Integrated knowledge systems

This paper presents a preliminary system structure supporting integration of expert systems and knowledge-based problem solving to other kinds of computing. The structure is based on layers on different abstraction levels communicating with each other through well-defined interface protocols. The result of applying such structure is a knowledge system capable of utilizing existing computer programs and information stores during its problem solving process.The structure is used in an application supporting hydrodynamic design of ships. A brief presentation of a demonstration system integrated to an existing ship design and engineering system is given.     

Principled Modeling and Automatic Classification for Enhancing the Reusability of Problem Solving Methods of Expert Systems

Software reuse is widely believed to be a key to improving software productivity and quality in conventional software. In expert systems, much of the knowledge has been compiled (i.e., compressed and restricted into effective procedures) and this makes reusability difficult. One of the issues in modeling expert systems for enhanced reusability is capturing explicity the underlying problem solving designs. Principled knowledge representation schemes have been used to model components of complex software systems. However, the potential for applying these principled modeling techniques for explicitly capturing the problem solving designs of expert systems has not been fully explored. To overcome this omission, we use an Artificial Intelligence knowledge representation scheme for developing an ontology of the software components to facilitate their classification and retrieval. The application of our ontological approach is of both theoretical and practical significance. This method facilitates the reuse of high-level design. We illustrate the application of principled domain modeling using two real world applications of knowledge-based systems.     

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.     

An imitation from observation approach for dozing distance learning in autonomous bulldozer operation

Bulldozers are indispensable heavy equipment for earthwork construction, and improving the intelligence level of bulldozers is of great significance to the construction industry. An efficient autonomous construction of earthmoving machinery requires imitating and learning the expert knowledge of operators under complex environments, and imitation from observation is an effective way. In this work, the expert knowledge of operators was imitated using the proposed hybrid method for rational decision-making of dozing distance, which is one of the key factors affecting the construction efficiency of bulldozers. The proposed method is established based on the modified deep convolutional neural networks (DCNNs) and observation dataset, combined with transfer learning to apply the pre-trained deep learning model to the target task through fine tuning. Comparing the results of different methods reveals that our proposed method obtains the smallest root mean squared error (RMSE) and average error when the expert knowledge of different operators is integrated. The proposed method has universal applicability in solving the observation-based expert knowledge imitation problem. This method also breaks through the imitations of big datasets and computing resource requirements and provides an effective technical route for the practical engineering application of expert knowledge.     

The role of the industrial engineer in developing expert systems

The most popular area of Artificial Intelligence application today is in expert systems. This paper contains a discussion of expert systems, otherwise known as knowledge-based systems and knowledge systems. The principal components of an expert system, and the evolution of expert systems are presented. The suitability of a task to an expert system is proposed. When a task is suitable for an expert system application, the system must be developed by a knowledge engineer. The methodology that the knowledge engineer must go through to develop an expert system is demostrated. Industrial engineers have formal training in many areas which can be useful when assumming the role of knowledge engineer. These areas of industrial engineering and how they are beneficial is discussed. What the future may hold in store is also pondered.     

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 representation in human problem solving: implications for expert system design

The study focuses on the identification of the underlying representational properties of human problem solving and their application to expert systems. In this study the interaction between problem representation (procedural, conceptual, unstructured) and problem type (transformation, arrangement, inducing structure) was observed. The results of this study indicate partly that quantitative and qualitative differences in problem-solving performance can be attributed to the form of knowledge representation employed by the problem solver. It is suggested that modularized expert systems could be designed with different problem-solving modules organized by problem characteristics or type, exploiting the representational differences identified in this study.     

专家系统中背景研究的意义

人工智能中不同领域的研究表明：新一代的智能辅助系统是与背景相关的。虽然人们普遍接受知识应有一个背景部件的观点，但在可用的知识表态方法及随后的知识处理中极少显式表达和利用背景知识。本文着眼于探讨 背景研究在专家系统开发中的意义，以期阐明：背景知识的显式识别、表达与利用有助于专家系统听知识获取、知识表示、推
推理、学习和解释，从而提高专家系统自适应能力和解决问题的智能性。     

Selection of knowledge acquisition techniques based upon the problem domain characteristics of production and operations management expert systems

The application of expert systems to various problem domains in business has grown steadily since their introduction. Regardless of the chosen method of development, the most commonly cited problems in developing these systems are the unavailability of both the experts and knowledge engineers and difficulties with the process of acquiring knowledge from domain experts. Within the field of artificial intelligence, this has been called the 'knowledge acquisition' problem and has been identified as the greatest bottleneck in the expert system development process. Simply stated, the problem is how to acquire the specific knowledge for a well-defined problem domain efficiently from one or more experts and represent it in the appropriate computer format. Given the 'paradox of expertise', the experts have often proceduralized their knowledge to the point that they have difficulty in explaining exactly what they know and how they know it. However, empirical research in the field of expert systems reveals that certain knowledge acquisition techniques are significantly more efficient than others in helping to extract certain types of knowledge within specific problem domains. In this paper we present a mapping between these empirical studies and a generic taxonomy of expert system problem domains. In so doing, certain knowledge acquisition techniques can be prescribed based on the problem domain characteristics. With the production and operations management (P/OM) field as the pilot area for the current study, we first examine the range of problem domains and suggest a mapping of P/OM tasks to a generic taxonomy of problem domains. We then describe the most prominent knowledge acquisition techniques. Based on the examination of the existing empirical knowledge acquisition research, we present how the empirical work can be used to provide guidance to developers of expert systems in the field of P/OM.     

Expert systems on microcomputers*

Abstract: The increasing popularity of expert systems has led to a demand to apply expert systems technology in a wide variety of computing environments. As a result, various efforts have been made to implement expert systems on microcomputers. This article reviews some of the ongoing work on tools for the development of microcomputer-based expert systems. Some specific application areas are noted, and a brief discussion of the advantages and disadvantages of implementing expert systems on microcomputers is presented.     

专家系统在抗弹复合材料性能计算中的应用

针对抗弹复合材料弹道性能影响因素多、理论计算困难而靶试实测数据成本高的困难,提出了一种基于专家系统的研究方法。该方法通过建立抗弹复合材料综合数据库和由抗弹机理公式、经验公式、静态数据等组成的知识库,并在此基础上依据一定的规则进行匹配和判定,进行抗弹复合材料的材料—性能、性能—材料的双向计算分析。经实用证明,该方法具有数据可靠、快速方便、扩展性强等特点,可用于指导材料设计和选材。     

Using soft computing to define standards of care in glaucoma monitoring

A fuzzy knowledge base encapsulating core expert rules for glaucoma follow up is developed and subsequently refined into a standard of care by reconciling several expert opinions. The Learning from Examples (LFE) [1] technique is used in addition to expert interviews to generate fuzzy rules from numerical data, and soft competition defines a fuzzy consensus metrics for the expert opinions. Web-based extension of this system into a comprehensive set of e-Health services for the glaucoma community enables, besides wide accessibility of the expert knowledge, continuous improvement of the core rule set (standard of care) with the perspectives of several experts.This work is funded under Collaborative Health Research Project Grant by the National Science and Engineering Research Council (NSERC) of Canada. We gratefully acknowledge the contributions of TransferTech GmbH Germany(www.Transfertech.de) with their soft computing software suite as well as their valuable insights in solving the implementation challenges we are faced with constantly.     

Continuous improvement through knowledge-guided analysis in experience feedback

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector.     

Paradigms for knowledge-based systems in industrial engineering

When developing expert systems, expertise lies not only in formulating the knowledge to be put into the knowledge base, but also in deciding upon the knowledge representation and inference mechanism most suited to the application. Six detailed knowledge bases demonstrate the application of various AI-based systems to industrial engineering problems. They illustrate a number of approaches: expert systems, which are based upon practical experience; decision systems, which derive from modelling skills; and situation-action systems, which rely on production process design skills. The six paradigms presented describe a logical expert system for selecting material handling equipment; a multi-valued expert system for selecting a dispatching rule for automatic guided vehicles; a profile matching expert system for selecting project management software; a confidence building expert system for selecting a machine feeder; a tandem decision system for developing a production schedule; and a situation-action system for controlling job allocation in a flexible manufacturing cell. The relationships between these various paradigms and the characteristics of problems to which they can be applied are categorized by the nature of the expert and his expertise; the features of the environment; the decision or decisions to be taken; and the manner in which AI-system performance can be evaluated. A knowledge base is proposed for determining which architecture is most appropriate for a given application.     

运用Web Service技术构建专家系统之间的交互

专家系统解决问题的范围常常受到知识领域狭窄的限制。Web Service实现了“基于Web无缝集成”的目标,可以运用Web Service技术来实现专家系统之间的交互,弥补专家系统知识不足的问题。文中运用Web Service提供的技术构建网络上专家系统之间的交互,并且对这种交互的可行性进行了分析研究,这就不仅为解决专家系统知识不足的问题提供了方法和技术,而且进一步构建了一个模型,使得网络上专家系统互相协作,共同解决一个领域更广的问题。     

A new algebraic model for implementing expert systems represented under the ‘Concept-Attribute-Value’ paradigm

This paper is concerned with expounding a new representation paradigm for modeling expert systems based on computing Groebner Bases. Previous research on Groebner Bases expert systems has so far been connected to modeling expert systems based on propositional logics. Our approach instead is based on the well-known Artificial Intelligence ‘Concept-Attribute-Value’ paradigm for representing knowledge. More precisely, our research is based on translating an already existent expert system described in terms of the ‘Concept-Attribute-Value’ paradigm to a new algebraic model which represents knowledge by means of polynomials. In this way, issues about consistence and inference within this expert system will be, through this new model, transformed into algebraic problems involving calculating Groebner Bases. By using this new model of ours, some interesting advantages ensue: on the one hand, knowledge representation may be performed in a more straightforward and intuitive way; on the other, calculating the Groebner Bases associated to our algebraic model is usually faster adopting this new ‘Concept-Attribute-Value’-based paradigm than it was in previous propositional logic-based expert systems.     

Why evolutionary development of expert systems appears to work

Currently most expert systems are developed using an evolutionary method. The method has been criticised for its lack of scientific approach in problem solving. It is here argued that the method continues to be popular among expert systems builders because it can tackle four crucial problems, namely: (1) the problem of defining system requirements; (2) the problem of extracting expert knowledge; (3) the problem of understanding and structuring expert knowledge for machine manipulation; (4) the problem of maintaining the interest and enthusiasm of the domain experts. The method has some shortcomings, which calls for a disciplined style and control as well as supportive software tools.     

An expert system for federal tax planning

Abstract: Recent developments in a subarea of computer science called artificial intelligence have included the creation of expert systems that are capable of solving difficult applications problems which require expert knowledge for their solution. Such expert systems have been found to be useful in a number of applications (e.g. medicine, biochemistry and mineral exploration). In this paper the author presents an expert system for solving problems concerning income and transfer tax planning for individuals In developing this system, a theoretical structure and a set of decision rules were specified and then programmed into a rule-based system that had previously been used for medical diagnosis (Mycin [1]) Once the system was developed, its problem-solving capabilities were refined and verified by a panel of tax experts using a blind verification procedure. This verification step demonstrated that an expert system could be developed in that domain.