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The presence of condensate inside the dryer cylinders of a paper machine produces a significant increase in the power required to accelerate the section, risking a drive overload and/or section shutdown. In previous works, the authors developed a robust estimation algorithm for the condensate power and a generalized correlation for the estimation of condensate loads in industrial dryer cylinders up to 55 mm of condensate rim. This paper presents and evaluates a rule-based expert system (ES) capable of detecting the presence and amount of condensate in up to three groups of cylinders with different condensate loads. The ES can give an early warning message to the operators and determine the condensate load and number of cylinders within each group. Evaluation is done using a combined condensate load for a fictitious 1500-mm eight-cylinder dryer section. Results show the capacity of the developed ES to give an early warning as well as a correct estimation of the condensate loads and number of cylinders involved. [ABSTRACT FROM PUBLISHER]

Linear programming formulations of forest ecosystem management (FEM) problems proposed in the 1960s have been adapted and improved upon over the years. Generating management alternatives for forest planning is a key step in building these models. Global forests are diverse, and a variety of models have been developed to simulate management alternatives. This paper describes iGen, a forest prescription generator that employs a rule-based system (AI-RBS), an AI technique that is often used for expert systems. iGen was designed with the goal of being able to generate management alternatives for virtually any FEM problem. The prescription generator is not designed for, adapted to, focused on—and ideally not limited to—any specific region, landscape, forest condition, projection method, or yield function. Instead, it aims to maximize generality, enabling it to address a broad range of FEM problems. The goal is that practitioners and researchers who do not have and do not want to develop their own alternative generator can use iGen as a prescription generator for their problem instances. For those who choose to develop their own alternative generators, we hope that the concepts and algorithms we propose in this paper will be useful in designing their own systems. iGen's flexibility can be attributed to three key features. First, users can define the state variable vector for management units according to the available data, models (production functions), and objectives of their problem instance. Second, users also define the types of interventions that can be applied to each type of management unit and create a rule base describing the conditions under which each intervention can be applied. Finally, users specify the equations of motion that determine how the state vector for each management unit will be updated over time, depending on which, if any, interventions are applied. Other than this basic structure, virtually everything in an iGen problem instance is user-defined. iGen uses these key elements to simulate all possible management prescriptions for each management unit and stores the resulting information in a database that is structured to efficiently store the output data from these simulations and to facilitate the generation of optimization models for ultimately determining the Pareto frontier for a given FEM problem. This article introduces iGen, illustrating its concepts, structure, and algorithms through two FEM example problems with contrasting forest management practices: natural regeneration with shelterwood harvests and plantation/coppice. For data and iGen source programs, visit github.com/SilvanaNobre/iGenPaper. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to study the impact of database accuracy on system learning. The paper assumes a basic model of an information system with a database, a rulebase, and an embedded machine learning approach that is used to add rules to the rulebase. The system learns from its database, changes to that database, and the examination of other databases. The results in this paper can be of use in the analysis of the design and behavior of such learning systems. It is found that the information system accuracy impacts the magnitude of a measure of goodness of individual roles. Thus, if only rules of a certain magnitude are kept, then some rules will be discarded because of database inaccuracy, unless that inaccuracy is accounted for. In addition, by accounting for database inaccuracy, the direction of the impact on measure of goodness can be determined. In some cases, the impact on the direction is monotonic. This finding allows us to understand the impact of database inaccuracy, without explicitly taking account of that inaccuracy. Further, information system accuracy can impact the resulting order of importance of rules, within a set of rules. Since only those higher-ranked rules are kept, database accuracy and measure of goodness can impact what rules are retained in the rulebase of the system. As a result, it is important to account for the information system accuracy in learning information systems. [ABSTRACT FROM AUTHOR]

Electrocardiography (ECG) is one of the most widely used recordings in clinical medicine. ECG deals with the recording of electrical activity that is generated by the heart through the surface of the body. The electrical activity generated by the heart is measured using electrodes that are attached to the body surface. The use of ECG in the diagnosis and management of cardiovascular disease (CVD) has been in existence for over a decade, and research in this domain has recently attracted large attention. Along this line, an expert system (ES) and decision support system (DSS) have been developed for ECG interpretation and diagnosis. However, despite the availability of a lot of literature, access to recent and more comprehensive review papers on this subject is still a challenge. This paper presents a comprehensive review of the application of ES and DSS for ECG interpretation and diagnosis. Researchers have proposed a number of features and methods for ES and DSS development that can be used to monitor a patient's health condition through ECG recordings. In this paper, a taxonomy of the features and methods for ECG interpretation and diagnosis were presented. The significance of the features and methods, as well as their limitations, were analyzed. This review further presents interesting theoretical concepts in this domain, as well as identifies challenges and open research issues on ES and DSS development for ECG interpretation and diagnosis that require substantial research effort. In conclusion, this paper identifies important future research areas with the purpose of advancing the development of ES and DSS for ECG interpretation and diagnosis. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) is impacting our world. In the 1970s and 1980s, Expert Systems (ES) consisted of AI systems that included explicit knowledge, often represented in a symbolic form (e.g., by using the Prologue language), that was extracted from human experts. Aiming to foster the interaction between two key ES areas, Knowledge Discovery (KD) and Business Intelligence (BI), a series of "Knowledge Discovery and Business Intelligence" (KDBI) tracks were held at the EPIA conference on Artificial Intelligence, with a total of six editions from 2009 to 2019. [Extracted from the article]

The final article by Yuan et al. (2022) explores the adoption value of deep learning combined with computed tomography (CT) imaging omics in the prediction of metastatic lymph nodes of nasopharyngeal carcinoma (NPC). Here, the key questions for understanding the performance of such deep neural networks could be (1) How effective can these neural networks detect a disease, via biomedical imaging? The next paper by Mansour et al. (2022) presents a novel AI based fusion model for CRC disease diagnosis and classification, named AIFM-CRC. [Extracted from the article]

The pre-choice stages of the problem solving process are more difficult to support through information systems than choice itself. Systems that facilitate problem formulation and solution finding are typically either expert system programs with narrow application domains or programs dealing with easily quantifiable problems. In contrast, this paper introduces a decision support system, GENI, that operates independent of domain, aiding the user in general problem solving tasks. The system's purpose is to support the problem solving process, rather than be a substitute for the human problem solver, by providing structure and by using different stimuli to prompt the user for data input. To deal with broad classes of complex qualitative problems, no single processing mode is sufficient. Hence the techniques in GENI differ mainly An earlier version of this paper was originally published in the Proceedings of the TwentyFourth Hawaii International Conference on System Sciences (IEEE Computer Society Press, 1991). This research was supported in part by the Social Sciences and Humanities Research Council of Canada and by the U.S. National Science Foundation (Grant SES-9016305). We thank Nick Keenan for his assistance in developing portions of the software described in the paper. in the interface, that is, the types of stimuli displayed, and the types of data recorded. GENI's user interface is built around a standard dialog function that can be modified by means of parameter settings. Experimental results show that differences in the interface content can lead to significant differences in problemsolver performance. [ABSTRACT FROM AUTHOR]

Technical systems for production are increasingly automate, which means that they have to work reliably. Therefore, the rapidly expanding concept of maintenance, where people would say that maintaining a process that allows the management of the technical condition and reliability during the entire life cycle of the system. Traditionally, maintenance has been considered as a support function, non-productive and not a core function adding little value to business. However it has been noticed that many manufacturing industries have used various approaches to improve maintenance effectiveness. An analysis of the problem of machines and industrial systems maintenance as well as maintenance strategies and maintenance management process is presented in this paper. Furthermore, a development process of traditional and modern maintenance management information system is given. The possibility of development maintenance management, applying decision support systems and expert systems, are presented, at the end. [ABSTRACT FROM AUTHOR]

Developing intelligent information systems has been a focus of recent research. A major component that makes a system intelligent is its learning capabilities. A learning system can adapt itself to new environments and improve its performance with minimum intervention from the developer. In this paper, we review major learning paradigms, examine the role of learning in intelligent information systems, and discuss potential research issues in integrating learning capabilities in information systems design. [ABSTRACT FROM AUTHOR]

There is growing recognition that the ability to provide automated support for unstructured decision making within organizations will require the integration of knowledge-based expert system techniques and traditional decision support system architectures. Several knowledge representations are applicable in the specification, management, and communication of knowledge associated with organizational planning, a classic ill-structured problem facing organizations. This paper describes the requirements for knowledge management in organizational planning. A knowledge-based planning system that has been implemented by the authors is presented. The system integrates data management, model management, and process management systems within a group decision support system environment. Knowledge management tools for describing, classifying, and storing the output of the planning process are described. Use of the system in the Management Information Systems (MIS) Planning and Decision Laboratory at the University of Arizona with a group of city planners is discussed. [ABSTRACT FROM AUTHOR]

Recent successes in the design and implementation of expert systems in non-managerial problem domains has spurred interest in the proposition that expert systems may be viable in managerial problem domains as well. Continuing research in the design, implementation, and testing of a prototype managerial expert system is described in this paper. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of implementing a context model for a smart environment. The problem has already been addressed several times using many different data- or problem-driven methods. In order to separate the modelling phase from implementation, we first represent the context model by a network of situations. Then, different implementations can be automatically generated from this context model depending on user needs and underlying perceptual components. Two different implementations are proposed in this paper: a deterministic one based on Petri nets and a probabilistic one based on hidden Markov models. Both implementations are illustrated and applied to real-world problems. [ABSTRACT FROM AUTHOR]

The paper proposes tools for diagnosing and forecasting port activities related to vehicle ferry transport on the example of the largest terminal located in the southern part of the Baltic Sea. It is a methodological proposal for a decision support system (DSS), subsequently used in practice. Diagnosing and forecasting port activities by entities managing ports is an obligation stemming directly from the Act on Ports and Harbours. Due to the size of operations, it is also necessary to undertake actions leading to accurate diagnoses and forecasts (e.g. concerning cargo volumes). Port authorities do not have specialized units within their structure to deal with this issue and while the analysis and reporting of cargo handling activities is carried out properly, the diagnosis and forecasting is usually done without a scientific approach. The article describes selected research tools (diagnoses and forecasts) based on statistical-econometric inference. The purpose of this paper is to propose specific tools for diagnosing and then forecasting the operation of ferry terminals for the need of decision support (DSS). The reason for undertaking the research was an inquiry of the ferry terminal in Świnoujście concerning the rationale for launching a new ferry (new supply). [ABSTRACT FROM AUTHOR]

This paper investigates a single period model for the analysis of the impact of the quality of the validation effort. The single period model uses a Bayesian approach to find that validation is a critical point process. That model is then extended to allow for the uncertainty of the validation process to determine the quality of the underlying model. Some monotonicity results are developed for the model and investigated in light of the process being a critical point process. The model indicates that, consistent with comments from real world settings, the impact of the quality of the validation effort can be substantial. The paper also presents two multiperiod models of the impact of the quantity of the validation effort. In practice, the development of an expert system may follow a recurring multiperiod life cycle, where a prototype is built, the system is validated to determine how well it performs, and based on that performance, is either funded or not funded. The first multiple period model assumes that validation and funding occurs at each point in the PVF budget cycle. The model employs Bayesian revision of probabilities to update the prior probability of obtaining a model with an appropriate level of success. It is found that the critical point for multiperiod problems is different than that for single period problems. This model forms the basis of the second model. The second multiple period model extends the first by assuming that the quantity of validation can be varied. The more validation, the more likely that flaws in the model will be found. Thus, the more validation, the better the understanding of the level of performance of the model. [ABSTRACT FROM AUTHOR]

This paper provides data on the first application of a prototype of the AXIS solution framework. AXIS (algorithms combined with knowledge systems in an interactive sequence) is a framework for interactively combining structured algorithms that seek a best solution with knowledge-based expert systems that seek expert heuristic solutions. This paper tests the framework using an interactive multiple objective integer programming algorithm combined with heuristics taken from the domain of aggregate production planning. The results indicate the AXIS framework can be successful in generating high quality solutions, in vastly reduced solution times compared to the structured algorithms, at much lower costs compared to the expert heuristics working alone. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of building decision support systems that make use of multiple operations research models as database applications. The motivation for developing applications in a database environment is that, by doing so, the development effort can be substantially reduced, while, at the same time, the application inherits valuable database features. The paper contains two main contributions. First, we present a set of modeling constructs that should aid developers in structuring such applications and in carrying out the development process. Included in this material is a fairly comprehensive model for handling versions. Second, we discuss certain design alternatives and evaluate performance tradeoffs associated with them. In addition to evaluating the differences among competing database designs, we provide evidence that properly designed database applications show little performance degradation over file based applications. [ABSTRACT FROM AUTHOR]

This article focuses on the automated dynamic audit programme tailoring approach. The choice of audit planning and program tailoring as the arena for the application of expert system technology is a fortunate one. Audit practice tends to use predecessor working papers and company practice as well as auditor experience as the primary source of procedure determination. The area of procedure planning is rich in knowledge and experience. Knowledge bases can be drawn from actual working papers, audit guides, and the knowledge of individuals. The first generation of systems focused on problems of narrower domain and used an evolving software technology. The second generation is using a richer set of software tools and the background of awareness of successful applications of expert systems in many domains of knowledge. Experience with the first generation of systems indicates enthusiasm and investment in the products and subsequent lack of implementation on the firmwide basis for most of the products.

The aim of this research is to create a medical expert system based on Bayes theorem to diagnose level of disc hernia based on real foot force measurement signals obtained using sensors and implement the whole system on field programable gate array (FPGA). We have created a database of attributes based on recorded foot force values of 33 patients pre-diagnosed with herniated disc on levels L4/L5 or L5/S1 on the left or right side. The results obtained by software (Matlab) and hardware (FPGA simulation) are matching well, achieving high accuracy, which shows that VHDL implementation of Naïve Bayes theorem for disc hernia diagnostics is adequate. The output on FPGA is easy to understand for any user, as it is implemented as four-bit output where the position of bit value 1 indicates the level of disc herniation. The system is able to distinguish between the healthy subjects and subjects with disc herniation and is able to detect if improvement in stability is present after surgery or physical therapy. Our proposed measurement platform can be coupled with FPGA to create a portable and not expensive tool for real time signal acquisition, processing and decision support system in disc hernia diagnosis and post-surgical recovery. [ABSTRACT FROM AUTHOR]

The Semantic Web shares many goals with Decision Support Systems (DSS), e.g., being able to precisely interpret information, in order to deliver relevant, reliable and accurate information to a user when and where it is needed. DSS have in addition more specific goals, since the information need is targeted towards making a particular decision, e.g., making a plan or reacting to a certain situation. When surveying DSS literature, we discover applications ranging from Business Intelligence, via general purpose social networking and collaboration support, Information Retrieval and Knowledge Management, to situation awareness, emergency management, and simulation systems. The unifying element is primarily the purpose of the systems, and their focus on information management and provision, rather than the specific technologies they employ to reach these goals. Semantic Web technologies have been used in DSS during the past decade to solve a number of different tasks, such as information integration and sharing, web service annotation and discovery, and knowledge representation and reasoning. In this survey article, we present the results of a structured literature survey of Semantic Web technologies in DSS, together with the results of interviews with DSS researchers and developers both in industry and research organizations outside the university. The literature survey has been conducted using a structured method, where papers are selected from the publisher databases of some of the most prominent conferences and journals in both fields (Semantic Web and DSS), based on sets of relevant keywords representing the intersection of the two fields. Our main contribution is to analyze the landscape of semantic technologies in DSS, and provide an overview of current research as well as open research areas, trends and new directions. An added value is the conclusions drawn from interviews with DSS practitioners, which give an additional perspective on the potential of Semantic Web technologies in this field; including scenarios for DSS, and requirements for Semantic Web technologies that may attempt to support those scenarios. [ABSTRACT FROM AUTHOR]

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Based on an analysis of the development of economic decision support systems, agents are applied to construct intelligent economic decision support systems. This paper proposes a task-oriented agent design concept and designs multiple types of agents to complete the decision-making tasks with the task as the core. The structure of multi-agent based systems is provided, and the concrete realization structure of different types of agents in the system is also provided. Additionally, this study discusses the operational mechanism of the whole system and the cooperation between multiple agents in the system. Finally, these functions are implemented through a combination of VC++ 6.0, multi-threading technology and the expert system tool CLIPS. The economic decision support system combines complex system theory, decision theory, information collection, knowledge discovery technology, economic decision making and simulation technology. It can aid users in making decisions by using communication and cooperation between multiple agents. [ABSTRACT FROM AUTHOR]

Distribution restoration process is done by switching actions of sectionalizing and tie switches on the feeders. The state of these switches can be determined by applying logical reasoning. Thus, knowledge-based systems are ideal tools to accomplish the task. In this paper, an expert system is developed by utilizing its fast reasoning mechanism and object-oriented features. The feeder component and configuration data are organized in a hierarchy way using the object-oriented programming paradigm. On the other hand, facts are used for representing the information during inference process. Service restoration procedure proposed in this paper takes load variation into consideration. The simulation results indicate that more solutions can be obtained for service restoration problems when load variation is considered. Another unique property of the proposed system is that it is capable of proposing multiple restoration plans. Multiple plans are important for the system operators since the best solution proposed by the system may not be workable in reality. By proposing multiple plans, the system operator can choose a plan which is more suitable to the real situation for service restoration problems. [ABSTRACT FROM AUTHOR]

This paper develops an integrated neural-network- based decision support system for predictive maintenance of rotational equipment. The integrated system is platform-independent and is aimed at minimizing expected cost per unit operational time. The proposed system consists of three components. The first component develops a vibration-based degradation database through condition monitoring of rolling element bearings. In the second component, an artificial neural network model is developed to estimate the life percentile and failure times of roller bearings. This is then used to construct a marginal distribution. The third component consists of the construction of a cost matrix and probabilistic replacement model that optimizes the expected cost per unit time. Furthermore, the integrated system consists of a heuristic managerial decision rule for different scenarios of predictive and corrective cost compositions. Finally, the proposed system can be applied in various industries and different kinds of equipment that possess well-defined degradation characteristics. [ABSTRACT FROM AUTHOR]

Effectively managing healthcare data is crucial for accurate diagnosis and personalized patient care. As the utilization of healthcare data grows for personalized care, concerns about reliability, privacy, and security have emerged. To address these issues, this research explores the fusion of analytical techniques with interactive visual representations, known as visual analytics, as a promising solution. The focus is on evaluating the trustworthiness of healthcare data in Kingdom of Saudi Arabia, particularly the capability of visual analytics tools in facilitating accurate and secure healthcare data analysis. This study tackles challenges such as the absence of specific evaluation criteria, the need to process vast healthcare datasets, the establishment of trust, and the necessity for automation. In response, a hybrid medical decision support system is introduced, leveraging hesitant fuzzy decision systems. The primary objective is to evaluate trustworthiness of visual analytics tools for disease diagnosis within healthcare data. Within the framework of hesitant fuzzy logic, the paper employs a medical multi-criteria decision-making system that integrates the analytic network process and the technique for order of preference by similarity to an ideal solution. Rigorous validation ensures the accuracy and reliability of the findings. The research not only provides valuable insights but also conducts comparative analyses of the proposed models against existing ones, demonstrating the practicality of optimal decision-making in Saudi Arabia environment of healthcare scenarios. Several popular alternatives of healthcare based tools have been used in this study such as Tableau, JupyteR, Zoho Reports, QlikView, Visual.ly, DOMO BI, SAS Visual Analytics. From the results achieved DOMO BI visual analytics tool is found to be most secure and robust tool for healthcare professionals. This effort aims to enhance patient care and outcomes, ultimately contributing to the improvement of the overall healthcare landscape in Saudi Arabia. [ABSTRACT FROM AUTHOR]

Objective: In order to comprehensively understand the characteristics of Adaptive Business Intelligence (ABI) in Healthcare, this study is structured to provide insights into the common features and evolving patterns within this domain. Applying the Sheridan's Classification as a framework, we aim to assess the degree of autonomy exhibited by various ABI components. Together, these objectives will contribute to a deeper understanding of ABI implementation and its implications within the Healthcare context. Methods: A comprehensive search of academic databases was conducted to identify relevant studies, selecting AIS e-library (AISel), Decision Support Systems Journal (DSSJ), Nature, The Lancet Digital Health (TLDH), PubMed, Expert Systems with Application (ESWA) and npj Digital Medicine as information sources. Studies from 2006 to 2022 were included based on predefined eligibility criteria. PRISMA statements were used to report this study. Results: The outcomes showed that ABI systems present distinct levels of development, autonomy and practical deployment. The high levels of autonomy were essentially associated with predictive components. However, the possibility of completely autonomous decisions by these systems is totally excluded. Lower levels of autonomy are also observed, particularly in connection with prescriptive components, granting users responsibility in the generation of decisions. Conclusion: The study presented emphasizes the vital connection between desired outcomes and the inherent autonomy of these solutions, highlighting the critical need for additional research on the consequences of ABI systems and their constituent elements. Organizations should deploy these systems in a way consistent with their objectives and values, while also being mindful of potential adverse effects. Providing valuable insights for researchers, practitioners, and policymakers aiming to comprehend the diverse levels of ABI systems implementation, it contributes to well-informed decision-making in this dynamic field. [ABSTRACT FROM AUTHOR]

Every morning, water suppliers need to define their pump schedules for the next 24 h for drinking water production. Plans must be designed in such a way that drinking water is always available and the amount of unused drinking water pumped into the network is reduced. Therefore, operators must accurately estimate the next day's water consumption profile. In real-life applications with standard consumption profiles, some expert system or vector autoregressive models are used. Still, in recent years, significant improvements for time series prediction have been achieved through special deep learning algorithms called long short-term memory (LSTM) networks. This paper investigates the applicability of LSTM models for water demand prediction and optimal pump control and compares LSTMs against other methods currently used by water suppliers. It is shown that LSTMs outperform other methods since they can easily integrate additional information like the day of the week or national holidays. Furthermore, the online- and transfer-learning capabilities of the LSTMs are investigated. It is shown that LSTMs only need a couple of days of training data to achieve reasonable results. As the focus of the paper is on the real-world application of LSTMs, data from two different water distribution plants are used for benchmarking. Finally, it is shown that the LSTMs significantly outperform the system currently in operation. [ABSTRACT FROM AUTHOR]

Expert systems often employ a weight on rules to capture conditional probabilities. For example, in classic rule-based settings, Pr(h|e) = x is used to mean "If e is known to be true then conclude h is true with probability x." Further, other probability-based approaches, such as influence diagrams and Bayes' Nets are increasingly being used to support decision making through decision support systems. Although algorithms for these systems have received substantial attention, less attention has been given to knowledge acquisition of probabilities used in these systems. However, the underlying probabilities are critical because they lead the user to particular solutions. Accordingly, the purpose of this paper is to investigate the quality of probability knowledge when it is acquired from groups or individuals.This paper summarizes the results of an empirical cognitive study on the ability of individuals and groups to provide consistent sets of probabilities Pr(A), Pr(B), Pr(A|B) and Pr(B|A). The analysis of these probabilities allowed the study of the ability of subjects to account for Bayes' theorem reversals, a basic assumption made by virtually all algorithms. It was found that knowledge acquisition from groups provided more correct orderings to the probabilities than knowledge acquisition from individuals. This suggests that knowledge acquisition from groups is more likely to obtain correct probability knowledge. [ABSTRACT FROM AUTHOR]

Decision support system (DSS) researchers and designers continue to look for unstructured organizational tasks where there is a critical need for intelligent computer-based support. One such decision task is information requirements determination. Requirements determination is recognized as the most crucial phase of the systems development life cycle. Unfortunately, most methodologies and CASE tools focus only on how to specify the requirements once they are determined. There is very little computer support for the process of determining requirements. This paper discusses the conceptual design and development of a knowledge-based DSS to support information analysts in the critical decision task of determining requirements for the design of effective information systems. The expert modeling support system has the expertise to assist the analyst in studying the organization as a whole and in modeling the system under study in the context of the overall organization's goals and needs. The focus of the paper is on the problems associated with building the knowledge base component of the intelligent decision support system. A prototype implementation of the system is described. [ABSTRACT FROM AUTHOR]

Drawing upon the choice models developed in the multiple criteria decision making (MCDM) area, this paper proposes an architecture for designing an intelligent decision support system (DSS) that is intended to aid in making choices among multiple alternatives along multiple dimensions. It argues that effective support can be provided to the decision maker when the knowledge-based DSS is capable of dynamically selecting choice models appropriate to the domain and context of a particular problem being specified by the decision maker, and of properly applying them to the problem solution. Development of a prototype intended to partially represent application of the architecture is described. The paper concludes with suggestions for research extensions. [ABSTRACT FROM AUTHOR]

Decision support systems (DSSs) are more complex than most other traditional decisionaid systems. For what types of problems are they more effective, and what design characteristics make them more effective? The laboratory experiment reported here examined the effect of three design characteristics of these systems in the context of decision makers faced with ill-structured problems. The characteristics were presence or absence of decision-aid heuristics, degree of interaction between the user and the system, and whether or not the system was computerized. The dependent variables were (1) quality of user performance, (2) user productivity of ideas, (3) user confidence in the quality of his/her performance, (4) user satisfaction with the decision aid or support system, (5) changes in user attitude toward the problem addressed, and (6) changes in user attitude toward computers. Use of heuristics and increased interaction had positive effects on decision quality, user productivity, and attitude toward computers; they had negative effects on user confidence, satisfaction, and attitude toward the problem addressed. Whether or not the system was computerized did not have a significant effect on any dependent variable The findings concerning negative effects, in particular, suggest the need for research on the design of heuristics for addressing ill-structured problems—heuristics that will deliver the positive but not the negative effects observed in this study. The findings also suggest the need for research on how to benefit from computers in the context of solving ill-structured problems. [ABSTRACT FROM AUTHOR]

This paper describes and evaluates three different approaches to building decision support systems: the Operations Research/Management Science approach, the Decision Analysis/Multiattribute Utility approach, and the Artificial Intelligence/Expert Systems approach. It evaluates the usefulness of the three approaches for risk management. In particular, it defines evaluation objectives of risk analysts, risk managers, and laypeople and provides a subjective assessment how the three approaches stack up against their objectives. The paper concludes that for most risk management applications a combination of the three approaches would be most desirable. [ABSTRACT FROM AUTHOR]

Transmission system restoration is a critical task which requires agility and safety. On the one hand, the restoration proceedings, composed of numerous documents, establish the sequence and its preconditions that must be observed before the execution of each action. On the other hand, a significant amount of information, such as alarms and protection signals, fills the operator's HMI and causes an effect known as "alarm avalanche." The time spent to understand all of this information impacts the result of the restoration, while any erroneous preconditions assessment can undermine the restoration process. In order to conduct an efficient restoration process for power transmission systems which improves both security and agility, an online decision support system inspired by assisted-healing approach is proposed. The system is also based on the expert systems methodology through which the inputs (alarms, state of circuit breakers, protection signals, and operation proceedings) are submitted to a large set of rules (a multilayer inference engine) to be transformed into intelligible outputs for operators. These outputs instantaneously give operators the full set of conclusions necessary for decision making with the advantage of preventing misunderstandings. This paper presents an applied research whose results and simulations performed at Cemig Generation and Transmission show that it is possible to automate the processing of all these data so that operators have better information to make decisions during these critical situations. [ABSTRACT FROM AUTHOR]

Abstract: The problem of improving the efficiency of designing process of knowledge bases and expert systems remains a relevant issue. The effectiveness of this process can be improved by using the principles of conceptual (cognitive) modelling and generative programming‐based approaches, in particular, a model‐driven development. This paper describes implementation and application of the model‐driven development approach for the design of rule‐based expert systems and knowledge bases. The implementation proposed takes into account peculiarities of the intelligent systems development process, such as stages of conceptualization and formalization, and provides the practical use of ontologies and conceptual models as computation‐independent and subject domain models. An extension of UML, namely, the Rule Visual Modelling Language is used to represent logical rules and create platform‐independent and platform‐specific models. Rule Visual Modelling Language elements ensure that some features of a programming language for knowledge bases, for example, CLIPS, are taken into account. Therefore, CLIPS and the Personal Knowledge Base Designer are used as the targeted platforms for generation of source codes and specifications. The applicability of the approach is demonstrated by a case study: development of the knowledge base and expert system for identification of the causes of damages and destruction of construction materials in petrochemistry. [ABSTRACT FROM AUTHOR]

Decision making in a semistructured environment often involves the use of quantitative, structured analysis along with the qualitative judgment of an expert. Decision support systems and expert systems are often developed to assist in this judgment process. The hybrid information system described in this paper combines a statistical model with a rule-based expert system in order to integrate the quantitative and qualitative aspects of decision making. The GC Advisor hybrid system is designed for use by auditors to assess the ability of the client firm to continue as a going concern. The guidelines for expert system validation given in previous literature are then applied to the validation of GC Advisor. [ABSTRACT FROM AUTHOR]

Probabilistic networks, used as an adjunct or alternative to the logical models used in artificial intelligence (Al) and decision support systems (DSS), offer a way to compactly represent a distribution over a set of random variables. Nonetheless, the specification of a given network may require conditional probabilities that are simply unavailable. In this paper a means for analyzing incompletely specified networks is presented, and some general rules are derived from the application of the method to some simple networks. The use of the technique in MIS settings is illustrated. [ABSTRACT FROM AUTHOR]

Knowledge-based systems are widely used for decision support system and expert system applications. However, they are not a panacea for all business problems. Thorough requirements analysis methods must determine the need for knowledge-based systems vis-á-vis alternative information system designs. Furthermore, knowledge-based systems are often used as embedded subsystems within larger organizational information systems. Rigorous design methods must support the development of complex information systems with integrated, heterogeneous subsystems, including knowledge-based subsystems. In this paper, we demonstrate that the box structure methods of information systems development support both the requirements analysis of knowledge-based systems and the design of embedded knowledgebased systems. An extended example is presented to show the use of the box structure methods in the development of a financial planning system that requires embedded knowledge-based system components. [ABSTRACT FROM AUTHOR]

Decision Support Systems (DSS) are now an established part of the information systems mainstream, both in research and in practice. The DSS development approach differs from traditional systems development not only with regard to its associated paradigms and orientation, but also in the methods of requirements analysis it employs. This paper surveys the major methodologies used for DSS development. It analyzes them by structure, paradigm, and orientation, and discusses their underlying assumptions. A research model is then developed and used to relate the functions of DSS methodology to decision-making environments and the relevant processes within them. Finally, a contingency theory is presented to show how each of the surveyed methodologies reduces the lack of structure in the decision-making environment, and bow this may be used by DSS developers for selecting a DSS methodology. Other components of the research model are discussed, to identify further prerequisites for improved understanding of the DSS role and application. [ABSTRACT FROM AUTHOR]

Decision Support Systems (DSS) and Expert Systems (ES) are both aimed at improving decision making. Current DSS usually concentrate on quantitative models while ES emphasize logic and reasoning. In this paper, we review previous ES approaches used by DSS to handle decision models. We propose an extension to such systems, an Agent for Intelligent Model Management (AIMM), as an interface between DSS and the users. AIMM utilizes a combination of ES knowledge representations and reasoning mechanisms to make a wide variety of models available to decision makers so that they may apply these models without becoming involved in technical or procedural aspects of implementation. A prototype of the system for financial problems is described. [ABSTRACT FROM AUTHOR]

This paper addresses the treatment of uncertainty in expert system shells. A review of the modeling of uncertainty by expert system shells is presented. An experiment to replicate earlier work investigating the manner in which individuals manipulate certainty factors in comparison to commercial shells is discussed. Comparisons are made between seven commercial shells, both personal computer (PC) and mainframe based, and individuals. A significant difference between individuals and shells as well as among shells themselves is indicated. Some implications for both expert system and decision support system (DSS) methodologies are discussed. [ABSTRACT FROM AUTHOR]

Over the last decade there have been several efforts at building knowledge-based "expert systems," mostly in the scientific and medical arenas. Despite the fact that almost all such systems are in their experimental stages, designers are optimistic about their eventual success. In the last few years, there have been many references to the possibility of expert systems in the management literature. However, what is lacking is a clear theoretical perspective on how various management problems differ in nature from problems in other domains and the implications of these differences for knowledge-based decision support systems for management. In this paper, I examine some of these differences, what they suggest in terms of the functionality that a computer-based system must have in order to support organizational decision making, and the scope of such a system as a decision aid. The discussion is grounded in the context of a computer-based system called PLANET that exhibits some of the desired functionality. [ABSTRACT FROM AUTHOR]

Extensive coverage of knowledge-based languages has appeared in the recent literature. However, there has been no discussion of the criteria to be used in selecting between a knowledge-based approach and a traditional, that is, non-knowledge-based, approach for a particular application. This paper presents a framework of application-based criteria to assist in this selection. It also applies this decision framework to a number of real and hypothetical applications. [ABSTRACT FROM AUTHOR]

Many decisions are made in interactive situations in which decision makers interact and may also affect each other's decision outcomes. In order to model decision makings in such interactive situations, this paper incorporates game theory into influence diagrams and presents a new approach, called game theory-based influence diagrams (GIDs). GIDs consider an extra factor, that of the choice of strategies made by other decision makers, to the list of determinants that influence the decision-making process of each decision maker, so that decision makers can make more rational decisions. As a result of integrating influence diagrams and game theory, GIDs benefit from the simplicity and efficiency of influence diagrams for modelling complex decision problems as well as the rationality and suitability of applying game theory for making decisions in dynamic interactive scenarios. This paper also introduces genetic algorithm-based methods to evaluate GIDs. Experimental studies have been performed for validation and evaluation. [ABSTRACT FROM AUTHOR]

An increasing number of decision support systems based on domain knowledge are adopted to diagnose medical conditions such as diabetes and heart disease. It is widely pointed that the classical ontologies cannot sufficiently handle imprecise and vague knowledge for some real world applications, but fuzzy ontology can effectively resolve data and knowledge problems with uncertainty. This paper presents a novel fuzzy expert system for diabetes decision support application. A five-layer fuzzy ontology, including a fuzzy knowledge layer, fuzzy group relation layer, fuzzy group domain layer, fuzzy personal relation layer, and fuzzy personal domain layer, is developed in the fuzzy expert system to describe knowledge with uncertainty. By applying the novel fuzzy ontology to the diabetes domain, the structure of the fuzzy diabetes ontology (FDO) is defined to model the diabetes knowledge. Additionally, a semantic decision support agent (SDSA), including a knowledge construction mechanism, fuzzy ontology generating mechanism, and semantic fuzzy decision making mechanism, is also developed. The knowledge construction mechanism constructs the fuzzy concepts and relations based on the structure of the FDO. The instances of the FDO are generated by the fuzzy ontology generating mechanism. Finally, based on the FDO and the fuzzy ontology, the semantic fuzzy decision making mechanism simulates the semantic description of medical staff for diabetes-related application. Importantly, the proposed fuzzy expert system can work effectively for diabetes decision support application. [ABSTRACT FROM AUTHOR]

This paper describes a novel approach in the area of evaluating suitable job applicants for various job positions, and specifies typical areas of requirement and their usage. Requirements for this decision-support system are defined in order to be used in middle-size companies. Suitable tools chosen were fuzzy expert systems, primarily the inference system Takagi-Sugeno type, which were then supplied with implementation of methods of variant multi-criteria analysis. The resulting system is a variable tool with the possibility to simply set the importance of individual selection criteria so that it can be used in various situations, primarily in repeated selection procedures for similar job positions. A strong emphasis is devoted to the explanatory module, which enables the results of the expert system to be used easily. Verification of the system on real data in cooperation with a collaborating company has proved that the system is easily usable. [ABSTRACT FROM AUTHOR]

Aggregation plays a central role as a means of combining all opinions that are expressed by group members in a group decision support system (GDSS). In this paper, we propose a new r-ASA (Aggregation based on Situation Assessment) algorithm to reflect a decision situation in the aggregation process. Al- though some research findings showed the importance of cultural factors in GDSS, most of them did not present how to implement cultural factors in GDSS. To handle this problem, we present a proposal for reflecting status differentials in GDSS. It may be used to reflect cultural differences in GDSS design. It is a further step toward the integration of cultural factors into existing GDSS design. [ABSTRACT FROM AUTHOR]

Identification and classification of voltage and current disturbances in power systems are important tasks in the monitoring and protection of power system. Most power quality disturbances are non-stationary and transitory and the detection and classification have proved to be very demanding. The concept of discrete wavelet transform for feature extraction of power disturbance signal combined with artificial neural network and fuzzy logic incorporated as a powerful tool for detecting and classifying power quality problems. This paper employes a different type of univariate randomly optimized neural network combined with discrete wavelet transform and fuzzy logic to have a better power quality disturbance classification accuracy. The disturbances of interest include sag, swell, transient, fluctuation, and interruption. The system is modeled using VHSIC Hardware Description Language (VHDL), a hardware description language, followed by extensive testing and simulation to verify the functionality of the system that allows efficient hardware implementation of the same. This proposed method classifies, and achieves 98.19% classification accuracy for the application of this system on software-generated signals and utility sampled disturbance events. [ABSTRACT FROM AUTHOR]

In this paper and attached video, we present a third-generation expert system named Knowledge Amplification by Structured Expert Randomization (KASER) for which a patent has been filed by the U.S. Navy's SPAWAR Systems Center, San Diego.1 CA (SSC SD). KASER is a creative expert system. It is capable of deductive, inductive, and mixed derivations. Its qualitative creativity is realized by using a tree-search mechanism. The system achieves creative reasoning by using a declarative representation of knowledge consisting of object trees and inheritance. KASER computes with words and phrases. It possesses a capability for metaphor-based explanations. This capability is useful in explaining its creative suggestions and serves to augment the capabilities provided by the explanation subsystems of conventional expert systems. KASER also exhibits an accelerated capability to learn. However, this capability depends on the particulars of the selected application domain. For example, application domains such as the game of chess exhibit a high degree of geometric symmetry. Conversely, application domains such as the game of craps played with two dice exhibit no predictable pattern, unless the dice are loaded. More generally, we say that domains whose informative content can be compressed to a significant degree without loss (or with relatively little loss) are symmetric. Incompressible domains are said to be asymmetric or random. The measure of symmetry plus the measure of randomness must always sum to unity. [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]