THE SCOPE OF DIMENSIONAL ANALYSIS IN QUALITATIVE REASONING

Dimensional analysis, traditionally used in physics and engineering to identify quantitative relationships, has recently been applied to qualitative reasoning of physical systems. We illustrate some problems of this approach. In the light of this, we reexamine the fundamentals of dimensional analysis in order to more precisely characterize its scope and limitations as a tool in qualitative reasoning. We also explore its relationship to state equation representations of physical systems. In particular, we describe its value in providing a set of constraints to reduce the ambiguity that bedevils qualitative reasoning schemes. We argue that dimensional analysis should not be seen as a substitute for knowledge about the physics but rather a supplement to other sources of knowledge.     

PROLEGOMENA TO ANY FUTURE QUALITATIVE PHYSICS

We evaluate the success of the qualitative physics enterprise in automating expert reasoning about physical systems. The field has agreed, in essentials, upon a modeling language for dynamical systems, a representation for behavior, and an analysis method. The modeling language consists of generalized ordinary differential equations containing unspecified constants and monotonic functions; the behavioral representation decomposes the state space described by the equations into discrete cells; and the analysis method traces the transitory response using sign arithmetic and calculus. The field has developed several reasoners based on these choices over some 15 years. We demonstrate that these reasoners exhibit severe limitations in comparison with experts and can analyze only a handful of simple systems. We trace the limitations to inappropriate assumptions about expert needs and methods. Experts ordinarily seek to determine asymptotic behavior rather than transient response, and use extensive mathematical knowledge and numerical analysis to derive this information. Standard mathematics provides complete qualitative understanding of many systems, including those addressed so far in qualitative physics. Preliminary evidence suggests that expert knowledge and reasoning methods can be automated directly, without restriction to the accepted language, representation, and algorithm. We conclude that expert knowledge and methods provide the most promising basis for automating qualitative reasoning about physical systems.     

基于Agent的专家系统推理模型

针对传统专家系统推理模型结构在知识获取方面适应性差的现状,从系统科学的视角,运用复杂适应系统理论,对传统专家系统的结构及运行机制进行了改进.引入Agent来模拟人脑中的神经元,用来承载专家系统中相互作用的知识,然后,基于Multi-Agent之间的相互作用来构建复杂适应的专家系统推理模型.从而,将专家系统中的知识获取机制、知识库、推理机三者统一于由Multi-Agent进行相互作用的复杂适应系统之中.通过设计体育赛事申办决策专家系统的原型,进行了专家系统推理模型的验证.原型运行结果表明:基于Multi-Agent的专家系统推理模型结构能够有效地提高专家系统知识获取的适应性.这为研究更加接近人脑智能的专家系统提供了崭新的研究思路.     

A reasoning method for a ship design expert system

Abstract: The ship design process is a highly data‐oriented, dynamic, iterative and multi‐stage algorithm. It utilizes multiple abstraction levels and concurrent engineering techniques. Specialized techniques for knowledge acquisition, knowledge representation and reasoning must be developed to solve these problems for a ship design expert system. Consequently, very few attempts have been made to model the ship design process using an expert system approach. The current work investigates a knowledge representation–reasoning technique for such a purpose. A knowledge‐based conceptual design was developed by utilizing a prototype approach and hierarchical decompositioning. An expert system program called ALDES (accommodation layout design expert system) was developed by using the CLIPS expert system shell and an object‐oriented user interface. The reasoning and knowledge representation methods of ALDES are explained in the paper. An application of the method is given for the general arrangement design of a containership.     

Causal models for diagnostic reasoning

Theories of qualitative physics are crucial in developing knowledge-based systems in engineering. Basic models for diagnostic reasoning are based on causal connections between the system parameters. In this paper a knowledge representation and problem solving technique is presented as a ground-laying step. The technique is based on the concepts introduced by Iwasaki and Simon⁹ and employs the methods of analysis from the field of control engineering. The causal analysis of systems with feedback is done by using knowledge about their block diagrams. A constraint representing the feed-forward path is modified in order to eliminate the feedback parameter. This approach supports the confluence heuristic in de Kleer and Brown's qualitative physics⁴. Causal dependencies that partially describe the behaviour of a system are used to generate a search space for faulty components. This approach is based on reasoning about counterfactuals using modal categorizations, as proposed by Nicolas Rescher and Herbert Simon¹². The scope of application of this method in real-time monitoring and diagnosis of large industrial processes is discussed.     

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.     

Development of an expert system for preliminary risk assessment of existing concrete dams

The paper presents an expert system to assist in the field inspection of existing concrete dams within the context of a preliminary risk assessment. The paper describes the engineering knowledge and reasoning required to conduct a deterministic field evaluation of the structural stability of the dam. The symptoms and failure modes identified by the expert system along with the required knowledge and procedures are organized in a structured knowledge tree. The instantiation of the frames and firing of the rules for each consultation traces part of the inference tree contained in the structured knowledge tree. Interaction between nearly decomposable problems are executed with metaknowledge procedures, shared rule groups, and active values. Examples are provided.     

D-KAT: a deep knowledge acquisition tool

Abstract: This paper describes a system of shallow and deep knowledge acquisition and representation for diagnostic expert systems. The acquisition system is integrated into a diagnostic expert system shell. Shallow knowledge is represented in a failure model as a set of cause-effect relations among the possible faults, while deep knowledge is represented in three deep models: a functional, a deep causal and a taxonomic model. The acquisition and the representation of all the models are fully integrated. The deep knowledge is used by the final expert system in order to provide the user with deep explanations of the cause-effect relations of the failure model.     

A general expert system design for diagnostic problem solving

Existing expert systems have a high percentage agreement with experts in a particular field in many situations. However, in many ways their overall behavior is not like that of a human expert. These areas include the inability to give flexible, functional explanations of their reasoning processes, and the failure to degrade gracefully when dealing with problems at the periphery of their knowledge. These two important shortcomings can be improved when the right knowledge is available to the system. This paper presents an expert system design, called the integrated diagnostic model (IDM), that integrates two sources of knowledge, a shallow, reasoning-oriented, experiential knowledge base and a deep, functionally oriented, physical knowledge base. To demonstrate the IDM's usefulness in the problem area of diagnosis and repair, an implementation in the mechanical domain is described.     

The smooth (tractor) operator: insights of knowledge engineering

The design of and training for complex systems requires in-depth understanding of task demands imposed on users. In this project, we used the knowledge engineering approach (Bowles et al., 2004) to assess the task of mowing in a citrus grove. Knowledge engineering is divided into four phases: (1) Establish goals. We defined specific goals based on the stakeholders involved. The main goal was to identify operator demands to support improvement of the system. (2) Create a working model of the system. We reviewed product literature, analyzed the system, and conducted expert interviews. (3) Extract knowledge. We interviewed tractor operators to understand their knowledge base. (4) Structure knowledge. We analyzed and organized operator knowledge to inform project goals. We categorized the information and developed diagrams to display the knowledge effectively. This project illustrates the benefits of knowledge engineering as a qualitative research method to inform technology design and training.     

一种面向对象的专家系统设计方法

近年来,已经有不少面向对象技术运用于专家系统的研究。该文提出一种新的面向对象专家系统的设计方法。首先介绍系统总体结构,给出知识表示模型和推理机制,并且引入RMG的概念,进一步提出运用RMG完成推理过程的算法。最后把提出的方法应用于中西医结合糖尿病诊疗专家系统的开发。实践证明此方法是有效的。     

A structural equation modeling approach to generate explanations for induced rules

The performance of an expert system depends on the quality and validity of the domain-specific knowledge built into the system. In most cases, however, domain knowledge (e.g. stock market behavior knowledge) is unstructured and differs from one domain expert to another. So, in order to acquire domain knowledge, expert system developers often take an induction approach in which a set of general rules is constructed from past examples. Expert systems based upon the induced rules were reported to perform quite well in the hold-out sample test.

However, these systems hardly provide users with an explanation which would clarify the results of a reasoning process. For this reason, users would remain unsure about whether to accept the system conclusion or not. This paper presents an approach in which explanations about the induced rules are constructed. Our approach applies the structural equation model to the quantitative data, the qualitative format of which was originally used in rule induction. This approach was implemented with Korean stock market data to show that a plausible explanation about the induced rule can be constructed.     

Causal domain model driven knowledge acquisition for expert diagnosis system development

Despite the successful operation of expert diagnosis systems in various areas of human activity these systems still show several drawbacks. Expert diagnosis systems infer system faults from observable symptoms. These systems usually are based on production rules which reflect so called shallow knowledge of the problem domain. Though the explanation subsystem allows the program to explain its reasoning, deeper theoretical justifications of program's actions are usually needed. This may be one of the reasons why in recent years in knowledge engineering there has been a shift from rule-based systems to model-based systems. Model-based systems allow us to reason and to explain a system's physical structure, functions and behaviour, and thus, to achieve much better understanding of the system's operations, both in normal mode and under fault conditions. The domain knowledge captured in the knowledge base of the expert diagnosis system must include deep causal knowledge to ensure t he desired level of explanation. The objective of this paper is to develop a causal domain model driven approach to knowledge acquisition using an expert–acquisition system–knowledge base paradigm. The framework of structural modelling is used to execute systematic, partly formal model-based knowledge acquisition, the result of which is three structural models–one model of morphological structure and two kinds of models of functional structures. Hierarchy of frames are used for knowledge representation in topological knowledge base (TKB). A formal method to derive cause–consequence rules from the TKB is proposed. The set of cause–consequence rules reflects causal relationships between causes (faults) and sequences of consequences (changes of parameter values). The deep knowledge rule base consists of cause–consequence rules and provides better understanding of system's operation. This, in turn, gives the possibility to construct better explanation fa cilities for expert diagnosis system. The proposed method has been implemented in the automated structural modelling system ASMOS. The application areas of ASMOS are complex technical systems with physically heterogeneous elements.     

Intelligent computer-aided-design systems: a synergical approach of artificial intelligénce and engineering

A synergism has begun to surface from the artificial intelligence (AI) and engineering communities: an effort to apply AI techniques to engineering problem-solving activities, and to study problems arisen from various engineering fields as a way to develop AI theories and methodologies. This paper first discusses the needs of such a synergical approach and identifies in a broad perspective some AI techniques currently being applied to engineering. It then describes a system, called KREATOR, which applies qualitative reasoning, a subfield of AI, to computer-aided design (CAD). The key observation is that an engineer designer's qualitative knowledge can offer a good basis for the reasoning of device behaviors. Such knowledge, however, is not captured by conventional CAD systems for lack of good representations. KREATOR is a knowledge capturing scheme that allows the designers to record their qualitative knowledge of how mechanical devices behave, KREATOR then automatically generates qualitative simulations.     

设计型专家系统在机械工程中的应用研究

专家系统是人工智能技术的一个重要分支，它是特定领域的一套计算机程序，具有类似专家工作时利用知识进行推理来解决问题的能力。它一般用以求解那些需要人类专家才能求解的高难度问题或不良结构的问题，为人类保存、使用、传播和评价知识提供了一条有效的捷径。文中主要介绍设计型专家系统在机械工程中的应用以及其基本结构、知识表示方法、推理方式及构建策略，然后介绍了它在齿轮传动设计中的应用。设计型专家系统的产生和发展必然会促进设计自动化技术在机械工程中的应用。     

一种基于关系数据库的知识表示和推理方法

获得一种具有广泛的知识表示方法和灵活高效的推理逻辑是专家系统研究中一直追求的一个目标?将日益发展成熟的关系数据库技术引入专家系统,论述了一种基于关系数据库的知识表示方法和推理逻辑,利用关系数据库几乎不受限制的字段个数和丰富的字段类型来表示专家知识和组织推理逻辑,达到了知识表示的广泛性和逻辑推理的高效性。这一方法可广泛应用于各领域的专家系统,在中医专家系统的实例中,取得了令人满意的效果。     

基于置信规则库专家系统的发动机故障诊断

针对发动机故障原因和征兆之间存在的复杂非线性关系,利用RIMER(基于证据推理算法的置信规则库推理方法)对发动机进行故障诊断,克服了传统专家系统或神经网络技术只能单一利用专家知识或训练数据的缺点,将定性知识与定量数据有效结合,对发动机故障原因进行了研究,给维修人员提供了重要参考依据,仿真实验结果表明该方法可行有效.     

Implementing expert system rule conditions by neural networks

The relation of subsymbolic (neural computing) and symbolic computing has been a topic of intense discussion. We address some of the drawbacks of current expert system technology and study the possibility of using neural computing principles to improve their competence. In this paper we focus on the problem of using neural networks to implement expert system rule conditions. Our approach allows symbolic inference engines to make direct use of complex sensory input via so called detector predicates. We also discuss the use of self organizing Kohonen networks as a means to determine those attributes (properties) of data that reflect meaningful statistical relationships in the expert system input space. This mechanism can be used to address the defficult problem of conceptual clustering of information. The concepts introduced are illustrated by two application examples: an automatic inspection system for circuit packs and an expert system for respiratory and anesthesia monitoring. The adopted approach differs from the earlier research on the use of neural networks as expert systems, where the only method to obtain knowledge is learning from training data. In our approach the synergy of rules and detector predicates combines the advantages of both worlds: it maintains the clarity of the rule-based knowledge representation at the higher reasoning levels without sacrificing the power of noise-tolerant pattern association offered by neural computing methods. This research is supported by Technology Development Center (TEKES) in Software Technology Programme (FINSOFT). Part of this work was done while the author was visiting AT & T Bell Laboratories.     

Knowledge intensive software engineering tools

Most software engineering tools use a shallow representation of software objects and manipulate this representation using procedural methods. This approach allows one to get off to a fast start and quickly provides a tool that delivers benefits. However, a point will be reached where more knowledge-intensive approaches will be needed to achieve significantly higher levels of capability. The authors suggest that the software engineering tools of the future will have to rely on: deep representation to capture a sufficiently large part of knowledge about programming in general and particular programs; inspection methods to deal with complexity; and intelligent assistance