A Delphi-based approach to developing expert systems with the cooperation of multiple experts

Knowledge acquisition has been a critical bottleneck in building knowledge-based systems. In past decades, several methods and systems have been proposed to cope with this problem. Most of these methods and systems were proposed to deal with the acquisition of domain knowledge from single expert. However, as multiple experts may have different experiences and knowledge on the same application domain, it is necessary to elicit and integrate knowledge from multiple experts in building an effective expert system. Moreover, the recent literature has depicted that “time” is an important parameter that might significantly affect the accuracy of inference results of an expert system; therefore, while discussing the elicitation of domain expertise from multiple experts, it becomes an challenging and important issue to take the “time” factor into consideration. To cope with these problems, in this study, we propose a Delphi-based approach to eliciting knowledge from multiple experts. An application on the diagnosis of Severe Acute Respiratory Syndrome has depicted the superiority of the novel approach.     

FILIP: A fuzzy intelligent information system with learning capabilities

FILIP (fuzzy intelligent learning information processing) system is designed with the goal to model human information processing. The issues addressed are uncertain knowledge representation and approximate reasoning based on fuzzy set theory, and knowledge acquisition by “being told” or by “learning from examples”. Concepts that can be “learned” by the system can be imprecise (fuzzy), or the knowledge can be incomplete. In the latter case, FILIP uses the concept of similarity to extrapolate the knowledge to cases that were not covered by examples provided by the user. Concepts are stored in the Knowledge Base and employed in intelligent query processing, based on flexible inference that supports approximate matches between the data in the database and the query.

The architecture of FILIP is discussed, the learning algorithm is described, and examples of the system's performance in the knowledge acquisition and querying modes, together with its explanatory capabilities are shown.     

Tool life prediction for cup shaped cold forgings with fuzzy language risk analysis and fuzzy inference

A method of Die-life prediction is suggested for cup shaped forgings.Authors theorize that forge specialists can make die-life prediction by comparing a “target” forging process” with other standard processor whose actual life are known. The authors make comparison by calculation of risk (that which shortens die-life span). Risk is estimated by using a risk tree network, based on information compiled from a survey of forge experts. The risk rate of an “end node” is estimated by a computer-aided forging process planning system. Comparing dimensions of the target and standard forging processes has effect on the risk rate. Once risk is determined, it is used to predict dielife span using Fuzzy inference. The Fuzzy inference rule is estimated based on data received from the interview of experts.     

Quality control & inspection: Knowledge-based quality control system

The majority of Statistical Quality Control (SQC) microcomputer systems/packages are based on the idea of storing data and producing results such as QC charts from which a user may be able to make inferences regarding Quality. The selection of the type of control charts (e.g. charts for variables vs. charts for attributes) and interpretation of the results are usually left to the user. This is mainly due to the fact that SQC packages are generic systems while interpretations may be product specific.

Presented in this paper is a system which tries through dialogue with the user to direct him to the proper chart(s) in the package. An analysis component for each chart determines statistical phenomena (both good and bad) and provides general explanations. Another component in the system accesses an accompanying knowledge database (DB) — keyed by phenomena — which provide possible explanation as well as advise. The user may add to the knowledge base at any time assisted by the statistical DB Management Subsystem.

The elements of the system are the IIE Microsoftware Statistical Quality Control package and a number of “add-on” routines supporting DB creation and the inference engine.     

为测试系统诊断设计并构造规则库和推理机

目前ＶＬＳＩ测试系统日趋复杂，要快速，精确地对它们进行诊断，不得不求助于专家系统，专家知识和解释专家知识并控制诊断进程的机制是专家系统的两大要素，它们又分别被称为规则库和推理机，针对这两大要素，本文阐述以下两方面的问题：（１）设计规则库的结构，（２）设计并构造用于ＶＬＳＩ测试系统诊断的推理机。     

Inference engine for material handling selection

In this paper we describe the inference engine of an expert system for material handling method and equipment selection. The system branches through a tree guided by collected data essential for solving the material handling equation: Material + Move → Method. The equation is solved using rules developed to handle relationships between elements and factors of the equation. While the engine in its current status is limited to a “ first cut” in the equipment selection process, it is of value to nonspecialist industrialist. Plans are for the final system to provide its user with access to vendors specialized in particular handing operations.     

Prioritization of human capital measurement indicators using fuzzy AHP

People in an organization constitute an important and essential asset which tremendously contributes to development and growth of that company by the help of their collective attitudes, skills and abilities. This is why the human capital (HC) can be considered the most important sub-dimension of the intellectual capital. Since you cannot manage what you cannot control, and you cannot control what you do not measure, the measurement of HC is a very important issue. This study aims at defining a methodology to improve the quality of prioritization of HC measurement indicators under fuzziness. To do so, a methodology based on the extent fuzzy analytic hierarchy process (AHP) is proposed. Within the model, five main attributes; talent, strategical integration, cultural relevance, knowledge management, and leadership; their sub-attributes, and 20 indicators are defined. The proposed model can be used for any country. However, the results obtained in the numerical example reflect the situation of HC in Turkey, since the experts are asked to make their evaluations considering the cultural characteristics of Turkey. The results of the study indicate that “creating results by using knowledge”, “employees’ skills index”, “sharing and reporting knowledge”, and “succession rate of training programs” are the four most important measurement indicators for the HC in Turkey.     

MESTRIMAN: An expert system for medical triage and clinical management of patients in catastrophes

Providing appropriate medical assistance in catastrophes is not an easy problem to cope with. Some of the problems involved in the task are the diversity of catastrophes and medical emergencies, the insufficient number of experts usually available during a given catastrophe, the necessity of improving medical performace time, and the lack of coordination of the resources that may be used. In this respect, the application of both communication and computer technologies may provide important and useful support in most of the different phases involved in the management of catastrophes. In this paper we describe “MESTRIMAN,” an expert system designed to provide medical assistance in catastrophes. “MESTRIMAN” is a module of SIAC, a computer system designed to manage relevant information and data about a wide range of different catastrophes and capable of performing as the central coordinator of the available resources.     

It-Supported Competence Management: A Case Study at Ericsson

This article highlights the potential benefits and challenges related to implementation of IT-sup-ported strategic competence management as part of a human resource management system. A case study of the implementation of a competence management system in the global telecommunications company Ericsson reveals several implementation challenges, and also shows how the system supports Ericsson's knowledge networking strategy, through locating experts and stimulating emerging “communities of knowing.”     

An intelligent learning diagnosis system for Web-based thematic learning platform

This work proposes an intelligent learning diagnosis system that supports a Web-based thematic learning model, which aims to cultivate learners’ ability of knowledge integration by giving the learners the opportunities to select the learning topics that they are interested, and gain knowledge on the specific topics by surfing on the Internet to search related learning courseware and discussing what they have learned with their colleagues. Based on the log files that record the learners’ past online learning behavior, an intelligent diagnosis system is used to give appropriate learning guidance to assist the learners in improving their study behaviors and grade online class participation for the instructor. The achievement of the learners’ final reports can also be predicted by the diagnosis system accurately. Our experimental results reveal that the proposed learning diagnosis system can efficiently help learners to expand their knowledge while surfing in cyberspace Web-based “theme-based learning” model.     

Application of expert systems to process control

This paper describes the EXTASE system, an expert system for alarm processing in the vacuum distillation tower of a refinery. The system is able to find the cause(s) of an active alarm as well as the dependencies between alarms that were originated by the same disturbance in the process. Starting with an analysis of the operato's activity while performing information synthesis tasks, a computer model is abstracted. This model is based on a very simple hypothesis-driven search strategy. The search relies on causal relationship called “couplings” which encode the operator's basic knowledge about the behavior of the process. This approach allows the system to account for possible sensor failure in the diagnosis of process alarms.     

Designing a semantic bliki system to support different types of knowledge and adaptive learning

Though blogs and wikis have been used to support knowledge management and e-learning, existing blogs and wikis cannot support different types of knowledge and adaptive learning. A case in point, types of knowledge vary greatly in category and viewpoints. Additionally, adaptive learning is crucial to improving one’s learning performance. This study aims to design a semantic bliki system to tackle such issues. To support various types of knowledge, this study has developed a new social software called “bliki” that combines the advantages of blogs and wikis. This bliki system also applies Semantic Web technology to organize an ontology and a variety of knowledge types. To aid adaptive learning, a function called “Book” is provided to enable learners to arrange personalized learning goals and paths. The learning contents and their sequences and difficulty levels can be specified according to learners’ metacognitive knowledge and collaborative activities. An experiment is conducted to evaluate this system and the experimental results show that this system is able to comprehend various types of knowledge and to improve learners’ learning performance.     

Knowledge-based spectroscopic assignment

Studying the energy level structure of different molecules by means of spectroscopy is one of the fields in physics and chemistry. In this paper, we describe a knowledge-based system, molecular spectroscopic assignment assistant (MOSAA), which can assist researchers in the assignment of the far-infrared and infrared absorption peaks of the molecules in question by using the spectral information provided, along with a knowledge base containing known ground-state energy levels of the given molecules and spectroscopic assignment rules provided by researchers.

A total of 313 rules together with their associated components (i.e., parameters, functions and properties) compose the knowledge base for MOSAA. A specific inference engine was designed to handle the “trial and error” process required for spectroscopic assignment. An overview of MOSAA as well as the testing results are discussed in this paper. Testing of the CH₃¹⁸OH and CD₃¹⁶OH spectra showed that MOSAA successfully assigned 11 testing series for each spectrum in 1/8 of the time on average required to do a manual assignment. Two series of the CD₃¹⁶OH spectrum assigned by MOSAA were missed in the original manual assignment.     

基于CAN总线的雷达装备BIT故障诊断专家系统研究

介绍了一种基于CAN总线的分散检测,集中诊断、显示的雷达BIT故障诊断系统,描述了其硬件组成、专家系统结构,知识表示和推理机制。通过在该雷达BIT中采用专家系统诊断方法,使得推理机与知识库分离,便于诊断知识库的扩充、维护,有效提高了系统BIT故障诊断能力。     

An improved neural network for fuzzy reasoning implementation

Neural networks or connectionist models are massively parallel interconnections of simple neurons that work as a collective system, can emulate human performance and provide high computation rates. On the other hand, fuzzy systems are capable to model uncertain or ambiguous situations that are so often encountered in real life. One way for implementing fuzzy systems is through utilizations of the expert system architecture. Recently, many attempts have been made to “fuse” fuzzy systems and neural nets in order to achieve better performance in reasoning and decision making processes. The systems that result from such a fusion are called neuro-fuzzy inference systems and possess combined features. The purpose of the present paper is to propose such a neuro-fuzzy system by extending and improving the system of Keller et al. (1992). The present system makes use of Hamacher's fuzzy intersection function and Sugeno's complement function. After a brief outline of the operation of the system its features are established with the aid of four theorems which are fully proved. The capabilities of the system are shown by a set of simulation results derived for the case of trapezoidal fuzzy sets. These results are shown to be better than the ones obtained with the original neuro-fuzzy system of Keller et al.     

A view-based neurocomputational system for relational map-making and navigation in visual environments

Artificial navigation systems stand to benefit greatly from learning maps of visual environments, but traditional map-making techniques are inadequate in several respects. This paper describes an adaptive, view-based, relational map-making system for navigating within a 3D environment defined by a spatially distributed set of visual landmarks. Inspired by an analogy to learning aspect graphs of 3D objects, the system comprises two neurocomputational architectures that emulate cognitive mapping in the rat hippocampus. The first architecture performs unsupervised place learning by combining the “What” with the “Where”, namely through conjunctions of landmark identity, pose, and egocentric gaze direction within a local, restricted sensory view of the environment. The second associatively learns action consequences by incorporating the “When”, namely through conjunctions of learned places and coarsely coded robot motions. Together, these networks form a map reminiscent of a partially observable Markov decision process, and consequently provide an ideal neural substrate for prediction, environment recognition, route planning, and exploration. Preliminary results from real-time implementations on a mobile robot called MAVIN (the Mobile Adaptive VIsual Navigator) demonstrate the potential for these capabilities.     

A structure for expert systems maintenance

An approach to the design of maintainable expert systems is presented. Central to this approach is a conceptual model in which the data and knowledge are both modelled as formal “items” in a uniform way. “Objects” are introduced as “item building” operators. The notion of the “decomposition” of items and objects provides the foundation for a single rule of normalization. This single rule applies to all items and objects, including knowledge items, and is a non-trivial generalization of the traditional normal forms for database. Coupling relationships represent the necessary maintenance paths in the conceptual model. A complete characterization of coupling relationships is given, and the value of normalization to the reduction of maintenance costs is discussed.     

EEG signal classification using wavelet feature extraction and a mixture of expert model

Mixture of experts (ME) is modular neural network architecture for supervised learning. A double-loop Expectation-Maximization (EM) algorithm has been introduced to the ME network structure for detection of epileptic seizure. The detection of epileptiform discharges in the EEG is an important component in the diagnosis of epilepsy. EEG signals were decomposed into the frequency sub-bands using discrete wavelet transform (DWT). Then these sub-band frequencies were used as an input to a ME network with two discrete outputs: normal and epileptic. In order to improve accuracy, the outputs of expert networks were combined according to a set of local weights called the “gating function”. The invariant transformations of the ME probability density functions include the permutations of the expert labels and the translations of the parameters in the gating functions. The performance of the proposed model was evaluated in terms of classification accuracies and the results confirmed that the proposed ME network structure has some potential in detecting epileptic seizures. The ME network structure achieved accuracy rates which were higher than that of the stand-alone neural network model.     

控讨基于教学过程的ITS系统模型

计算机智能导师系统（ＩＴＳ）,是智能化教学的一个分支。围绕教师在常规教学过程中所起的作用,分析研究了教师在教学的各个阶段中所承担的角色和完成的工作,并根据教学过程的动态特性,提出了一个基于教学过程的ＩＴＳ模型。还针对ＩＴＳ模型中的学科知识库、学生模型和教学策略推理机的实现方法给出了描述。