The application of artificial intelligence in auditing: Looking back to the future

ICT-based decision aids are currently making waves in the modern business world simultaneously with increased pressure on auditors to play a more effective role in the governance and control of corporate entities. This paper aims to review the main research efforts and current debates on auditors’ use of artificial intelligent systems, with a view to predicting future directions of research and software development in the area. The paper maps the development process of artificial intelligent systems in auditing in the light of their identified benefits and drawbacks. It also reviews previous research efforts on the use of expert systems and neural networks in auditing and the implications thereof. The synthesis of these previous studies revealed certain research vacuum which future studies in the area could fill. Such areas include matching the benefits of adopting these intelligent agents with their costs, assessing the impact of artificial intelligence on internal control systems’ design and monitoring as well as audit committees’ effectiveness, and implications of using such systems for small and medium audit firms’ operations and survival, audit education, public sector organisations’ audit, auditor independence and audit expectations-performance gap.     

Expert systems applied to problems in geographic information systems: Introduction, review and prospects

This paper critically reviews current expert system developments relevant to geographic information systems, and identifies several research topics for application of expert system technology in geographic information systems. We have identified four major problem domains of geographic information systems in which expert system technology has been applied—automated map design, terrain/feature extraction, database management/user interface, and geographic decision support systems. Efforts in each problem domain are critically reviewed. Considering the accomplishments and shortcomings of efforts to date, we identify areas likely to gain importance in this field. Our view of these prospects is moderated by constraints of current technology and a realistic view of current efforts. Future research will place more emphasis on formal representation of both knowledge and uncertainty. Another future research area will be the development of advanced tools for geographic knowledge acquisition. Finally, better methods for working with large-scale geographic databases will be needed.     

专家系统研究现状及展望*

总结了自2000年以来专家系统在国内外的发展及应用情况,并根据知识表示方法的不同,对专家系统在各个领域的应用进行分类。结果显示：近五年来,伴随网络时代的到来及蓬勃发展,国内外专家系统发展迅速,一些新技术已经应用于专家系统,尤其是XML和W eb技术的应用,为专家系统的研究注入新的活力。同时对目前专家系统存在的一些问题以及今后的研究和发展进行了探讨。     

What are experts for?

Knowledge engineering is the term given to the process of developing expert systems and knowledge engineers are the people who acquire the requisite knowledge from experts and structure that knowledge into a useable computer program. As knowledge engineering becomes a more accepted technology, there is increasing concern about attendant social costs, such as job displacement or possible exploitation of experts. This paper reports on our efforts to explore this latter issue by scrutinizing how knowledge engineers think about the domain expert and the role that person plays in system development. To accomplish this aim, we asked several samples of novice engineers to write story completions to a preamble that describes a knowledge engineer encountering a reluctant expert who may be fearing job loss if the system is implemented. The resulting accounts were content-analysed for insights as to how novice system builders think about experts. The results indicate that experts are conceived more as a tool to be used rather than a person to be respected.Preparation of this chapter was supported by National Science Foundation Grant BNS 87-21882 to Marianne LaFrance.     

The bank of Scotland''s COMPASS—The future of bank lending?

This paper describes the background surrounding the need for risk analysis of commercial business within the domain of bank lending and of the development of an expert system for that task. Previous attempts at constructing expert systems in this area have either proved unsuccessful, software difficulties often being cited as the cause; or have stopped short of encapsulating all the relevant expertise. This paper considers the relevance of knowledge engineering to successful expert system construction. It reports on the development and structure of COMPASS, the Bank of Scotland's commercial lending adviser expert system which, by appropriate application of knowledge engineering, has succeeded in capturing and modelling the inherent risk of the Bank of Scotland's commercial lending process. The stages of its development are outlined; the knowledge elicitation process is described; knowledge articulation is examined from the perspective of the expert; the architecture of the system is explained; and the consultation procedure is described. In addition, reference is made to major attempts elsewhere to produce lending adviser expert systems; and the advantages; by-products, and long-term benefits of COMPASS are summarized.     

Collection, storage and application of human knowledge in expert system development

Abstract: Expert systems are programs that analyze data by mimicking the thought processes of an expert. Two expert systems were developed by the Reservoir Evaluation and Advanced Computational Techniques group to aid in oil prospecting for two New Mexico formations, leading to the development of a third customizable fuzzy expert system. Knowledge engineering is a major part of the development of these expert systems, in which expert knowledge is solicited, analyzed, converted to rules stored in a system's knowledge base, and used by the computer to produce expert judgment. Numerical versions of the rules are used to analyze data and produce an evaluation of the user's prospect. In addition, the knowledge base preserves expert knowledge for future workers. This is especially important in the petroleum industry, as there is a cyclical trend in employment relating to the price of oil, retirements and people leaving and entering the industry.     

The Knowledge Acquisition Bottleneck: Time for Reassessment?

Abstract: Knowledge acquisition has long been considered to be the major constraint in the development of expert systems. Conventional wisdom also maintains that the major problem encountered in knowledge acquisition is in identifying the varying structures and characteristics of domain knowledge and matching these to suitable acquisition techniques. With the aid of the first substantial systematic analysis of a sample of expert systems applications developed in the real world, the authors describe what is actually going on in terms of knowledge acquisition. In the light of the evidence, it is argued that a reappraisal of the conventional approach to knowledge acquisition is necessary.     

On two AI traditions

To understand the role of expert systems as a medium for transferring knowledge and skills within organisations requires an understanding of the nature of expertise within working life contexts. Central to this issue of transfer is the debate on the nature of tacit/implicit knowledge and the problem of formalising it in explicit form. This paper considers the British approach to the development of knowledge-based systems, which is regarded as being predominantly rationalistic, and compares it with the Scandinavian approach, which is regarded as being predominantly humanistic. The paper proposes that crucial to the debate on the transfer of knowledge and skills is the development of expert systems as communications media which aims at enhancing and sharing the knowledge and skills of users. A human-centred approach for the future is proposed which brings the above two traditions together thereby providing a developmental framework for knowledge based systems.     

Designing intelligent litigation support tools: The IKBALS perspective

In the legal domain, it is rare to find solutions to problems by simply applying algorithms or invoking deductive rules in some knowledge‐based program. Instead, expert practitioners often supplement domain‐specific knowledge with field experience. This type of expertise is often applied in the form of an analogy. This research proposes to combine both reasoning with precedents and reasoning with statutes and regulations in a way that will enhance the statutory interpretation task. This is being attempted through the integration of database and expert system technologies. Case‐based reasoning is being used to model legal precedents while rule‐based reasoning modules are being used to model the legislation and other types of causal knowledge. It is hoped to generalise these findings and to develop a formal methodology for integrating case‐based databases with rule‐based expert systems in the legal domain.     

Logistics information system auditing using expert system technology

This paper brings together two methodological strands of thinking. These are the managerial problem solving methodology of Logistics Information System auditing and the structured development of expert system technology. The investment being made in logistics in organizations is enormous and, although much effort has been devoted to creating structured methods to aid the development of information systems to support these organizations' logistics, the area of Logistics Information System auditing remains less developed.

The major aim of this paper is to provide a systemic approach of the application of expert system technology to Logistics Information System auditing. Taking a strategic view of Management Information System (MIS) in logistics, this paper describes the application of INFAUDITOR, an audit expert system, to logistics information systems auditing.

INFAUDITOR has two fundamental features. First, it covers all domains of information systems, managerial (like logistics) as well as technical aspects. Secondly, it helps to determine, in a given audit situation, the respective importance that should be given to the different audit domains and tests of control. INFAUDITOR can be viewed as consisting of several expert systems as in blackboard systems. Its fact bases include the characteristics of the enterprise, its logistics information system and the audit objectives. Its rule bases encompass the audit criteria represented as a hierarchical tree.

INFAUDITOR is used to assess the ability of a Logistics Information System (LIS) to provide decision makers with relevant, timely information for designing, planning and maintaining an efficient production system, for securing materials necessary for production, and for facilitating achievement of low operating and maintenance costs. We present and discuss results obtained by using INFAUDITOR in auditing the logistics Management Information System of a large European company.     

Explanation-based learning as justification of knowledge

Abstract: This paper describes the use of the explanation-based learning (EBL) machine learning technique in the practical domain of knowledge acquisition for expert systems. A knowledge acquisition tool, EBKAT (Explanation-Based Knowledge Acquisition Tool), is described, which may be used in the development of knowledge bases for diagnostic expert systems. The functioning of EBKAT attempts to combine the full potential of a domain expert's skills and the power of explanation-based machine learning techniques. The EBL component is not employed in the acquisition of the knowledge base rules but is used to justify the knowledge entered and to relate it to the knowledge already in the system. It is suggested that the EBKAT tool goes some way towards overcoming the knowledge acquisition bottleneck and results in the acquisition of knowledge which is rich in contextual information.     

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.     

Audit Resource Planning Success in B2B E-Commerce: Development and Testing of a Measurement Scale

Abstract

The e-commerce audit engagement scenario makes audit resource planning (ARP) an activity full of technological complexities. These complexities generally involve both e-commerce technologies and enterprise information integration, putting real pressure on the audit organization's scarce human and financial resources. However, the success (or effectiveness) of such complex audit resource planning activity is not well understood or easily measured. This paper attempts to develop and diagnostically test measurement scales using classical measurement development framework and contemporary structural equation modeling methods.

This study is motivated by prior studies in the conventional (non e-commerce) audit resource planning, and audit-planning domain published in the accounting and management information systems discipline. The prior studies suggest that audit resource planning in a B2B (business-to-business) e-commerce engagement is a multi dimensional construct consisting of four latent dimensions. The four latent dimensions of ARP success are reflected through several manifest variables obtained from various relevant studies.

The results confirm that audit resource planning success is operationalizable as a measurement scale with the four dimensions. The results of the study are tools for benchmarking future efforts by audit organizations. Accounting scholars can also use this study as a base for operationalizing audit resource planning success as a key dependent variable in future research.     

Communicating the knowledge in knowledge-based systems

As the role of knowledge-based systems grows in the marketplace, the necessity of clearly communicating their knowledge to people increases. However, well-represented internally, a system's knowledge cannot be used to train, advise, or assist an individual unless it can be discussed naturally. Recent efforts to standardize knowledge coding and expert system user interfaces fall short in defining a real ability to communicate knowledge. Most systems are unable to explain their knowledge, inferences, or applicability to anyone but a well-trained, domain-knowledgeable user. In this paper, we examine the features needed to enable intelligent expression of knowledge, and survey previous work in this area. We also describe an intelligent text generator (ITG) designed as an adjunct to an object-oriented expert system. We present a structure within which a rule-based system for a given domain can be expanded to communicate its knowledge intelligibly, in any of several natural languages.     

A GUI for Jess

The paper describes JessGUI, a graphical user interface developed on top of the Jess expert system shell. The central idea of the JessGUI project was to make building, revising, updating, and testing Jess-based expert systems easier, more flexible, and more user friendly. There are many other expert system building tools providing a rich and comfortable integrated development environment to expert system builders. However, they are all either commercial or proprietary products. Jess and JessGUI are open-source freeware, and yet they are well suited for building even complex expert system applications, both stand-alone and Web-based ones. An important feature of JessGUI is its capability of saving knowledge bases in XML format (in addition to the original Jess format), thus making them potentially easy to interoperate with other knowledge bases on the Internet. Jess and JessGUI are also used as practical knowledge engineering tools to support both introductory and advanced university courses on expert systems. The paper presents design details of JessGUI, explains its links with the underlying Jess knowledge representation and reasoning tools, and shows examples of using JessGUI in expert system development. It also discusses some of the current efforts in extending Jess/JessGUI in order to provide intelligent features originally not supported in Jess.     

Automated knowledge acquisition for the surface-mount PWB assembly domain

The development of knowledge-based (or expert) systems for the surface-mount printed wiring board (PWB) assembly domain requires the understanding and regulation of several complex tasks. While the knowledge base in an expert system serves as a storehouse of knowledge primitives, its design and development is a bottleneck in the expert system development life-cycle. Therefore the development of an automated knowledge acquisition (KA) facility (or KA tool) would facilitate the implementation of expert systems for any domain. This paper describes an automated KA tool that helps to elicit and store information in domain-specific knowledge bases for surface-mount PWB assembly. A salient feature of this research is the acquisition of uncertain information.     

面向应用领域的知识发现系统开发平台KDIST

介绍了一个面向应用领域的知识发现系统开发平台KDIST。将数据挖掘技术巧妙地封装在应用领域的问题中,使开发出的知识发现系统操作傻瓜化,用户无须关心数据挖掘本身,有效地减轻了领域用户使用负担,提高了数据挖掘技术实用性。所开发出的知识发现系统将挖掘得到的知识融合到已有专家系统的知识库中。两个实例系统的应用证明知识发现系统是专家系统自动半自动知识获取和知识库精化的良好工具。     

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.     

温室控制专家系统开发平台的设计与实现

本文研制了一个面向领域专家(温室工人)的温室控制专家系统开发平台。其知识编辑器采用图形组态的形式．使不具备专家系统编程方面专门知识的用户也能容易地输入知识经验。平台的知识编辑器与推理机把模糊控制与专家控制理论相结合，提高了系统智能化程度。实例证明了用该平台开发温室控制专家系统的可行性与优点。     

A methodology for knowledge engineering using an interactive graphical tool for knowledge modelling

A basic feature of human nature is the propensity to construct boundaries which define territorial possession. Such assumed possessions are often jealously guarded by their owners, and a consequence of this primitive instinct is the emergence of subject specialists who exercise in-depth ‘lordship’ over their domain of expertise. For many years computer science has made attempts to relate to this phenomenon of expert intellectual property by developing mechanisms in software to emulate reasoning capability. Correspondingly this has resulted in the development of intelligent knowledge-based (or expert) systems, along with their attendant processes of knowledge elicitation, representation and exploitation. This paper attempts to define a context for knowledge engineering, the term being used to define spanning the void between domain expertise and the intelligent knowledge based system. It goes on to describe the systemic development of a particular solution to the knowledge engineering problem which is underpinned by a software environment called VEGAN (a Visual Editor for the Generation of Associative Networks). Many attempts have been made at bridging this gap, and VEGAN represents a significant aid to the knowledge engineering task, in the context of frame-based systems. Rather than attempt to create a unidirectional information path from expert to computer system (or knowledge engineer), VEGAN presents a common forum for discussion about, and exploration of, the expertise of the domain specialist. By doing so it helps the flow of information between the two parties. VEGAN represents an approach to a human-natured design of a software system which:

• 1.empathises with the ‘culpture’ of the expert,
• 2.provides a bridge between the expert and the computer system by shielfing the expert from the underlying complexity of the system,
• 3.aids the study of the organization of expertise, and thus induces further information.