Abstract: This paper presents a new solution to the expert system for reliable heartbeat recognition. The recognition system uses the support vector machine (SVM) working in the classification mode. Two different preprocessing methods for generation of features are applied. One method involves the higher order statistics (HOS) while the second the Hermite characterization of QRS complex of the registered electrocardiogram (ECG) waveform. Combining the SVM network with these preprocessing methods yields two neural classifiers, which have been combined into one final expert system. The combination of classifiers utilizes the least mean square method to optimize the weights of the weighted voting integrating scheme. The results of the performed numerical experiments for the recognition of 13 heart rhythm types on the basis of ECG waveforms confirmed the reliability and advantage of the proposed approach.

Abstract: 1. Introduction.- 2. Models in the diagnostics of processes.- 3. Process diagnostics methodology.- 4. Methods of signal analysis.- 5. Control theory methods in designing diagnostic systems.- 6. Optimal detection observers based on eigenstructure assignment.- 7. Robust H?-optimal synthesis of FDI systems.- 8. Evolutionary methods in designing diagnostic systems.- 9. Artificial neural networks in fault diagnosis.- 10. Parametric and neural network Wiener and Hammerstein models in fault detection and isolation.- 11. Application of fuzzy logic to diagnostics.- 12. Observers and genetic programming in the identification and fault diagnosis of non-linear dynamic systems.- 13. Genetic algorithms in the multi-objective optimisation of fault detection observers.- 14. Pattern recognition approach to fault diagnostics.- 15. Expert systems in technical diagnostics.- 16. Selected methods of knowledge engineering in systems diagnosis.- 17. Methods of acqusition of diagnostic knowledge.- 18. State monitoring algorithms for complex dynamic systems.- 19. Diagnostics of industrial processes in decentralised structures.- 20. Detection and isolation of manoeuvres in adaptive tracking filtering based on multiple model switching.- 21. Detecting and locating leaks in transmission pipelines.- 22. Models in the diagnostics of processes.- 23. Diagnostic systems.

Abstract: As the artificial intelligence (AI) systems in military simulations and computer games become more complex, their actions become increasingly difficult for users to understand. Expert systems for medical diagnosis have addressed this challenge though the addition of explanation generation systems that explain a system's internal processes. This paper describes the AI architecture and associated explanation capability used by Full Spectrum Command, a training system developed for the U.S. Army by commercial game developers and academic researchers.

Abstract: In many applications, black-box prediction is not satisfactory, and understanding the data is of critical importance. Typically, approaches useful for understanding of data involve logical rules, evaluate similarity to prototypes, or are based on visualization or graphical methods. This paper is focused on the extraction and use of logical rules for data understanding. All aspects of rule generation, optimization, and application are described, including the problem of finding good symbolic descriptors for continuous data, tradeoffs between accuracy and simplicity at the rule-extraction stage, and tradeoffs between rejection and error level at the rule optimization stage. Stability of rule-based description, calculation of probabilities from rules, and other related issues are also discussed. Major approaches to extraction of logical rules based on neural networks, decision trees, machine learning, and statistical methods are introduced. Optimization and application issues for sets of logical rules are described. Applications of such methods to benchmark and real-life problems are reported and illustrated with simple logical rules for many datasets. Challenges and new directions for research are outlined.

Abstract: A plurality of chronic sensors are used to facilitate diagnosis and medical decision making for an individual patient An expert system evaluates the sensor data, combines the sensor data with stored probability data and provides an output signal for notification or medical intervention

Abstract: In recognizing knowledge as a new resource in gaining organizational competitiveness, knowledge management suggests a method in managing and applying knowledge for improving organizational performance. Much knowledge management research has focused on identifying, storing, and disseminating process related knowledge in an organized manner. Applying knowledge to decision making has a significant impact on organizational performance than solely processing transactions for knowledge management. In this research, we suggest a method of knowledge-based decision-making using system dynamics, with an emphasis to strategic concerns. The proposed method transforms individual mental models into explicit knowledge by translating partial and implicit knowledge into an integrated knowledge model. The scenario-based test of the organized knowledge model enables decision-makers to understand the structure of the target problem and identify its basic cause, which facilitates effective decision-making. This method facilitates the linkage between knowledge management initiatives and achieving strategic goals and objectives of an organization.

Abstract: The need for economic, reliable, and effective delivery of electric power has lead to the search for fast, efficient, and effective methods for diagnosing the insulation of high-voltage (HV) equipment in the power industries. The recent dielectric techniques that have been carefully considered by major industries for transformer insulation condition assessment are the recovery voltage method (RVM) and the polarization and depolarization current (PDC) measurement. However, due to the complexity of the transformer insulation structure and various degradation mechanisms under multiple stresses, insulation condition assessment is usually performed by experts with special knowledge and experience. In this paper, an expert system (ES) is developed, which imitates the performance of a human expert, to make the complicated insulation condition assessment procedure accessible to plant maintenance engineers. The structure of the ES is described in detail including knowledge base, inference engine, and human-computer interface. Examples of the application of the ES are also presented to confirm that the system can provide accurate insulation diagnosis.

Abstract: A method and computer program for interfacing an expert system to a clinical information system. Embodiments of the invention provide tight integration of the systems permitting a clinician to use the functionality provided by the expert system without specifically maintaining separate patient data. They provide a method for communication between the expert system and one or more clinical information systems. This communication permits flow of information and actions between the expert system and the clinical systems and allows maintenance of audit logs in both systems.

Abstract: First-person shooter robot controllers (bots) are generally rule-based expert systems written in C/C++. As such, many of the rules are parameterized with values, which are set by the software designer and finalized at compile time. The effectiveness of parameter values is dependent on the knowledge the programmer has about the game. Furthermore, parameters are non-linearly dependent on each other. This paper presents an efficient method for using a genetic algorithm to evolve sets of parameters for bots which lead to their playing as well as bots whose parameters have been tuned by a human with expert knowledge about the game's strategy. This indicates genetic algorithms as being a potentially useful method for tuning bots.

Abstract: On-line fault detection and isolation techniques have been developed for automated processes during the last few years. These methods include numerical methods, artificial intelligence methods or combinations of the two methodologies. This paper includes a reference to recent research work on numerical methods, an extensive presentation of artificial intelligence methods used for the fault detection process in technical systems and relevant survey material. Special reference is made to the on-line expert systems development where specific resent research work is illustrated.

Abstract: An expert system of crude oil distillation unit (CDU) was developed to carry out the process optimization on maximizing oil production rate under the required oil product qualities. The expert system was established using the expertise of a practical CDU operating system provided by a group of experienced engineers. The input operating variables of the CDU system were properties of crude oil and manipulated variables; while the system output variables were defined as oil product qualities. The knowledge database of the CDU operating model can be built using the input–output data with an approach of artificial neural networks (ANN). The built ANN model can be applied on predicting the oil product qualities with respect to the system input variables. In addition, a design of experiment was implemented to analyze the effect of the system input variables on the oil product qualities. Optimal operating conditions were then found using the knowledge database with an optimization method according to a defined objective function. The built expert system can provide on-line optimal operating information of the CDU process to the operators corresponding to the change of crude oil properties.

Abstract: Transformation-based verification has been proposed to synergistically leverage various transformations to successively simplify and decompose large problems to ones which may be formally discharged. While powerful, such systems require a fair amount of user sophistication and experimentation to yield greatest benefits – every verification problem is different, hence the most efficient transformation flow differs widely from problem to problem. Finding an efficient proof strategy not only enables exponential reductions in computational resources, it often makes the difference between obtaining a conclusive result or not. In this paper, we propose the use of an expert system to automate this proof strategy development process. We discuss the types of rules used by the expert system, and the type of feedback necessary between the algorithms and expert system, all oriented towards yielding a conclusive result with minimal resources. Experimental results are provided to demonstrate that such a system is able to automatically discover efficient proof strategies, even on large and complex problems with more than 100,000 state elements in their respective cones of influence. These results also demonstrate numerous types of algorithmic synergies that are critical to the automation of such complex proofs.

Abstract: In this paper, we present the architecture and describe the functionality of an Intelligent Tutoring System (ITS), which uses an expert system to make decisions during the teaching process. The expert system uses neurules for knowledge representation of the pedagogical knowledge. Neurules are a type of hybrid rules integrating symbolic rules with neurocomputing. The expert system consists of three components: the user modelling unit, the pedagogical unit and the inference system. The pedagogical knowledge is distributed in a number of neurule bases within the user modelling and the pedagogical unit. Another important component of the ITS, for both its development and maintenance, is its knowledge management unit, which provides knowledge acquisition and knowledge update capabilities to the system, that is, offers expert knowledge authoring capabilities to the system.

Abstract: Conveyor equipment selection is a complex, and sometimes, tedious task since there are literally hundreds of equipment types and manufacturers to choose from The expert system approach to conveyor selection provides advantages of unbiased decision making, greater availability, faster response, and reduced cost as compared to human experts This paper discusses the development of a prototype expert system for industrial conveyor selection The system, which was developed on Level V Object, provides the user with a list of conveyor solutions for their material handling needs along with a list of suppliers for the suggested conveyor devices Conveyor types are selected on the basis of a suitability score, which is a measure of the fulfillment of the material handling requirements by the characteristics of the conveyor The computation of the score is performed through the Weighted Evaluation Method, and the Expected Value Criterion for decision making under risk The prototype system was successfully validated through two industrial case studies

Abstract: Automatic inspection of welded gas pipelines is desirable because human inspectors are not always consistent evaluators. In addition, automatic inspection decreases the cost of inspection process and improves the inspection quality. In this paper, an expert vision system for automatic inspection of gas pipeline welding defects from radiographic films is presented. The proposed system has been established in the Metrology lab, Mansoura University, Faculty of Engineering. The software, named AutoWDI, is fully written in lab using Microsoft Visual C++ and is ready to run on any Windows environment. The proposed vision system is used to capture images for the radiographic films then applies various image processing and computer vision algorithms to recognize the defects and to make acceptance decisions according to international standards. The expert system is based on a knowledge base, which was gathered from specialists, textbooks and international standards. The proposed system is capable of identifying and testing the main types of welding defects (11 defects) in gas pipelines welded by shielded metal arc welding.

Abstract: Explanation of reasoning is one of the most important abilities an expert system should provide in order to be widely accepted. In fact, since MYCIN, many expert systems have tried to include some explanation capability. This paper reviews the methods developed to date for explanation in heuristic expert systems.

Abstract: 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, 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: An interactive voice and data response system then directs input to a voice, text, and web-capable software-based router, which is able to intelligently respond to the input by drawing on a combination of human agents, advanced speech recognition and expert systems, connected to the router via a TCP/IP network. The digitized input is broken down into components so that the customer interaction is managed as a series of small tasks rather than one ongoing conversation. The router manages the interactions and keeps pace with a real-time conversation. The system utilizes both speech recognition and human intelligence for purposes of interpreting customer utterance or customer text. The system may use more than one human agent, or both human agents and speech recognition software, to interpret simultaneously the same component for error-checking and interpretation accuracy.

Abstract: In this paper, we present an approach that integrates symbolic rules, neural networks and cases. To achieve it, we integrate a kind of hybrid rules, called neurules, with cases. Neurules integrate symbolic rules with the Adaline neural unit. In the integration, neurules are used to index cases representing their exceptions. In this way, the accuracy of the neurules is improved. On the other hand, due to neurule-based efficient inference mechanism, conclusions can be reached more efficiently. In addition, neurule-based inferences can be performed even if some of the inputs are unknown, in contrast to symbolic rule-based inferences. Furthermore, an existing symbolic rule-base with indexed exception cases can be converted into a neurule-base with corresponding indexed exception cases. Finally, empirical data can be used as a knowledge source, which facilitates knowledge acquisition. We also present a new high-level categorization of the approaches integrating rule-based and case-based reasoning.

Abstract: Air pollution from power plants is responsible for some of the most pressing environmental problems today Much research has been done on pollution control for power plants Contemporary approaches to pollution control often take advantage of computer technology, but research on use of expert systems for power plant management is scarce In this study an expert system was developed to assist power plant decision makers in selecting an economical and efficient pollution control system that meets new stringent emission standards The study will also provide the key design parameters for such a system A fuzzy relation model and a Gaussian dispersion model were integrated into this expert system Using the fuzzy relation model, the system can quickly select feasible control methods according to the desired removal efficiency The system will then identify the most cost effective control strategy according to economic considerations provided by users To assess and ensure effectiveness of the selected method, ambient air quality is simulated using the Gaussian dispersion model and compared with required standards The developed system was applied to a case study The results generated show that the system is able to consider the trade-offs between environmental requirement and economic objective, decrease the possibility of pollutant risk, and help the power plant reduce environmental-related capital and operation costs

Abstract: The complexity of current organization systems, and the increase in importance of the realization of internal controls in firms, make it necessary to construct models that automate and facilitate the work of auditors. An intelligent system has been developed to automate the internal control process. This system is composed of two case-based reasoning systems. The objective of the system is to facilitate the process of internal auditing in small and medium firms from the textile sector. The system, analyses the data that characterises each one of the activities carried out by the firm, then determines the state of each activity, calculates the associated risk, detects the erroneous processes, and generates recommendations to improve these processes. As such, the system is a useful tool for the internal auditor in order to make decisions based on the risk generated. Each one of the case-based reasoning systems that integrates the system uses a different problem solving method in each of the steps of the reasoning cycle: fuzzy clustering during the retrieval phase, a radial basis function network and a multi-criterion discreet method during the reuse phase and a rule based system for recommendation generation. The system has been proven successfully in several small and medium companies in the textile sector, located in the northwest of Spain. The accuracy of the technologies employed in the system has been demonstrated by the results obtained over the last two years.

Abstract: This paper presents a data-mining-based production control approach for the testing and rework cell in a dynamic computer-integrated manufacturing system. The proposed competitive decision selector (CDS) observes the status of the system and jobs at every decision point, and makes its decision on job preemption and dispatching rules in real time. The CDS equipped with two algorithms combines two different knowledge sources, the long-run performance and the short-term performance of each rule on the various status of the system. The short-term performance information is mined by a data-mining approach from large-scale training data generated by simulation with data partition. A decision tree-based module generates classification rules on each partitioned data that are suitable for interpretation and verification by users and stores the rules in the CDS knowledge bases. Experimental results show that the CDS dynamic control is better than other common control rules with respect to the number of tardy jobs.

Abstract: Recent advances in computer internetworking and continued increases in Internet usage have been accompanied by a continued increase in the incidence of computer related crime At the same time, the number of sources of potential evidence in any particular computer forensic investigation has grown considerably, as evidence of the occurrence of relevant events can potentially be drawn not only from multiple computers, networks, and electronic systems but also from disparate personal, organizational, and governmental contexts Potentially, this leads to significant improvements in forensic outcomes but is accompanied by an increase in both the complexity and scale of event information In order for forensic investigators to effectively investigate this mass of data, semantically strong representational models and automated methods of correlating such event data is becoming a necessity The contribution of the work described in this paper is the automated detection of a computer forensic scenario, based upon facts automatically derived from digital event logs We present an expert systems based approach that has the ability to manage the scalability and semantic issues arising in such inter-domain forensics, using an extensible, semantic domain model specified using the Web Ontology Language (OWL) We have developed a prototype system, Forensics of Rich Events (FORE), which supports investigation of heterogeneous event data using a novel form of manipulation of hypothetical knowledge, while supporting the application of standard rule and signature based event correlation techniques We demonstrate proof of concept of our approach by applying the prototype we have developed to a test case scenario that demonstrates the flexibility of the approach in a single domain context

Abstract: An expert system for more efficiently and accurately deducing document structure from document formatting, the expert system including a conversion engine for converting an unstructured file to a structured file, and a verification engine, responsive to the output of the conversion engine, for generating and displaying a representation of the structured file annotated with a visual depictions of the classified components thereof so that the annotations can be modified and/or classifications can be added and/or classifications can be suggested, and/or rules for classification can be suggested and the structured file reprocessed by the conversion engine.

Abstract: This paper presents the development of artificial neural network models for predicting client satisfaction levels arising from the performance of contractors, based on data from a UK-wide questionnaire survey of clients. Important independent variables identified by the models indicate that long-term relationships may encourage higher satisfaction levels. Moreover, the performance of contractors was found to only partly contribute to determining levels of client satisfaction. Attributes of the assessor (i.e. client) were also found to be of importance, confirming that subjectivity is to some extent prevalent in performance assessment. The models demonstrate accurate and consistent predictive performance for "unseen" independent data. It is recommended that the models be used as a platform to develop an expert system aimed at advising project coalition (PC) participants on how to improve performance and enhance satisfaction levels. The use of this tool will ultimately help to create a performance-enhancing environment, leading to harmonious working relationships between PC participants.