Value-driven expert systems for decision support

Values are an inherent part of all decision processes. Hence, values are at least implicity included in all expert systems intended for decision support. This paper outlines the concepts and methodology, which are based on the principles and procedures of decision analysis, to address explicity the values in an expert system logically and consistently. Implementation of the concepts and methodology involves the elicitation of values using the same general approach as that used by knowledge engineers to explicate expert knowledge.     

Expert systems and behavioral decision research

This paper reviews a part of the literature on behavioral decision research (policy capturing, psychophysics of numerical judgments and cognitive illusions) and examines implication for knowledge elicitation in expert systems. The literature on policy capturing demonstrates that simple and compact numerical models of expert knowledge can be built, but that experts are poor in verbalizing the knowledge expressed in them. The psychophysical literature indicates that numerical encoding of expert knowledge may be difficult and biased, but that it has definitive advantages over qualitative elicitation schemes: Numerical encoding forces hard throught, encourages precision, and allows to access a substantial computational apparatus. The literature on cognitive illusions suggests that the expert knowledge one elicits may be an illusion. The review concludes by recommending to use numerical judgments and explicit models by experts where possible, and to decompose the elicitation task in order to avoid cognitive illusions.     

面向问题分析与决策的专家系统

专家系统的根本目标在于为实际应用问题提供强有力的分析与决策能力。以人类通过长期实践活动总结的复杂问题分析与决策方法为指导思想,建立了以问题对象为核心、相关对象为问题主体、问题现象为表现形式、因果关系为问题变化驱动力、过程知识和原理知识为参考对象的面向问题分析与决策的专家系统。这种专家系统围绕应用领域中的问题构建知识库,而不是使用规则,所以得到的知识系统比较合理、清晰,不容易产生知识矛盾与冲突,有利于大型知识库的构建;同时,采用基于问题的推理,与人类的思维习惯相符合,可以大大提高推理效率;此外,开发这种专家     

Quantitative decision making for cued land reconnaissance: a defence case study

Helping commanders make more informed decisions is one of the most important roles played by defence operations analysts. This paper presents a defence case study of quantitative decision making. It develops a decision model for cued land reconnaissance missions that treats detecting, identifying, and destroying a hostile target to protect an asset as an integral process. The paper investigates issues such as whether an information assessment system, which provides improved knowledge about the nature of the cue, is necessary and what kind of reconnaissance platforms should be used under various conditions. Using a decision tree and evaluating all possible outcomes, the model derives the best course of action based on expected values. Then serials of sensitivity analysis are carried out to account for uncertainties of the scenarios, and their implications are explored. This illustrates that decision tree methodology can be used to analyse complex sequential decision problems in real military situations, and uncertainties can be taken into account in the decision making process.     

Knowledge engineering concepts for decision support system design and implementation

The design of computer-based systems that simulate expert human consulting by drawing on large amounts of task-specific knowledge has been a major research activity of applied artificial intelligence over the last ten years. Building decision support systems that incorporate aspects of this research is a promising new field. The purpose of this paper is to discuss concepts of “knowledge engineering” that are most relevant in designing and building knowledge-based decision support systems.     

An Efficient Expert System for Air Compressor Troubleshooting

In this paper, an expert system for automobile air compressor troubleshooting (ACTS) is presented. This system can assist users to conduct an efficient and effective diagnosis on air compressor failures. Unlike most diagnosis expert systems, ACTS uses a new control strategy to enhance the efficiency of the diagnostic process. This control strategy attempts to spend the least amount of time to detect the compressor fault accurately by investigating only portions of the knowledge base. ACTS first constructs a diagnostic tree based on the functions or connectivity of the air compressor's devices. A fuzzy multiple-attribute decision-making method is used to determine the priority of the nodes (devices) in the diagnostic tree. The prioritized result creates a 'meta knowledge base' to control the diagnostic process. In addition, each node possesses its own knowledge base for hypothesizing the possible faults for the node. ACTS, written in MS Visual BASIC, has been successfully developed and implemented in MS-Windows environment on a PC. To validate the system performance, ACTS is compared to EXACT, an expert system for automobile air compressor troubleshooting, using 50 sample cases. The evaluation results indicate that ACTS performs better than EXACT by reducing the number of queries and the diagnosis time by 20.7% and 24.9%, respectively.     

Sequential decision models for expert system optimization

Sequential decision models are an important element of expert system optimization when the cost or time to collect inputs is significant and inputs are not known until the system operates. Many expert systems in business, engineering, and medicine have benefited from sequential decision technology. In this survey, we unify the disparate literature on sequential decision models to improve comprehensibility and accessibility. We separate formulation of sequential decision models from solution techniques. For model formulation, we classify sequential decision models by objective (cost minimization versus value maximization) knowledge source (rules, data, belief network, etc.), and optimized form (decision tree, path, input order). A wide variety of sequential decision models are discussed in this taxonomy. For solution techniques, we demonstrate how search methods and heuristics are influenced by economic objective, knowledge source, and optimized form. We discuss open research problems to stimulate additional research and development     

An example of expert system on numerical modelling system in coastal processes

With the advent of artificial intelligence technology as well as the widespread popularity of desktop microcomputers in recent years, integration of this new technology with the traditional numerical modelling system becomes a current trend in order to solve various engineering problems. It renders a more intelligent and user-friendly system on the problem domain. In this paper, a knowledge-based expert system on numerical modelling system for coastal water processes is delineated. Expert system application, as a key branch of artificial intelligence technology, is integrated with traditional numerical modelling for simulating flow and water quality phenomenon in coastal waters. The knowledge bases are classified into five major types, namely, a variety of models, relations between various model parameters and real physical conditions, feasible options of model parameters, question base as a user-interface directing the user to depict the actual physical conditions, and the rules of inference deducing the feasible choice of model and its parameters. A hybrid expert system shell, Visual Rule Studio, is employed as an ActiveX Designer under Microsoft Visual Basic environment because it combines the advantages of both production rules and object-oriented programming technology. Both forward chaining and backward chaining are used collectively during the inference process, which is mainly driven by premises and conditions with the highest factors of confidence. The inference engine will drive the decision tree to explore the most probable option of numerical model and parameters matching the real problem specifications. It is shown that the application and integration of the knowledge-based expert system technology into numerical modelling for coastal processes can provide substantial assistance to novice users for selection of numerical model as well as parameters.     

Restructuring decision tables for elucidation of knowledge

Decision tables are widely used in many knowledge-based and decision support systems. They allow relatively complex logical relationships to be represented in an easily understood form and processed efficiently. This paper describes second-order decision tables (decision tables that contain rows whose components have sets of atomic values) and their role in knowledge engineering to: (1) support efficient management and enhance comprehensibility of tabular knowledge acquired by knowledge engineers, and (2) automatically generate knowledge from a tabular set of examples. We show how second-order decision tables can be used to restructure acquired tabular knowledge into a condensed but logically equivalent second-order table. We then present the results of experiments with such restructuring. Next, we describe SORCER, a learning system that induces second-order decision tables from a given database. We compare SORCER with IDTM, a system that induces standard decision tables, and a state-of-the-art decision tree learner, C4.5. Results show that in spite of its simple induction methods, on the average over the data sets studied, SORCER has the lowest error rate.     

Inducing decision trees with an ant colony optimization algorithm

Decision trees have been widely used in data mining and machine learning as a comprehensible knowledge representation. While ant colony optimization (ACO) algorithms have been successfully applied to extract classification rules, decision tree induction with ACO algorithms remains an almost unexplored research area. In this paper we propose a novel ACO algorithm to induce decision trees, combining commonly used strategies from both traditional decision tree induction algorithms and ACO. The proposed algorithm is compared against three decision tree induction algorithms, namely C4.5, CART and cACDT, in 22 publicly available data sets. The results show that the predictive accuracy of the proposed algorithm is statistically significantly higher than the accuracy of both C4.5 and CART, which are well-known conventional algorithms for decision tree induction, and the accuracy of the ACO-based cACDT decision tree algorithm.     

LUBRES: An expert system development and implementation for real-time fault diagnosis of a lubricating oil refining process

An expert system LUBRES (LUBricating oil Refining Expert System) is introduced in this paper. It helps plant operators in monitoring and diagnosing of abnormal situations in refining process of lubricating oil. The LUBRES structure, the knowledge base, and the inference machine are presented in detail. A new strategy is proposed for conflicts resolution – the sorting strategy of antecedents, and a selection strategy of knowledge rules in the memory knowledge base. Knowledge acquisition mechanism is based on an empirical knowledge table, while knowledge verification is carried out based on the directed graph approach. C++Builder and SQL Server 2000 have been used in developing the proposed system. LUBRES has been successfully implemented in Microsoft Windows Server environment. For 1 year, LUBRES has been used for monitoring and diagnosing of refining process of the lubricating oil. The industrial application of LUBRES proved its high reliability and accuracy.     

Multiknowledge for decision making

The representation of knowledge has an important effect on automated decision-making. In this paper, vector spaces are used to describe a condition space and a decision space, and knowledge is represented by a mapping from the condition space to the decision space. Many such mappings can be obtained from a training set. A set of mappings, which are created from multiple reducts in the training set, is defined as multiknowledge. In order to get a good reduct and find multiple reducts, the WADF (worst-attribute-drop-first) algorithm is developed through analysis of the properties of decision systems using rough set theory. An approach that combines multiknowledge and the naïve Bayes classifier is applied to make decisions for unseen instances or for instances with missing attribute values. Benchmark data sets from the UCI Machine Learning Repository are used to test the algorithms. The experimental results are encouraging; the prediction accuracy for unseen instances by using the algorithms is higher than by using other approaches based on a single body of knowledge.     

农用柴油机故障诊断系统的研究与开发

根据小型拖拉机的常见故障,利用Microsoft Visual FoxPro编程工具和人工智能专家系统原理,建立了知识库和相应知识表达推理机制,设计并组建了农用柴油发动机故障诊断专家系统,缓解了故障诊断专家供不应求的矛盾,提高了农用柴油机的常见故障诊断的效率和准确率。该系统主要实现的功能包括:发动机故障诊断;故障模糊查询;用户诊断数据库浏览、打印;知识库维护修改和扩充。     

Fuzzy logic-based decision support system for automating ergonomics risk assessments

Workers in the modular construction industry are frequently exposed to ergonomic risks, which may lead to injuries and lower productivity. In light of this, researchers have proposed a number of ergonomics risk assessment methods to identify design flaws in work systems, thereby reducing ergonomic discomfort and boosting workplace productivity. However, organizations often disregard ergonomics risk assessments due to a lack of convenient tools and knowledge. Therefore, this study proposes a fuzzy logic-based decision support system to help practitioners to automatically and comprehensively assess the ergonomic performance of work systems. For comprehensive assessment of ergonomic risk, the proposed decision support system considers physical, environmental, and sensory factors. Specifically, the decision support system comprises eight fuzzy expert systems that output a composite risk score, called an “ergonomic risk indicator”, that indicates the overall level of ergonomic risk present in a given work system. The performance of the proposed decision support system is then evaluated using a real-world case study in a modular construction facility by comparing the results of the decision support system with the facility's occupational injury reports. The results prove the effectiveness of the decision support system. Overall, the decision support system is capable of generating a composite risk score, the ergonomic risk indicator, and the proposed high-level architecture and design represent significant contributions for the enhancement of health and safety in the modular construction industry.     

The use of vicinal-risk minimization for training decision trees

We propose the use of Vapnik's vicinal risk minimization (VRM) for training decision trees to approximately maximize decision margins. We implement VRM by propagating uncertainties in the input attributes into the labeling decisions. In this way, we perform a global regularization over the decision tree structure. During a training phase, a decision tree is constructed to minimize the total probability of misclassifying the labeled training examples, a process which approximately maximizes the margins of the resulting classifier. We perform the necessary minimization using an appropriate meta-heuristic (genetic programming) and present results over a range of synthetic and benchmark real datasets. We demonstrate the statistical superiority of VRM training over conventional empirical risk minimization (ERM) and the well-known C4.5 algorithm, for a range of synthetic and real datasets. We also conclude that there is no statistical difference between trees trained by ERM and using C4.5. Training with VRM is shown to be more stable and repeatable than by ERM.     

设备诊断型专家系统的一种开发工具

本文设计了一种用于开发诊断型专家系统的工具系统.该系统能完成知识获取、一致性 检测和诊断推理等任务.它具有使用变量、常量和函数来描述领域知识、集成符号推理和数值 计算等特点.该系统用Microsoft C5.0语言在IBM微机上得以实现.     

基于离散度的决策树构造方法

在构造决策树的过程中,属性选择将影响到决策树的分类精度.对此,讨论了基于信息熵方法和WMR方法的局限性,提出了信息系统中条件属性集的离散度的概念.利用该概念在决策树构造过程中选择划分属性,设计了基于离散度的决策树构造算法DSD.DSD算法可以解决WMR方法在实际应用中的局限性.在UCI数据集上的实验表明,该方法构造的决策树精度与基于信息熵的方法相近,而时间复杂度则优于基于信息熵的方法.     

From decision tables to expert system shells

Building and maintaining high quality knowledge based systems is not a trivial task. Decision tables have sometimes been recommended in this process, mainly in verification and validation. In this paper, however, it is shown how decision tables can also be used to generate, and not just to validate, knowledge bases and how the transformation process from decision tables to knowledge bases can be organized. Several options to generate rules or other knowledge representation from decision tables are described and evauluated.

The proposed generation strategy enables the knowledge engineer to concentrate on the acquisition and modelling issues and allows him to isolate the knowledge body from its implementation. The generation process has been implemented for two commercial tools, AionDS and KBMS and has been applied to real world applications.     

Evaluation of the Use of Induction in the Development of a Medical Expert System

In an earlier study, two medical expert systems for diagnosing thyroid disorders, developed by the application of induction on a sample of previously diagnosed cases and on expert-generated rules, diagnosed a set of test cases better than an expert system developed by the more traditional method of collaboration between a knowledge engineer and an expert. In this paper, an alternative measure of the accuracy of diagnosis of each system is used to evaluate the systems. Diagnoses for every distinct case represented by a combination of indicating factors are compared with diagnoses that the expert made. The induced systems provide diagnoses for many more distinct cases, but a much higher proportion of these diagnoses are incorrect. It is argued that generalizing to unseen cases is an inappropriate use of induction algorithms. The systematic development of a decision table is a more appropriate method for devising a medical expert system.