Hybrid knowledge integration using the fuzzy genetic algorithm: prediction of the Korea stock price index

This paper proposes the hybrid knowledge integration mechanism using the fuzzy genetic algorithm for the optimized integration of knowledge from several sources such as machine knowledge, expert knowledge and user knowledge. This mechanism is applied to the prediction of the Korea stock price index. Machine knowledge is generated by applying neural networks to technical indicators, while expert knowledge and user knowledge are generated from the evaluations of external factors that affect the stock market. Cooperative knowledge is generated from the weighted sum of these sources using a genetic algorithm. Experimental results show that the hybrid mechanism can provide more accurate and less ambiguous results. It means that this mechanism is useful in integrating knowledge from multiple sources for an unstructured environment such as the stock market. Copyright © 2004 John Wiley & Sons, Ltd.     

Shamrock Shirt Co.'s non-interest bearing note

Student knowledge engineering is a new instructional technique which places students in the role of expert systems developers. This paper examines the feasibility, costs and benefits of this technique within the context of the accounting curriculum. A knowledge engineering project in the area of taxation is presented and evaluated by monitoring students' problem solving behaviour before, during and after developing their expert systems. Evidence suggests that knowledge engineering provides and environment in which students learn to (1) search, (2) read with a problem solving frame of mind, (3) communicate logically, (4) organize and structure accounting knowledge, and (5) logically problem solve, while learning accounting content material. In addition, knowledge engineering gives students a hands-on introduction to expert systems technology. Student knowledge engineering was implemented and evaluated in an Accounting Information Systems course. Student experiences were generally positive and confirmed expectations. Times required for completion of the projects were similar to those of other major course assignments.     

IN-QUOTES: a knowledge-based system for supporting decision making in weakly structured domains

In complex and weakly structured domains, decision makers often employ multiple techniques, including quantitative modeling and reasoning from past experiences, to address the problem at hand. As such, there has been a call for more research on developing systems that merge problem-solving approaches, such as reasoning from past experiences, with other paradigms to provide support for both the unstructured and structured aspects of the decision-making process. Development of these systems is dependent on acquiring and modeling the knowledge and expertise inherent in the process and then representing and implementing it in an appropriate form. However, in weakly structured domains, knowledge acquisition may be better described as knowledge ‘co-creation’ in which the expert and system builder work together to understand the process and lend as much structure to it as possible. In this paper, we propose that the integration of principles drawn from the paradigms of case-base reasoning, expert systems, and object-oriented programming facilitates this process by providing a powerful approach to acquire and model knowledge in a weakly structured domain. We demonstrate this approach through the development of a system designed to assist a decision maker in the performance of a difficult, somewhat unstructured design and planning task. © 1998 John Wiley & Sons, Ltd.     

Experiences with EXGAME: an expert system for playing a competitive business game

This paper looks at expert systems in management, by using a business game as an experimental vehicle. An expert system called EXGAME was developed to play a business game, which is normally played by students, with minimal human intervention. This paper concentrates on the effectiveness of EXGAME as compared with human players for tasks at different levels. EXGAME was able to replace human players in decision making at the operational level, and indeed outperform them. However, it proved to be impractical to replace human input at the strategic level. The paper also sheds some light on the problems of trying to build an expert system when there is no real expert. A combination of a modular knowledge-base structure and a process of ‘learning by experimentation’ was effective in this case; it is suggested that this may be an appropriate development strategy in other similar situations. © 1998 John Wiley & Sons, Ltd.     

Future utility services’ (un)knowns framework: Knowledge existence and knowledge reach

This paper presents a framework for assessing risks and uncertainties in the domain of utility services in futures up to 100 years hence. A nine-step process framework is presented following a review of extant literature. The framework provides a way to quantitatively and qualitatively evaluate one or more vignettes (potential future utility infrastructure solutions) by examining: 1. Alternative scenario contexts; 2. User utility service requirements; 3. Technologies’ readiness; 4. Knowledge of each technology life cycle stage; 5. Criticality of technologies to user requirements; 6. Interdependencies between technologies. The framework is based on collecting multiple expert contributions in order to arrive at a comparative evaluation. We use the ‘city blood’ vignette, which represents an infrastructure system delivering hydrogen enriched water, to apply the framework to two radically different scenario contexts.     

Refining the behavior of multiple expert systems: a concept and empirical results

Expert System Refinement (ESR) is introduced as a means to automatically refine the performance of one or more expert systems. The ESR concept is based on Holland’s learning classifier systems and a method for integrating multiple expert systems. Through users’ feedback about the usefulness/correctness of the integrated expert system’s recommendations, ESR enables behaviors of both individual expert systems as well as the integrated system to improve over time. The ESR concept is tested on a German Credit Database. This empirical evidence suggests that the ESR concept can be usefully applied in automating the process of expert system refinement and multiple expert systems integration. © 1998 John Wiley & Sons, Ltd.     

Machine‐independent audit trail analysis—a tool for continuous audit assurance

This paper reports the results of a research project which examines the feasibility of developing a machine‐independent audit trail analyser (MIATA). MIATA is a knowledge‐based system which performs intelligent analysis of operating system audit trails. Such a system is proposed as a decision support tool for auditors when assessing the risk of unauthorized user activity in multi‐user computer systems. It is also relevant to the provision of a continuous assurance service to clients by internal and external auditors. Monitoring user activity in system audit trails manually is impractical because of the vast quantity of events recorded in those audit trails. However, if done manually, an expert security auditor would be needed to look for two main types of events—user activity rejected by the system's security settings (failed actions) and users behaving abnormally (e.g. unexpected changes in activity such as the purchasing clerk attempting to modify payroll data). A knowledge‐based system is suited to applications that require expertise to perform well‐de?ned, yet complex, monitoring activities (e.g. controlling nuclear reactors and detecting intrusions in computer systems). To permit machine‐independent intelligent audit trail analysis, an anomaly‐detection approach is adopted. Time series forecasting methods are used to develop and maintain the user pro?le database (knowledge base) that allows identi?cation of users with rejected behaviour as well as abnormal behaviour. The knowledge‐based system maintains this knowledge base and permits reporting on the potential intruder threats (summarized in Table I). The intelligence of the MIATA system is its ability to handle audit trails from any system, its knowledge base capturing rejected user activity and detecting anomalous activity, and its reporting capabilities focusing on known methods of intrusion. MIATA also updates user pro?les and forecasts of behaviour on a daily basis. As such, it also ‘learns’ from changes in user behaviour. The feasibility of generating machine‐independent audit trail records, and the applicability of the anomaly‐detection approach and time series forecasting methods, are demonstrated using three case studies. These results support the proposal that developing a machine‐independent audit trail analyser is feasible. Such a system will be an invaluable aid to an auditor in detecting potential computer intrusions and monitoring user activity. Copyright © 2004 John Wiley & Sons, Ltd.     

Combining Expert Systems and Hypertext: Opportunities and Obstacles

Hypertext discussions are occurring more frequently at expert systems conferences. Hypertext is not an expert system language or expert system shell, but instead it is simultaneously a method of storing and retrieving data. The growing interest in hypertext in the expert system domain is because the combination of hypertext capabilities and expert systems creates a compelling synergistic relationship (Oren, 1987). During a panel discussion at the Second International Symposium on Expert Systems in Business, Finance and Accounting, Bill Swartout compared traditional expert systems to hypertext by saying that the knowledge base (e.g. the production rules) of an expert system can be thought of as formal knowledge and hypertext can be thought of as informal knowledge. This article will demonstrate how this informal knowledge can add power to traditional expert systems by increasing the flexibility of the explanation facilities and thereby the appeal of the system to a broader range of users. The article also will discuss how expert systems can improve the functionality of hypertext systems by adding intelligence to traditional hypertext systems. This article is divided into three sections. The first describes basic hypertext concepts. The second section shows how hypertext can enhance expert systems and, conversely, how expert systems can enhance hypertext systems. The final section discusses some potential problems and concerns that must be considered when designing a hypertext system.     

Expert Systems at the Customer Interface

ESCFE (Expert System for Customer Facing Environments) is an integrated expert systems architecture which supports a three-way dialogue between British Telecommunications (BT) customers, staff and its technical and commercial support systems. ESCFE is customer facing—this means that the user interface dialogue is directed explicitly at the customer, as opposed to other expert systems/help desks which have a user interface directed at staff, who then deal with the customer in whatever manner they have been trained. In dealing with the customer, BT staff require a wide range of knowledge concerning the products and services portfolio, internal BT procedures, selling opportunities, customer-care policy and contractual liabilities. This knowledge can be contained within ESCFE. The knowledge is used to support a dialogue which allows for both customer- and staff-driven interaction, and for different levels of expertise in both customers and staff.     

Neuro‐fuzzy time‐series analysis of large‐volume data

This paper describes a framework that utilizes an adaptive‐network‐based fuzzy inference system to perform user‐constrained pattern recognition on time‐series data. Using a customizable fuzzy logic grammar, the architecture allows an analyst to capture domain expertise in a context‐relevant manner. Fuzzy logic rules constructed by the analyst are used to perform feature extraction and influence the training of a neural network to perform pattern recognition. We demonstrate that the architecture is capable of performing noise‐tolerant searches across multiple features on large volumes of time‐series data. The experiments presented here are from the domain of stock analysis. We are able to create simple rule sets automatically to search a data warehouse of stocks to select stocks that exhibit desirable behaviours. Copyright © 2011 John Wiley & Sons, Ltd.     

From “Broad Studies” to Internet-based “Expert Knowledge Aggregation”. Notes on the methodology and technology of knowledge integration

The article takes as a starting point Herman Kahn's concept of a process-based approach to future-oriented knowledge aggregation and integration and discusses the opportunities created by Internet technologies to achieve the goal envisioned by such a model. The article briefly introduces a general framework for analyzing knowledge aggregation, the use of various aggregation mechanisms as well as aggregation systems that rely either on human facilitators (meta-experts) or on computer algorithms. Available online instruments for knowledge aggregation and expert challenge are presented and the possibility of using them for setting up “virtual think tanks” for foresight studies is explored.     

A rule-based fuzzy reasoning system for assessing the risk of management fraud

Detecting management fraud and assessing the risk of management fraud are significant issues confronting the auditing profession. Considerable theoretical and empirical research (Loebbecke, Eining, and Willingham, 1989; Bell, Szykowny, and Willingham, 1993; Fanning, Cogger, and Srivastava, 1995; and Hansen, McDonald, Messier, and Bell, 1996) has been accomplished investigating these issues. Building on this research, we demonstrate the construction of a rule-based fuzzy reasoning system to assess the risk of management fraud. The paper illustrates how fuzzy sets can be used intuitively to measure red flags on a categorical or interval scale, how different red flags can be combined using fuzzy rules, and how a single measure of the risk of management fraud can be derived. The knowledge base for this fuzzy reasoning system is developed by using the causal model of management fraud developed by Loebbecke, Eining and Willingham (1989), the empirical investigation of this model by Bell, Szykowny, and Willingham (1993), other researchers’ efforts and the authors’ judgments, using XpertRule software. The fuzzy reasoning system is tested using the fraud data provided by KPMG Peat Marwick. We discuss methods to magnify the knowledge base of this fuzzy reasoning system to make it a viable auditing tool, the costs and benefits of building a fuzzy reasoning system, and further extensions of this research. Copyright © 1998 John Wiley & Sons, Ltd.     

The ‘SKADE LITorSET’ Expert System for Corporate ‘Litigate or Settle’ Decisions

The ‘SKADE LITotSET’ system is a blackboard-based expert system that makes ‘litigate or settle’ decisions in the product liability area. It has three knowledge sources: Legal, Manager and Insurance Adjuster. The combined expertise from each of these is required to solve the ‘litigate or settle’ problem. The control component co-ordinates the interaction between the various knowledge sources on the blackboard. Based on the latest changes to the data on the blackboard, it selects and executes the next knowledge source. The model reproduces the decision makers' opportunistic reasoning processes by the interaction between the various knowledge sources through the blackboard. The results of analyses of a hypothetical case through a series of experiments with the ‘SKADE LITorSET’ system indicate that the blackboard is an appropriate model for development of expert systems in the ‘litigate or settle’ decision domain. The initial success with the blackboard approach suggests that further work needs to be done to see whether more complex models can be built to incorporate a broader range of determinants of settlement decisions.     

LEGAL KNOWLEDGE AND AGILITY IN PUBLIC ADMINISTRATION

To address agility in public administration, we have developed a knowledge acquisition infrastructure for legal knowledge, based on an implementation‐oriented conceptualization of the legal system. Our objective is to reframe legal knowledge as a knowledge source in a design‐oriented task ontology, building on insights from the CommonKADS methodology for intelligent system design. The main result is to make case law and legal expert knowledge of critical incidents in organizations, two diagnostic knowledge sources underutilized in modern management and engineering, more accessible as a resource for design of agile organizational structures and intelligent systems. Copyright © 2013 John Wiley & Sons, Ltd.     

Multiple channel structures in financial services: A framework

The use of multiple channels is probably the most common distribution strategy nowadays. Surprisingly, the determinants of this strategy remain largely unexplored. This paper aims to address the problem by elaborating on the configuration of a multiple channel strategy. A framework is proposed that attempts to elaborate on the entire channel mix decision starting with the factors that influence and constrain the choice of channels, and finishing with key outcomes of a channel mix. It is proposed that a multiple channel structure should be thought of along three dimensions or properties: number of channels; channel integration; and extent of customer contact afforded by the channel mix. The relationships between these properties of a mix of channels and the performance of the channel, measured by sales and costs, control and flexibility is discussed. It is hoped the framework will be useful to channel managers and researchers alike. Directions for future research are developed.     

Social impacts of selected artificial intelligence applications : The Canadian context

Artificial intelligence (AI) will be a transforming technology because it will allow old things to be done in a dramatically different way-whether cheaper, faster, or simply better. This article looks at the social impacts of computerization and discusses natural language processing, machine translation, expert systems and the overall effect of AI applications on employment. It is concluded that AI applications are likely to develop in an evolutionary sequence rather than through one or more sudden breakthroughs. However, the sum of the changes which will result from the sequence of these suboptimal systems will almost certainly transform a wide range of human activities.     

Extending COLOSSUS to Cover Compensation for Future Economic Loss

COLOSSUS is a mainframe-based expert system jointly developed by Continuum Australia and GIO (General Insurance Organization) for the assessment of third-party claims. COLOSSUS has been in production for over two years and is accessible throughout all GIO regions in New South Wales, Australia. In this paper we describe the extension of COLOSSUS to cater for future economic loss to a claimant. Because of the diversity of knowledge sources in the insurance domain, we have adopted a highly interactive approach that requires successive user confirmations during a consultation. The practicality of our knowledge engineering approach is highlighted by examining several difficult issues. Design issues are also addressed with particular reference to clarity and ease of maintenance. An annotated report of a sample run is enclosed.     

Evaluating Expert Systems in a Financial Domain: A Study Demonstrating Issues in Case Selection,Validation Standards and Evaluator Bias

This article describes and generalizes a validation study of four commercially available personal financial planning expert systems and the rationale for the research methodology used. Our evaluation of these systems adds to the understanding of verification and validation issues related to case selection, validation standards and evaluator bias. The article describes the systems, their domain and the empirical method—field tests using hypothetical cases—and relates that method to the literature. Comparing same-task systems combines multiple system perspectives and multiple models. Our methodology did efficiently and effectively identify conflicting terminology, omissions and system weaknesses but was inadequate for comparing the complex plan recommendations. The results re-emphasize the importance of continual knowledge base updating, formal system testing and the need for external evaluation. The results also show the value of comparing multiple, same-task systems.     

THE USE OF SOCIAL MEDIA IN THE SUPPLY CHAIN: SURVEY AND EXTENSIONS

This paper investigates the capabilities of social media, such as Facebook, Twitter, Delicious, Digg and others, for their current and potential impact on the supply chain. In particular, this paper examines the use of social media to capture the impact on supply‐chain events and develop a context for those events. This paper also analyses the use of social media in the supply chain to build relationships among supply‐chain participants. Further, this paper investigates the of use of user‐supplied tags as a basis of evaluating and extending an ontology for supply chains. In addition, using knowledge discovery from social media, a number of concepts related to the supply chain are examined, including supply‐chain reputation and influence within the supply chain. Prediction markets are analysed for their potential use in supply chains. Finally, this paper investigates the integration of traditional knowledge management along with knowledge generated from social media. Copyright © 2011 John Wiley & Sons, Ltd.     

The 'Independence' of Expert Opinions in Corporate Takeovers: Agreeing With Directors' Recommendations

Abstract:   The impact of non‐audit services on auditor independence has been the recent focus of regulators worldwide. Using expert reports provided in Australian takeovers, this study investigates a context where the audit independence issue is reversed. As approximately a quarter of expert reports are prepared by the target firm's auditor, concerns have been expressed over the independence of the opinion provided. This paper finds that, relative to other experts, there is no difference in the rate at which experts with other business dealings with the target, including the target's auditor, provide an opinion that agrees with that of directors. However, the capital market reaction around the release of the report indicates that reports produced by auditors are viewed as non‐independent.