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This paper describes the development of an integrated intelligent (expert) system and conventional information system to support decision making of a diagnostic decision in a multi-agent environment. It argues that many a priori requirements for the information system can be elicited by accounting for the specific nature of the diagnostic decision and for the single/multiple-actor environment. This finding is generalized to a framework that indicates that a priori requirements take into account the basic theories on which the information system supporting the decision task is ultimately based. In particular, attention is focused on the nature of the task (e.g. diagnostic), whether the application is for a single- or multiple-agent situation and the nature of the multi-agent structure (e.g. product or functional form of organization). [ABSTRACT FROM AUTHOR]

The process of translating objectives into actions is a difficult task. This difficulty is due to the wide range of possibilities and the lack of structured information. Managers must take into account relevant information and generate a range of options before a decision is reached. So far, little is available to guide managers in translating a set of objectives into actions. This paper presents a three-stage action-planning process to address this gap. The process, supported by a software tool, takes managers through the stages of model building, action generation, and action evaluation and selection. A case study illustrates the application of the process. The paper concludes by discussing the implication of this work for managers and academics. [ABSTRACT FROM AUTHOR]

Dental trauma in children and adolescents is a common problem, and the prevalence of these injuries has increased in the last 10-20 years. A dental injury should always be considered an emergency and, thus, be treated immediately to relieve pain, facilitate reduction of displaced teeth, reconstruct lost hard tissue, and improve prognosis. Rational therapy depends upon a correct diagnosis, which can be achieved with the aid of various examination techniques. It must be understood that an incomplete examination can lead to inaccurate diagnosis and less successful treatment. Good knowledge of traumatology and models of treatments can also reduce stress and anxiety for both the patient and the dental team. Knowledge-based Systems (KBS) are a practical implementation of Artificial Intelligence. In complex domains which humans find difficult to understand, KBS can assist in making decisions and can also add knowledge. The aim of this paper is to describe the structure of a knowledge-based system for structured examination, diagnosis and therapy for traumatised primary and permanent teeth. A commercially available program was used as developmental tool for the programming (XpertRule, Attar, London, UK). The paper presents a model for a computerised decision support system for traumatology. [ABSTRACT FROM AUTHOR]

This paper describes INSOLVE--an expert system for corporate recovery decisions. INSOLVE was built to understand the decision-making processes of corporate recovery experts who deal with companies in financial difficulties. INSOLVE has been developed using a multi-phase process similar to that widely adopted in software engineering. The expert system is described in terms of the assessment task and interpretation models of CommonKADS. The detailed results of the validation of INSOLVE with 17 experts show that it is an accurate model of human expertise in this domain. [ABSTRACT FROM AUTHOR]

Of all of the new technologies emerging in the late 20th century, the production of artificial intelligence may provide the most profound impacts on organizational decision making. Because the development of artificial intelligence technologies and models has largely been based on psychological models of human cognition, the effects of their implementation in complex social settings have not been thoroughly examined. This paper is an attempt to generate research which will develop a comprehensive understanding of the impacts of artificial intelligence and its role in complex organizations. A set of 11 hypotheses has been developed which examine the relationships between artificial intelligence technologies and the dimensions of organizational decision making. It is argued here that the implementation of expert systems will lead to less complex and political decision processes, while the implementation of natural language systems will lead to more complex and political decision processes. [ABSTRACT FROM AUTHOR]

Fuzzy logic has been used as a means of interpreting vague, incomplete and even contradictory information into a compromised rule base in artificial intelligence such as machine decision–making. Within this context, fuzzy logic can be applied in the field of expert systems to provide additional flexibilities in constructing a working rule base: different experts’ opinions can be incorporated into the same rule base, and each opinion can be modeled in a rather vague notion of human language. As some illustrative application examples, this paper describes how fuzzy logic can be used in expert systems. More precisely, it demonstrates the following applications: (i) a healthcare diagnostic system, (ii) an autofocus camera lens system and (iii) a financial decision system. For each application, basic rules are described, the calculation method is outlined and numerical simulation is provided. These applications demonstrate the suitability and performance of fuzzy logic in expert systems. [ABSTRACT FROM AUTHOR]

Research has recently focused on the effort-reduction or minimization role of computerized decision aids, and how users may employ aids m manage their effort, which in mm affects their choice of decision strategies. In this paper, it is argued that consideration of effort reduction or minimization by itself is not sufficient for inducing changes in decision strategy. Instead, decision aid effects on effort must be considered jointly with the decision quality associated with the various decision strategies. This is true even if the decision aid has no effect on decision quality. We adapt and extend a theoretical framework that can be used to evaluate the joint effects of effort and quality on decision strategy choice. In addition, we reinterpret past research results in light of the framework and present new experimental evidence on the descriptive validity of the framework. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Many studies have shown that decision tree induction methods could be used to determine rules for expert systems. Pruning techniques are often used to increase the accuracy of an induced decision tree over the instance space. While recent results of decision tree induction show that large samples may be required to induce a decision tree of small error, recent expository studies have used very small sample sizes. In such cases it is of value to obtain a posterior evaluation of the error of the induced concept. In this paper we give three methods to estimate the accuracy of a pruned decision tree. The first method assumes uniform prior distribution. For those cases where uniform prior is not appropriate, we develop a method to obtain appropriate prior using a beta distribution. Finally, we provide a general bound which requires no assumption over the instance space. These results can be used when a pruned decision tree is used to classify the original domain or another close domain. [ABSTRACT FROM AUTHOR]

This paper proposes modelbase construction mechanisms in a decision support system (DSS) for representing and managing models of diverse management science/operations research modeling paradigms, using object-oriented database management systems (ODBMS) constructs. It focuses on the construction of a modelbase that maintains logical independence among the DSS components including modelbase, database, and solvers, but relieves the mismatching characteristics by facilitating intelligent and stabilized integration of them. As a conceptual framework to build such a modelbase, this research uses generic model concepts, and adopts structured modeling language (SML) as a paradigm-neutral model representation sublanguage. In the modelbase, three model abstraction layers including model type, model structure, and model instance are devised to facilitate the capture of multiple modeling paradigms and specific application models in different instantiation levels. The constructs and methods discussed are flexible enough to be applied to a wide variety of decision-making and problem-solving paradigms. A prototype system is developed under a commercial ODBMS called OBJECTSTORE with the C++ programming language, and diverse model manipulation commands are illustrated by an object-oriented structured query language (OSQL). [ABSTRACT FROM AUTHOR]

One justification for eliciting and representing the judgment strategy of an expert in a rule-based expert system (RBES) is to facilitate knowledge transfer to individuals with less expertise. However, prior research suggests complexities and potential problems when using RBESs for training. In this paper, a conceptual framework of user learning from RBESs is presented. It is proposed that learning may be ineffective when the problem representation of the RBES is inconsistent with the user's mental representation of the task-domain knowledge. An experiment was conducted to examine the effects of consistency (inconsistency) between the problem-solving strategy of RBESs and individuals' memory structures. Groups of subjects whose memory structure either matched or did not match two RBESs' problem-solving strategies were examined using an internal control evaluation task. The results suggest that learning was facilitated only for groups with congruence between the RBES's problem-solving strategy and a subject's memory structure. [ABSTRACT FROM AUTHOR]

This paper discusses two principles that have become increasingly important in the design of knowledge-based systems: domain-specific knowledge used to support opportunistic reasoning and hierarchical organization structure used to control and coordinate problem solving activity. We propose a design framework that embodies these two principles and describe how this framework has been used to develop a knowledge-based job-shop scheduling system. This system, called OPIS 0, has undergone limited testing in an experimental environment modeled after an actual job shop. Its performance has been very good compared to ISIS and to the more traditional approach of constructing a schedule by dispatching jobs using the COVERT priority rule. The resulting design also shows potential for use in a decision support role. [ABSTRACT FROM AUTHOR]

This paper traces the development of the concept of nursing diagnosis and the various approaches being used to explain the cognitive processes used by practitioners in diagnosing patient problems. Three main types of explanation are compared; hypothesis generation/testing, decision analysis, and the information processing model. The recent development of the latter approach within the field of artificial intelligence is described and expert system research introduced. Finally, the potential benefits of the advent of expert systems into nursing are discussed. [ABSTRACT FROM AUTHOR]

Construction of an expert system is frequently studied in knowledge engineering. Hence, in constructing sophisticated expert systems, analysis of the human thinking process and the human memory mechanism is essential. This is because humans in the teal world have problem-solving abilities. This paper presents the expert system "MUSAS" (musical arrangement system) for musical arrangement, to study the mechanisms and processes in human problem solving. Since musical arrangements have both a theoretical and emotional side, the former being text knowledge and the latter being heuristic, text knowledge is used to propose chords. Finally, the basic construction of MUSAS is given, and the method of application of a knowledge-based system for musical arrangement along with the system process and experimental results are presented. [ABSTRACT FROM AUTHOR]

The purpose of this study is to evaluate a hybrid system as a decision support model to assist with the auditor's going-concern assessment. The going-concern assessment is often an unstructured decision that involves the use of both qualitative and quantitative information. An expert system that predicts the going-concern decision has been developed in consultation with partners at three of the Big Five accounting firms. This system is combined with a statistical model that predicts bankruptcy, as a component of the auditor's decision, to form a hybrid system. The hybrid system, because it combines the use of quantitative and qualitative information, has the potential for better prediction accuracy than either the expert system or statistical model predicting separately. In addition, testing of the system provides some insight into the characteristics of firms that experience problems, but do not necessarily receive a going-concern modification. Further investigation into those firms that have problems could reveal factors that may be incorporated into decision support systems for auditors, in order to improve accuracy and reliability of these decision tools. [ABSTRACT FROM AUTHOR]

The use of continuous multivariate models to represent experts' knowledge of relations among a set of variables is reviewed. Such knowledge models can be incorporated into expert systems, complementing contingent rules, especially when representing experts' knowledge of functional relations among entities in noisy domains. Past work has most commonly involved linear averaging models in static domains, although nonlinear models and dynamic domains are also possible. Detecting errors in continuous multivariate models requires a different approach from detecting errors in collections of if-then rules. Methods for eliciting expert knowledge include modeling judgments made in real or hypothetical situations and using expert's self-insight directly to assist in construction of the model. Procedures for managing each of these methods have been computerized and could be included as elicitation tools in expert system building environments. [ABSTRACT FROM AUTHOR]

Logistics managers frequently utilize decision support systems (DSS) to make facility network design decisions. Many DSS do not provide optimization capabilities, but instead rely on scenario evaluation as a means for developing solutions. We experimentally assessed the performances of decision makers, including experienced managers, who used four variants of a scenario evaluation-based DSS to solve realistically sized network design problems of varying complexities. Complexity factors included DSS attributes, problem size, network types, and demand dispersion patterns. Decision makers' performances were assessed relative to optimal solutions. Overall, the decision makers generated relatively high-quality solutions using the DSS variants. The type of design problem solved did not significantly impact problem-solving performance. However, performance degraded and variability in solution quality escalated as problem size was increased. The availability of incremental solution cost improvement cues in the DSS significantly improved solution quality and reduced performance variability! Iconic graphic enhancements to the DSS did not consistently affect performance. However, significant interactions existed among the effects of DSS graphics capabilities, DSS information cues, and problem attributes. [ABSTRACT FROM AUTHOR]

A number of recent studies have compared the performance of neural networks (NNs) to a variety of statistical techniques for the classification problem in discriminant analysis. The empirical results of these comparative studies indicate that while NNs often outperform the more traditional statistical approaches to classification, this is not always the case. Thus, decision makers interested in solving classification problems are left in a quandary as to what tool to use on a particular data set. We present a new approach to solving classification problems by combining the predictions of a well-known statistical tool with those of an NN to create composite predictions that are more accurate than either of the individual techniques used in isolation. [ABSTRACT FROM AUTHOR]

A learning-by-example algorithm, the ordinal learning model (OLM), that automatically generates symbolic rule-bases from examples was applied to four real-world multiattribute ordinal problem domains. The model automatically generates consistent and irredundant symbolic classification rules that mimic, in many aspects, the behavior of human subjects who solved similar problems during empirical studies. The OLM's performance is compared with those of regression analysis and with C4, a well-known symbolic learning-by-example decision tree building algorithm. The OLM uses mainly comparison operations and does not attempt to optimize the rule-bases it generates. Yet, the results show that the OLM's predictions are very accurate and the resulting rule-bases are relatively compact. The time required for constructing the rule-bases via the OLM was very competitive as well. [ABSTRACT FROM AUTHOR]

Knowledge-based systems support the decision-making process with the help of domain specific knowledge bases. The knowledge bases almost always have uncertainty associated with them. A variety of approaches have been proposed in the artificial intelligence (AI) literature for the construction of and reasoning with uncertain knowledge bases. Building on this stream of research, we focus on how stochastic simulation can be used to construct and reason with knowledge bases that have uncertainties. An advantage of the simulation methodology is that it may not have to make many of the assumptions made by other approaches. It also allows the designer of the knowledge-based system to control the methodology based on accuracy and time requirements. The simulation approach to knowledge base construction is a modified version of the concept induction procedure used in AI. However, it incorporates, as does simulation modeling, statistical tests to identify the best rule that describes the relationship among the variables. We show that when simulation is used to reason with uncertain knowledge bases, under certain conditions, the number of simulation trials needed to achieve a given level of accuracy is independent of the characteristics, such as the size, of the knowledge base. Empirical results obtained from an experiment confirm our theoretical results and provide evidence that simulation methodology is practical for real life knowledge-based systems. [ABSTRACT FROM AUTHOR]

Recent advances in information systems technology have made it possible to support the work of interacting groups using networked personal computers. A laboratory experiment was conducted using a group decision support system to evaluate effects of anonymity and proximity on group process in automated group problem solving. Twenty groups of four persons each performed an idea-generating task using an interactive electronic brainstorming program. This experiment's main findings were (1) Group members working anonymously and apart generated more comments. (2) Working in the same room increased satisfaction. (3) Highest levels of perceived system effectiveness were reported under anonymity. [ABSTRACT FROM AUTHOR]

This article examines the potential for synergy between decision support systems (DSS) and expert systems research. Three predominant research traditions in DSS (applications, design, and technology) are examined and used as a basis to identify ways in which expert systems and DSS research interrelate. A range of emerging trends in DSS research that focus on these commonalities is discussed. [ABSTRACT FROM AUTHOR]

Medical therapy for cardiac arrhythmias is still to a large extent based on empirical methods. Assessing and evaluating different therapeutical strategies constitutes the starting point for inducing decision methods to select the appropriate regimen for an individual patient. We designed a computer-based system that establishes a set o/heuristic rules linking attributes in a data base of patients with rhythm disturbances. A feasibility analysis conducted on a small set of 23 patients indicated that constraints on the number of attributes and their clinical relevancy together with a representation scheme for temporal changes have to be incorporated to provide for a useful and efficient algorithm. [ABSTRACT FROM AUTHOR]