Abstract: Rights management information is used at least in part in a matching, narrowcasting, classifying and/or selecting process. A matching and classification utility system comprising a kind of Commerce Utility System is used to perform the matching, narrowcasting, classifying and/or selecting. The matching and classification utility system may match, narrowcast, classify and/or select people and/or things, non-limiting examples of which include software objects. The Matching and Classification Utility system may use any pre-existing classification schemes, including at least some rights management information and/or other qualitative and/or parameter data indicating and/or defining classes, classification systems, class hierarchies, category schemes, class assignments, category assignments, and/or class membership. The Matching and Classification Utility may also use at least some rights management information together with any artificial intelligence, expert system, statistical, computational, manual, or any other means to define new classes, class hierarchies, classification systems, category schemes, and/or assign persons, things, and/or groups of persons and/or things to at least one class.

Abstract: One of the key factors for the acceptance of expert systems in real-world domains is the ability to explain their reasoning (Buchanan & Shortliffe, 1984; Henrion & Druzdzel, 1990). This paper describes the basic properties that characterise explanation methods and reviews the methods developed to date for explanation in Bayesian networks.

Abstract: This paper presents an expert system that is able to classify different types of power system events to the underlying causes (i.e., events) and offer useful information in terms of power quality. The expert system uses the voltage waveforms and distinguishes the different types of voltage dips (fault-induced, transformer saturation, induction motor starting), as well as interruptions (nonfault, fault induced). A method for event-based classification is used, where a segmentation algorithm is first applied to divide waveforms into several possible events. The expert system is tested using real measurements and the results show that the system enables fast and accurate analysis of data from power quality monitors.

Abstract: A variety of forecast modeling techniques, from conventional techniques such as regression and time series analyses to relatively new artificial intelligence (AI) techniques such as expert systems and artificial neural networks (ANNs), were investigated for use in short-term water demand forecasting. Daily water demand, daily maximum air temperature, and daily total rainfall data from Lexington, Ky., for 1982-92 were used to develop and test several forecast models. The performance of each model was evaluated using two standard statistical parameters. On the basis of the measured statistical parameters, the Al models outperformed the conventional models. Both expert system and ANN technologies should be further explored by water utility engineers and managers because these techniques have the potential to enhance the operational performance of various water supply and delivery systems.

Abstract: A method and system for patient generation and evolution for a computer-based testing system and/or expert system. One or more belief networks, which describe parallel health state networks are accessed by a user or a computer. A knowledge base, at least in part, is scripted from the one or more belief networks by the computer. A model patient at least in part, is instantiated by the computer from the scripted knowledge base. Optionally, the model patient is evolved by the computer in accordance with the parallel health state networks and responsive to a received course of action.

Abstract: System and method for detecting, monitoring and evaluating hazardous situations in a structure includes the use of an expert system and, to the extent necessary, fuzzy logic in the generation of solution sets. Sensor units having two-way communication capability are strategically located in a structure or in a matrix of structures. These units are high-level multifunctional detectors, RF and other wireless or hardwired communication modules and signal generating systems that may communicate with a base station, with other modules and/or may have onboard logical solution generation capacity.

Abstract: Fish disease diagnosis is a complicated process and requires high level of expertise. Any attempt of developing a web-based system dealing with disease diagnosis has to overcome various difficulties. This paper describes a Chinese National Funded Research Project (863 project) aiming to develop a web-based intelligent diagnosis system for fish diseases. The paper explains the need for a web-based expert system, the fish diagnosis process and the difficulties involved in developing the system. The system structure and its components, such as database, knowledge base and image base and their functions are described. The system has over 300 rules and 400 images and graphics for different types of diseases and symptoms. It can diagnose 126 types of diseases amongst nine species of primary freshwater fishes. The system has been tested and is now in pilot use by fish farmers in the North China region. Some issues on developing web-based expert systems from the experience gained from the research are discussed.

Abstract: Intelligent solutions, based on expert systems, to solve problems in the field of production planning and scheduling are becoming more and more widespread nowadays. Especially the last decade has witnessed a growing number of manufacturing companies, including glass, oil, aerospace, computers, electronics, metal and chemical industries—to name just a few—interested in the applications of expert systems (ESs) in manufacturing. This paper is a state-of-the-art review of the use of ESs in the field of production planning and scheduling. The paper presents famous expert systems known in the literature and current applications, analyzes the relative benefits and concludes by sharing thoughts and estimations on ESs future prospects in this area.

Abstract: VOLUME 1 History and Applications Tools and Applications Development and Applications of Decision Trees Reasoning with Imperfect Information Experimental Design and Decision Support A Model-Based Expert System Based on a Domain Ontology Intelligent System Control: A Unified Approach and Applications Real Time Fault-Tolerant Control Systems VOLUME 2: Model of Reasoning with Conflicting Information Sources in Knowledge-Based Systems Process Planning in Design and Manufacturing Systems Intelligent Systems Techniques and Their Applications in Manufacturing Systems Architecture, Engineering and Construction (AEC) Design Neural Networks for Process Control Application to the Temperature Control of Batch Chemical Reactors Intelligent Tools and Their Applications in Geographic Information Systems Microprocessor Systems Scheduling Systems for Shipbuilding VOLUME 3 Genetic Image Interpretation Automated Visual Assembly Inspection Multiresolution Invariant Image Recognition Image Processing for Automatic Roads Determination Automated Visual Inspection Systems Visual Programming Technology in Expert Systems Development CAD-Based Vision Systems in Pattern Matching Process Cellular Automata Architectures for Pattern Recognition Machine Intelligent Systems Techniques for Automatic Harvest System Integrating Machine Learning with Knowledge Acquisition Modeling Human Reasoning Processes Under Uncertain Conditions VOLUME 4 Devising an Expert System for Pediatric Syndrome Diagnosis Knowledge Discovery in Larger Scale Knowledge Databases Efficient Data Utilization Investment Decision Making Intelligent Systems Control in Manufacturing Cells Knowledge-Based Approach for Automating Web Publishing from Databases Neural Networks for Economic Forecasting Problems Determination of Principal Components in Data Time Series Prediction VOLUME 5 Hybrid Expert Systems: An Approach to Combining Neural Computation and Rule-Based Reasoning POPFNNS: Fuzzy Neural Techniques for Rule-Based Identification in Expert Systems Preventive Quality Management Neuro-Fuzzy Systems Knowledge Representation by Means of Multi-Layer Perceptrons A Guide to Research in Assumption-Based Truth Maintenance Systems (ATMS) in Constraint Satisfaction A Method for Utilization of Previous Experience in Designing Expert Systems Model-Based Process Fault Diagnosis VOLUME 6 Automation of Concept Development A Methodology for Building Case-Based Reasoning Systems in Ill-Structured Optimization Domains The Trainor System: Applying QR Techniques to Intelligent Tutoring Systems Structuring Expert Control Using the Integrated Process Supervision Architecture TAP: An Inquiry Teaching Shell Using both Rule-Based and State-Space Approaches Self Teaching and Exploratory Task-Learning Methods in Unkown Environments and Applications in Robotic Skills

Abstract: This dissertation presents a case-based reasoning (CBR) approach to knowledge acquisition and knowledge-based inference for soil mapping under fuzzy logic. CBR is a technique in the artificial intelligence discipline. It uses the knowledge represented in specific cases to solve a new problem. The solution to the new problem is based on the similarities between the new problem and the available cases. With the CBR method, the soil scientist can express his or her knowledge by providing locations (cases) to indicate the association between a certain soil type and a specific landscape. To perform soil inference using these cases, the CBR inference engine first computes the similarity between the environmental configuration at a given location in the mapping area and the environmental configuration associated with each case, then use the similarity value to approximate the fuzzy membership value of the local soil at that location for the soil type represented by that case. A case study in the Pleasant Valley study area, southern Wisconsin, demonstrates the advantages of the inference results generated from this methodology over the published soil survey map produced from the conventional soil mapping process.

Abstract: Detection and isolation of failures in large and complex systems such as telecommunication networks are crucial and challenging tasks. The problem considered here is that of diagnosing the largest French packet switching network. The challenge is to be as efficient as the existing expert system while providing greater generality and flexibility with respect to technological and reconfiguration changes in the network. The network is made up of interconnected components each of which can send, receive and transmit messages via its ports. The problem we are faced with is to follow the evolution of the network on the basis of the stream of time-stamped alarms which arrive at the supervision center. We have decided to use model-based techniques which are recognized to be more adapted to evolutive systems than expertise-based approaches are. This paper starts with a description of how we model the global behavior of this discrete-event system by using communicating finite state machines. It goes on to explain how this model is used for analyzing the stream of alarms and diagnosing the network. Our work is based on the diagnoser approach proposed by Sampath et al. (1995). Starting from a model of the network adapted to simulate faults, this approach transforms it into a finite state automaton, called a diagnoser, in order to analyze the stream of alarms. The approach described in Sampath et al. (1995; 1996) proved to be grounded on certain basic hypotheses which were too restrictive for our application. This paper extends Sampath’s proposal to communicating finite state machines. The difficulties we had to cope with are outlined and the way we overcome them is presented. A major difficulty is the huge size of the global model of the system. To solve this problem we take advantage of the hierarchical structure of the network and rely on a generic model of the system for building a generic diagnoser.

Abstract: We identify and address a fundamental general problem which we regard as crucial for the widespread, effective use of decision support systems (DSS) in the future: how can we substantially improve the quality of interaction, and the degree of flexible engagement, between humans and computers? Rather than seeking an answer in additional technical functionality, we propose a new paradigm for computing that is human-centered and that adopts a novel, observation-oriented approach to data modelling We report a recent practical work (a timetabling instrument) showing an unusual degree of openness for interaction, and we give evidence that our approach can encompass conventional tools such as expert systems

Abstract: In the past, the design and engineering of process plants has been driven by factors related to economics followed by operability, reliability, maintainability, and safety with an emphasis on engineering budget and time. Decisions concerning safety during the evaluation phase focused on risk reduction instead of reducing or eliminating hazards. Increased public concern on safety issues and stringent environmental standards have led plant designers to consider inherently safer and environmentally friendlier processes. Opportunities for developing such processes are highest in the early stages of design. Constraints such as time and lack of inherent safety analysis tools have been cited as hurdles to the development and implementation of an inherently safer design. We address the need for inherent safety support tools in this two-part series by developing a systematic methodology for automating inherent safety analysis in the route selection and flowsheet development stages of process design. In the first part, we describe how the knowledge required for inherent safety analysis during route selection can be formalized in the form of expert rules. We also present a new inherent safety index for ranking of process routes and a graphical method for analyzing reaction networks. We illustrate the methodology by using it to compare three routes for phenol manufacture. In the second part, we describe the methodology for inherently safer flowsheet design and the implementation of an expert system that automates the methodology.

Abstract: Data Mining techniques have been applied in many application areas. A Data Mining project has been often described as a process of automatic discovery of new knowledge from large amounts of data. However the role of the domain knowledge in this process and the forms that this can take, is an issue that has been given little attention so far. Based on our experience with a large scale Data Mining industrial project we present in this paper an outline of the role of domain knowledge in the various phases of the process. This project has led to the development of a decision support expert system for a major Telecommunications Operator. The data mining process is described in the paper as a continuous interaction between explicit domain knowledge, and knowledge that is discovered through the use of data mining algorithms. The role of the domain experts and data mining experts in this process is discussed. Examples from our case study are also provided.

Abstract: System and method for detecting, monitoring and evaluating hazardous situations in a structure includes the use of an expert system and, to the extent necessary, fuzzy logic in the generation of solution sets. Sensor units having two-way communication capability are strategically located in a structure or in a matrix of structures. These units are high-level multifunctional detectors, RF and other wireless or hardwired communication modules and signal generating systems that may communicate with a base station, with other modules and/or may have onboard logical solution generation capacity.

Abstract: Measurement strategies for hazard control will have to be efficient and effective to protect a worker's health and well being. No measurement strategy for hazard control will ever be cost efficient in the short run when it is compared with the promises of tools such as the Control of Substances Hazardous to Health (COSHH) essentials (box 1): “a simple system of generic risk assessments which leads to the selection of an appropriate control approach”.1 Going straight to benchmark standards without the need of exposure measurements will certainly eliminate the cost of measurements. However, generic risk assessment tools like COSHH essentials and expert systems like the Estimation and Assessment of Substances Exposure (EASE)2 (box 2), as well as expert judgement by an occupational hygienist, are known to be inaccurate and they do not take into account the various components of variability in exposure levels (box 3). In fig 1, results of EASE estimates are compared with actual measured concentrations. From these pictures it can be seen that EASE estimates tend to be (1) higher than the measured concentrations, and (2) inaccurate especially at lower “true” concentrations (< 50 ppm and < 5 mg/m3). Nowadays, the latter exposures are being more relevant for workplaces of the developed world. ### Box 1 Control of Substances Hazardous to Health (COSHH) essentials: easy steps to control chemicals

Abstract: The computer-implemented international trade system is the business logic of an expert system which manages, guides and integrates the complete export/import trade process of single cross-border merchandise trade transactions on behalf of a buyer and seller, each accessing the system remotely using personal computers and web browsers. This system is designed to be accessed by users in an application service provider environment, and utilizes a combination of currently existing e-commerce technology, computer server technology, extensible markup language, encryption software, and database software, all integrated and controlled by a unique series of software applications created around the business logic. The object of the invention is to minimize time, costs, risks and required process knowledge, while maximizing the probability of a successful outcome, for cross-border merchandise trade transactions, especially for small to mid-sized firms worldwide.

Abstract: A method and apparatus that provides an expert system for determining respiratory phase during ventilatory support of a subject. Discrete phase states are partitioned and prior probability functions and observed probability functions for each state are defined. The probability functions are based upon relative duration of each state as well as the flow characteristics of each state. These functions are combined to determine phase probabilities for each state using Bayes' theorem. The calculated probabilities for the states may then be compared to determine which state the subject is experiencing. A ventilator may then conform respiratory support in accordance with the most probable phase. To provide a learning feature, the probability functions may be adjusted during use to provide a more subject specific response that accounts for changing respiratory characteristics.

Abstract: Information-theoretic modelling of manufacturing organizations and their supply chains has led to the development of measures of manufacturing complexity. The measures include assessment of the structural, dynamic and decision-making complexity associated with the processing and movement of material and information around a manufacturing system. A computer program has been written to calculate the decision-making complexity of a manufacturing system, under different system layouts and operating characteristics. In order to make the results of this program accessible to manufacturing organizations, an expert system has been developed to act as a mediator between the program and interested organizations. Given some simple quantitative data on manufacturing performance, the expert system can estimate the organization's complexity and suggest some recommendations to reduce it, based on the data provided by the organization. The expert system will be implemented on the web to enable on-line acquisition and searching of data on companies. The quid pro quo of the expert system is that anonymized data on the organizations will be retained so that complexity benchmarks may be established.

Abstract: Publisher Summary Over the last two decades, the knowledge engineer's toolbox has continued to develop and today, it constitutes a powerful set of tools for building expert systems to manage real-world problems across a wide range of application areas Each tool offers unique features that make it well suited for certain types of problems This chapter explores and reviews the most popular expert system development tools It provides the history of the development of each tool This is important because, it demonstrates how the artificial intelligence (AI) community responded to problems beyond the reach of existing methods This chapter also deals with the working of these tools to provide a general sense of its operation It considers the relevance of the tool by considering its strengths and weaknesses, and by looking at applications where it is typically employed It provides valuable references that allow further probing of the tool's theory and applications to design a successful expert system

Abstract: Preface. Artificial Intelligence. Fundamentals of Expert Systems. Problem Analysis. Knowledge Engineering. Probabilistic and Fuzzy Reasoning. Fuzzy Systems. Neural Networks. Neural-Fuzzy Networks. Evolutionary Computing. Intelligent Strategy Generation in Complex Manufacturing Environments. Products Demand Forecasting Using Genetic Programming. References. Index.

Abstract: In part 1 of this series, we proposed a systematic methodology for inherent safety analysis during the process route selection stage. In this part, we present the methodology for inherent safety analysis during the flowsheet development stage. An expert system, called iSafe, that implements the methodology has also been developed. One key benefit of automation is substantial reduction in the time and effort required to perform safety analysis. The architecture of iSafe is described and illustrated using an acrylic acid process case study.

Abstract: Contemporary microprocessors provide a rich set of integrated performance counters that allow application developers and system architects alike the opportunity to gather important information about workload behaviors. Current techniques for analyzing data produced from these counters use raw counts, ratios, and visualization techniques help users make decisions about their application performance. While these techniques are appropriate for analyzing data from one process, they do not scale easily to new levels demanded by contemporary computing systems. Very simply, this paper addresses these concerns by evaluating several multivariate statistical techniques on these datasets. We find that several techniques, such as statistical clustering, can automatically extract important features from the data. These derived results can, in turn, be fed directly back to an application developer, or used as input to a more comprehensive performance analysis environment, such as a visualization or an expert system.

Abstract: A hybrid approach for integrating group Delphi, fuzzy logic and expert systems for developing marketing strategies is proposed in this paper. Within this approach, the group Delphi method is employed to help groups of managers undertake SWOT analysis. Fuzzy logic is applied to fuzzify the results of SWOT analysis. Expert systems are utilised to formulate marketing strategies based upon the fuzzified strategic inputs. In addition, guidelines are also provided to help users link the hybrid approach with managerial judgement and intuition. The effectiveness of the hybrid approach has been validated with MBA and MA marketing students. It is concluded that the hybrid approach is more effective in terms of decision confidence, group consensus, helping to understand strategic factors, helping strategic thinking, and coupling analysis with judgement, etc.

Abstract: Multi-agent transportation simulations are rule-based The fact that such simulations do not vectorize means that the recent move to distributed computing architectures results in an explosion of computing capabilities of multiagent simulations This paper describes the general modules which are necessary for transportation planning simulations, reports the status of an implementation of such a simulation for all of Switzerland, and gives computational performance numbers

Abstract: This paper is focused on the development of a fuzzy expert system capable to diagnose the state of a pilot-scale wastewater treatment plant, its trend and also to be able to decide the best commands to be sent to the final control elements to recover the stable operation in case of disturbances. The development of the fuzzy expert system was carried out by selecting the on-line variables to be used, building the fuzzy membership functions for each input and output variable and developing a knowledge based rules structure. Finally, the fuzzy expert system was carefully tested and adjusted by performing some experiments.

Abstract: A computerized system to schedule high-rise building construction has been developed using line-of-balance technology assisted by an expert system. A review of the recent literature on the techniques available for scheduling and controlling construction projects of a repetitive nature shows that Gantt charts are inadequate, and that there are serious problems with using network methods in such circumstances. There is evidence that the construction of high rise buildings has a decidedly repetitive nature but differs in some respects from other repetitive projects such as pipelines or pavement construction. Two new concepts have been introduced into line-of-balance methodology to accommodate the special conditions encountered in high rise building construction. These two concepts, namely ‘flexible’ unit networks and ‘multi-level’ LOB diagrams have been coded into a scheduling module (‘Lobplans’). A series of databases have been compiled regarding the productivity of resources. An expert system module (Lobex...