THE USE OF RULE-BASED EXPERT SYSTEMS TO INVESTIGATE THE EFFECTS OF EXPERIENCE ON AUDIT JUDGMENTS

Rule-based expert systems (RBES) are currently the focus of a great deal of research interest. Most of that work, however, has concentrated on the development of such systems. There has not been much analysis of the resulting RBES. This paper examines two RBESs designed to make audit judgments. The knowledge bases of the initial prototype versions of each system contain the rules used by novice auditors. Each system was refined by having experienced auditors use the system to make the audit judgments for actual clients. The rules contained in the refined versions of each RBES thus represent the knowledge used by an experienced auditor to make a particular audit judgment. The effects of experience are then examined by comparing the rules in the initial prototype knowledge base to those contained in the refined version of each system. Experience appears to provide the capability to deal with exceptions to general rules and expectations

KNOWLEDGE ACQUISITION METHODOLOGIES: SURVEY AND EMPIRICAL ASSESSMENT

Knowledge acquisition, the process of extracting information from human experts, is one of the challenges in building expert systems. Modern practitioners and researchers need more guidance than is provided by existing knowledge acquisition guidelines. However, there has been little empirical research upon which to base the needed guidelines. This paper surveys the available knowledge acquisition techniques and describes a knowledge acquisition experiment which contrasts three of these methods. A framework was developed to categorize the types of heuristic which can be elicited with different means of knowledge acquisition. This research represents the initial steps in a research program focused on the development of empirically evaluated, generalized guidelines for effecting .knowledge acquisition

The challenge of complexity for cognitive systems

Complex cognition addresses research on (a) high-level cognitive processes – mainly problem solving, reasoning, and decision making – and their interaction with more basic processes such as perception, learning, motivation and emotion and (b) cognitive processes which take place in a complex, typically dynamic, environment. Our focus is on AI systems and cognitive models dealing with complexity and on psychological findings which can inspire or challenge cognitive systems research. In this overview we first motivate why we have to go beyond models for rather simple cognitive processes and reductionist experiments. Afterwards, we give a characterization of complexity from our perspective. We introduce the triad of cognitive science methods – analytical, empirical, and engineering methods – which in our opinion have all to be utilized to tackle complex cognition. Afterwards we highlight three aspects of complex cognition – complex problem solving, dynamic decision making, and learning of concepts, skills and strategies. We conclude with some reflections about and challenges for future research

Machine learning and its applications in reliability analysis systems

In this thesis, we are interested in exploring some aspects of Machine Learning (ML) and its application in the Reliability Analysis systems (RAs). We begin by investigating some ML paradigms and their- techniques, go on to discuss the possible applications of ML in improving RAs performance, and lastly give guidelines of the architecture of learning RAs. Our survey of ML covers both levels of Neural Network learning and Symbolic learning. In symbolic process learning, five types of learning and their applications are discussed: rote learning, learning from instruction, learning from analogy, learning from examples, and learning from observation and discovery. The Reliability Analysis systems (RAs) presented in this thesis are mainly designed for maintaining plant safety supported by two functions: risk analysis function, i.e., failure mode effect analysis (FMEA) ; and diagnosis function, i.e., real-time fault location (RTFL). Three approaches have been discussed in creating the RAs. According to the result of our survey, we suggest currently the best design of RAs is to embed model-based RAs, i.e., MORA (as software) in a neural network based computer system (as hardware). However, there are still some improvement which can be made through the applications of Machine Learning. By implanting the 'learning element', the MORA will become learning MORA (La MORA) system, a learning Reliability Analysis system with the power of automatic knowledge acquisition and inconsistency checking, and more. To conclude our thesis, we propose an architecture of La MORA

Building expert systems: cognitive emulation.

Chapter 1 briefly introduces the concept of cognitive emulation, and outlines its current status. Chapter 2 reviews psychological research on human expert thinking. First, the study of expert thinking is placed in the context of modern cognitive psychology. Next, the principal methods and techniques employed by psychologists examining expert cognition are examined. The remainder of the chapter is given over to a review of the published literature on the nature and development of human expertise. Chapter 3 reviews the main arguments for and against cognitive emulation in expert system design. The tentative conclusion reached is that a significant degree of emulation is inevitable, but that a pure, unselective strategy of emulation is neither realistic nor desirable. Chapter 4 examines the prospects for cognitive emulation from a more pragmatic angle. Several factors are identified that represent constraints on the usefulness of a cognitive approach. However, a second set of factors is identified which should facilitate an emulation strategy - especially in the longer term. Some guidance is given on when to seriously consider adopting an emulation strategy. Chapter 5 presents a critical survey of expert system research that has already addressed the emulation issue. Six basic approaches to cognitive emulation are distinguished and evaluated. This helps draw out in more detail the implications of an emulation strategy for knowledge acquisition, knowledge representation and system architecture. The chapter concludes by discussing the issues that arise when different approaches to emulation are combined. Some guidance is offered on how this might be achieved. Chapter 6 summarizes the main themes and issues to have emerged, the design advice contained in the thesis, and the original contributions made by the thesis

Improving the Relevance of Cyber Incident Notification for Mission Assurance

Military organizations have embedded Information and Communication Technology (ICT) into their core mission processes as a means to increase operational efficiency, improve decision making quality, and shorten the kill chain. This dependence can place the mission at risk when the loss, corruption, or degradation of the confidentiality, integrity, and/or availability of a critical information resource occurs. Since the accuracy, conciseness, and timeliness of the information used in decision making processes dramatically impacts the quality of command decisions, and hence, the operational mission outcome; the recognition, quantification, and documentation of critical mission-information resource dependencies is essential for the organization to gain a true appreciation of its operational risk. This research identifies existing decision support systems and evaluates their capabilities as a means for capturing, maintaining and communicating mission-to-information resource dependency information in a timely and relevant manner to assure mission operations. This thesis answers the following research question: Which decision support technology is the best candidate for use in a cyber incident notification system to overcome limitations identified in the existing United States Air Force cyber incident notification process

Knowledge Based Systems: A Critical Survey of Major Concepts, Issues, and Techniques

This Working Paper Series entry presents a detailed survey of knowledge based systems. After being in a relatively dormant state for many years, only recently is Artificial Intelligence (AI) - that branch of computer science that attempts to have machines emulate intelligent behavior - accomplishing practical results. Most of these results can be attributed to the design and use of Knowledge-Based Systems, KBSs (or ecpert systems) - problem solving computer programs that can reach a level of performance comparable to that of a human expert in some specialized problem domain. These systems can act as a consultant for various requirements like medical diagnosis, military threat analysis, project risk assessment, etc. These systems possess knowledge to enable them to make intelligent desisions. They are, however, not meant to replace the human specialists in any particular domain. A critical survey of recent work in interactive KBSs is reported. A case study (MYCIN) of a KBS, a list of existing KBSs, and an introduction to the Japanese Fifth Generation Computer Project are provided as appendices. Finally, an extensive set of KBS-related references is provided at the end of the report