in this class of applications.     

基于置信规则库的工业控制系统安全状态评估

当前工业控制系统存在严重安全问题,针对现有工业控制系统安全状态评估模型存在的不足,提出一种基于置信规则库(BRB)专家系统的工业控制系统安全状态评估方法.该方法首先利用置信规则库专家系统将工业控制系统中定性知识与定量监测数据相结合.然后采用证据推理(ER)算法进行知识推理,并对所建立的BRB模型初始参数进行优化.最后以...     

Temporal representation and reasoning in artificial intelligence: Issues and approaches

Time is one of the most relevant topics in AI. It plays a major role in several areas, ranging from logical foundations to applications of knowledge‐based systems. In this paper, we survey a wide range of research in temporal representation and reasoning, without committing ourselves to the point of view of any specific application. The organization of the paper follows the commonly recognized division of the field in two main subfields: reasoning about actions and change, and reasoning about temporal constraints. We give an overview of the basic issues, approaches, and results in these two areas, and outline relevant recent developments. Furthermore, we briefly analyze the major emerging trends in temporal representation and reasoning as well as the relationships with other well‐established areas, such as temporal databases and logic programming. This revised version was published online in June 2006 with corrections to the Cover Date.     

Model-based remotely-sensed imagery interpretation

Abstract

We present a model-based remotely-sensed image interpretation expert system embeded in a knowledge-based geographic information system (K. BIS). The KBIS consists of four sub-systems: a pictorial data base system, an image interpretation expert system, a computer-aided planning system and a computer-aided cartographic system. The image interpretation expert system represents ecological knowledge and other expert knowledge by frames. Its reasoning process consists of a forward reasoning based on the Bayes classification of Landsat imagery, a backward reasoning using frame knowledge and reasoning using a spatial consistency model. A forest inventory study was conducted in Shaxian county, in the southern part of China, using this expert system. The results have shown a significant improvement. Building image interpretation expert systems within knowledge-based pictorial systems is very convenient and efficient because there are well-organized data, knowledge and procedures available.