Abstract: Lhasa Limited is a not-for-profit organization that exists to promote the sharing of data and knowledge in chemistry and the life sciences. It has developed the software tools Derek for Windows, Meteor, and Vitic to facilitate such sharing. Derek for Windows and Meteor are knowledge-based expert systems that predict the toxicity and metabolism of a chemical, respectively. Vitic is a chemically intelligent toxicity database. An overview of each software system is provided along with examples of the sharing of data and knowledge in the context of their development. These examples include illustrations of (1) the use of data entry and editing tools for the sharing of data and knowledge within organizations; (2) the use of proprietary data to develop nonconfidential knowledge that can be shared between organizations; (3) the use of shared expert knowledge to refine predictions; (4) the sharing of proprietary data between organizations through the formation of data-sharing groups; and (5) the use of pr...

Abstract: Knowledge acquisition has been a critical bottleneck in building knowledge-based systems. In past decades, several methods and systems have been proposed to cope with this problem. Most of these methods and systems were proposed to deal with the acquisition of domain knowledge from single expert. However, as multiple experts may have different experiences and knowledge on the same application domain, it is necessary to elicit and integrate knowledge from multiple experts in building an effective expert system. Moreover, the recent literature has depicted that ''time'' is an important parameter that might significantly affect the accuracy of inference results of an expert system; therefore, while discussing the elicitation of domain expertise from multiple experts, it becomes an challenging and important issue to take the ''time'' factor into consideration. To cope with these problems, in this study, we propose a Delphi-based approach to eliciting knowledge from multiple experts. An application on the diagnosis of Severe Acute Respiratory Syndrome has depicted the superiority of the novel approach.

Abstract: The present invention relates generally to a building automation system, and, more particularly, to an Internet-centric, open, extensible software and hardware framework supporting all aspects of control and monitoring of a smart building ecosphere. The present invention further relates to an “intelligent,” real-time control system capable of both autonomous process control and interaction with system users and system administrators, which is configured to accommodate functional extensions and a broad array of sensors and control devices. The system allows individuals to communicate, monitor and adjust their personal environmental preferences (temperature, light, humidity, white noise, etc.) much like they would in an automobile, via the Internet. The system is equipped with an occupancy sensor that recognizes the presence and identity of the individual. A built-in expert system can make decisions based on data from multiple sources so that the system can alter its activity to conserve energy while maintaining users' comfort.

Abstract: Artificial Intelligence: Structures and Strategies for Complex Problem Solving by George F. Luger 6th edition, Addison Wesley, 2008 The book serves as a good introductory textbook for artificial intelligence, particularly for undergraduate level. It covers major AI topics and makes good connection between different areas of artificial intelligence. Along with each technique and algorithm introduced in the book, is a discussion of its complexity and application domain. There is an attached website to the book that provides auxiliary materials for some chapters, sample problems with solutions, and ideas for student projects. Besides Prolog and Lisp, java and C++ are also used to implement many of the algorithms in the book. The book is organized in five parts. The first part (chapter 1) gives an overview of AI, its history and its various application areas. The second part (chapters 2–6) concerns with knowledge representation and search algorithms. Chapter 2 introduces predicate calculus as a mathematical tool for representing AI problems. The state space search as well as un-informed and heuristic search methods is introduced in chapters 3 and 4. Chapter 5 discusses the issue of uncertainty in problem solving and covers the foundation of stochastic methodology and its application. In chapter 6 the implementation of search algorithms is shown in production system and blackboard architectures. Part 3 (chapters 7–9) discusses knowledge representation and different methods of problem solving, including strong, weak and distributed problem solving. Chapter 7 begins with reviewing the history of evolution of AI representation schemes, including semantic networks, frames, scripts and conceptual graphs. This chapter ends with a brief introduction of Agent problem solving. Chapter 8 presents the production model and rule-based expert systems as well as case-based and model-based reasoning. The methods of dealing with various aspects of uncertainty are discussed in chapter 9. These methods include Dempster-Shafer theory of evidence, Bayesian and Belief networks, fuzzy logics and Markov models. Part 4 is devoted to machine learning. Chapter 10 describes algorithms for symbol-based learning, including induction, concept learning, vision-space search and ID3. The neural network methods for learning, such as back propagation, competitive, Associative memories and Hebbian Coincidence learning were presented in chapter 11. Genetic algorithms and evolutionary learning approaches are introduced in chapter 12. Chapter 13 introduces stochastic and dynamic models of learning along with Hidden Markov Models, Dynamic Baysian networks and Markov Decision Processes. Part 5 (chapters 14 and 15) examines two main application of AI: automated reasoning and natural language understanding. Chapter 14 begins with an introduction to weak methods in problem solving and continues with presenting resolution theorem proving. Chapter 15 deals with the complex issue of natural language understanding by discussing main methods of syntax and semantic analysis of natural language corpus. The chapter ends with examples of natural language application in Database query generation, text summarization and question answering systems. Finally, chapter 16 is a summary of the materials covered in the book as well current AI's limitations and future directions. One criticism about the book would be that the materials are not covered in enough depth. Because of the space limitation, many important AI algorithms and techniques are discussed briefly without providing enough details. As a result, some chapters (e.g., 8, 9, 11, and 13) of the book should be supported by complementary materials to make it understandable for undergraduate students and motivating for graduate students. Another issue is with the structure of the book. The order of presenting chapters introduces sequentially different challenges and techniques in problem solving. Consequently, some topics such as uncertainty and logic are not introduced separately and are distributed in different chapters of the book related to different parts. Although interesting, this makes the book hard to follow. In summary, the book gives a great insight to the readers that want to familiar themselves with artificial intelligence. It covers a broad range of topics in AI problem solving and its practical application and is a good reference for an undergraduate level introductory AI class. Elham S. Khorasani, Department of Computer Science Southern Illinois University Carbondale, IL 62901, USA

Abstract: Elevator group supervisory control systems (EGSCSs) are designed so that the movement of several elevators in a building is controlled efficiently. The efficient control of EGSCSs using conventional control methods is very difficult due to its complexity, so it is becoming popular to introduce artificial intelligence (AI) technologies into EGSCSs in recent years. As a new approach, a graph-based evolutionary method named genetic network programming (GNP) has been applied to the EGSCSs, and its effectiveness is clarified. The GNP can introduce various a priori knowledge of the EGSCSs in its node functions easily, and can execute an efficient rule-based group supervisory control that is optimized in an evolutionary way. Meanwhile, double-deck elevator systems (DDESs) where two cages are connected in a shaft have been developed for the rising demand of more efficient transport of passengers in high-rise buildings. The DDESs have specific features due to the connection of cages and the need for comfortable riding; so its group supervisory control becomes more complex and requires more efficient group control systems than the conventional single-deck elevator systems (SDESs). In this paper, a new group supervisory control system for DDESs using GNP is proposed, and its optimization and performance evaluation are done through simulations. First, optimization of the GNP for DDSEs is executed. Second, the performance of the proposed method is evaluated by comparison with conventional methods, and the obtained control rules in GNP are studied. Finally, the reduction of space requirements compared with SDESs is confirmed.

Abstract: An MCDM-based expert system was developed to tackle the interrelationships between the climate change and the adaptation policies in terms of water resources management in the Georgia Basin, Canada User interfaces of the developed expert system, named MAEAC (MCDM-based expert system for adaptation analysis under changing climate), was developed based on system configuration, knowledge acquisition, survey analysis, and MCDM-based policy analysis A number of processes that were vulnerable to climate change were examined and pre-screened through extensive literature review, expert consultation and statistical analysis Adaptation policies to impacts of temperature increase, precipitation-pattern variation and sea-level rise were comprehensively explicated and incorporated within the developed system The MAEAC could be used for both acquiring knowledge of climate-change impacts on water resources in the Georgia Basin and supporting formulation of the relevant adaptation policies It can also be applied to other watersheds to facilitate assessment of climate-change impacts on socio-economic and environmental sectors, as well as formulation of relevant adaptation policies

Abstract: Creating good adaptation policies is critical to building complex autonomic systems since it is such policies that define the system configuration used in any given situation. While online approaches based on control theory and rule- based expert systems are possible solutions, each has its disadvantages. Here, a hybrid approach is described that uses modeling and optimization offline to generate suitable configurations, which are then encoded as policies that are used at runtime. The approach is demonstrated on the problem of providing dynamic management in virtualized consolidated server environments that host multiple multi-tier applications. Contributions include layered queuing models for Xen-based virtual machine environments, a novel optimization technique that uses a combination of bin packing and gradient search, and experimental results that show that automatic offline policy generation is viable and can be accurate even with modest computational effort.

Abstract: Is artificial intelligence headed for another AI Winter? Not if we take action now.

Abstract: In this paper, a subspace-based multimedia data mining framework is proposed for video semantic analysis, specifically video event/concept detection, by addressing two basic issues, i.e., semantic gap and rare event/concept detection. The proposed framework achieves full automation via multimodal content analysis and intelligent integration of distance-based and rule-based data mining techniques. The content analysis process facilitates the comprehensive video analysis by extracting low-level and middle-level features from audio/visual channels. The integrated data mining techniques effectively address these two basic issues by alleviating the class imbalance issue along the process and by reconstructing and refining the feature dimension automatically. The promising experimental performance on goal/corner event detection and sports/commercials/building concepts extraction from soccer videos and TRECVID news collections demonstrates the effectiveness of the proposed framework. Furthermore, its unique domain-free characteristic indicates the great potential of extending the proposed multimedia data mining framework to a wide range of different application domains.

Abstract: Expert systems can be adapted for machine condition monitoring data interpretation due to the ability to identify systematic reasoning processes As vibration analysis in condition monitoring is still generally performed by highly trained professionals, the use of expert systems would allow a greater analysis throughput as well as enabling technicians to perform routine analysis The development of an expert system for vibration analysis of fixed plant is discussed, as well as laboratory and industry testing Unique to existing developments, the expert system incorporates triaxial and demodulated frequency and time domain vibration data analysis algorithms for high accuracy fault detection The tests confirm the potential value of the expert system for both laboratory and on-site maintenance departments of large manufacturing and mineral processing plants

Abstract: This work presents the design of an expert system that aims to provide the patient with background for suitable diagnosis of some of the eye diseases. The eye has always been viewed as a tunnel to the inner workings of the body. There are many disease states that may produce symptoms from the eye. CLIPS language is used as a tool for designing our expert system. An initial evaluation of the expert system was carried out and a positive feedback was received from the users.

Abstract: There are many skin diseases having similar symptoms, therefore, the most important objective - in order to prescribe the appropriate treatment - is the right diagnosis of the disease. In this paper the design of t he proposed Expert S ystem which was p roduced to help dermatologists in d iagnosing some of t he skin diseases (Psoriasis, Eczema, Ichthyosis, Acne, Meningitis, Measles, Scarlet Fever, Warts, Insect Bites and Stings) are presented, an overview about the skin diseases are displayed, the cause of diseases are outlined and the treatment of disease wh enever is possible i s given. CLIPS l anguage f or des igning the proposed expert system is used.

Abstract: In this study an expert system of a crude oil distillation column is designed to predict the unknown values of required product flow and temperature in required input feed characteristics. The system is also capable to optimize the distillation process with minimizing the model output error and maximizing the required oil production rate with respect to control parameter values. The designed expert system uses the practical data of an operating refinery located in Abadan. The input operating variables of the column were operational parameters of crude oil such as flow and temperature, while the system output variables were defined as oil product qualities. We can make the knowledge database of these input-output values of plant with the aid of a neural networks model (NNM) to organize and collect all data related to this process and also to predict the unknown output values of required inputs. In addition we have made the ability of system's optimization with the use of genetic algorithm (GA) with the aim of error minimizing of expert system's output and also maximizing the required product rate with respect to its industrial importance. The built expert system can be used by operators and engineers to calculate and get some unknown data for operational values of this distillation column.

Abstract: In this paper we will present the design and development of a proposed expert system that aims to improve the method of selecting the best suitable faculty/major for student planning to be enrolled in Al-Azhar University. The basic idea of our approach is designing a model for testing and measuring the student capabilities like intelligence, understanding, comprehension, mathematical concepts and others, and applying the module results to a rule-based expert system to determine the compatibility of those capabilities with the available faculties/majors in Al-Azhar University. The result is shown as a list of suggested faculties/majors that are most suitable with the student capabilities and abilities.

Abstract: Defence R&D Canada is developing a Collaborative Knowledge Exploitation Framework (CKEF) to support the analysts in efficiently managing and exploiting relevant knowledge assets to achieve maritime domain awareness in joint operations centres of the Canadian Forces. While developing the CKEF, anomaly detection has been clearly recognized as an important aspect requiring R&D. An activity has thus been undertaken to implement, within the CKEF, a proof-of-concept prototype of a rule-based expert system to support the analysts regarding this aspect. This expert system has to perform automated reasoning and output recommendations (or alerts) about maritime anomalies, thereby supporting the identification of vessels of interest and threat analysis. The system must contribute to a lower false alarm rate and a better probability of detection in drawing operator's attention to vessels worthy of their attention. It must provide explanations as to why the vessels may be of interest, with links to resources that help the operators dig deeper. Mechanisms are necessary for the analysts to fine tune the system, and for the knowledge engineer to maintain the knowledge base as the expertise of the operators evolves. This paper portrays the anomaly detection prototype, and describes the knowledge acquisition and elicitation session conducted to capture the know-how of the experts, the formal knowledge representation enablers and the ontology required for aspects of the maritime domain that are relevant to anomaly detection, vessels of interest, and threat analysis, the prototype high-level design and implementation on the service-oriented architecture of the CKEF, and other findings and results of this ongoing activity.

Abstract: Providing context-aware Web services is an adaptive process of delivering contextually matched Web services to meet service requesters' needs We define the term ''context'' from two perspectives: one from service requesters; and the other from Web services From the former perspective, context is defined as the surrounding environment affecting requesters' services discovery and access, such as requesters' preferences, locations, activities, and accessible network and devices From the latter perspective, context is defined as the surrounding environment affecting Web services delivery and execution, such as networks and protocols for service binding, devices and platforms for service execution, and so on This paper presents a Java Expert System Shell (JESS)-enabled context elicitation system featuring an ontology-based context model that formally describes and acquires contextual information pertaining to service requesters and Web services Based on the context elicitation system, we present a context-aware services-oriented architecture for providing context-aware Web service request, publication, and discovery Implementation details of the context elicitation system and the evaluation results of context-aware service provision are also reported

Abstract: The paper introduces modeling of human experience by linguistic if-then rules in terms of fuzzy logic controllers for tuning RF/Microwave filters. The approach could be used for any circuit or system tuning problem that involves human expert information provided that the expert information could be described in terms of linguistic if-then rules. The tuning approach is both theoretically and practically illustrated in this paper. The tuning is done in two stages both taking advantage of fuzzy controllers. The first stage uses the phase response of the filter, while the second stage uses the magnitude response of the filter for adjustment of the tuning screws. A fully automated experimental setup is implemented by high resolution motors with flexible leads to make the tuning possible. 3-pole and 7-pole Chebyshev waveguide filters are used to demonstrate the concept. The measured results prove the validity of the method.

Abstract: An expert system for fault diagnosis in internal combustion engines using adaptive order tracking technique and artificial neural networks is presented in this paper. The proposed system can be divided into two parts. In the first stage, the engine sound emission signals are recorded and treated as the tracking of frequency-varying bandpass signals. Ordered amplitudes can be calculated with a high-resolution adaptive filter algorithm. The vital features of signals with various fault conditions are obtained and displayed clearly by order figures. Then the sound energy diagram is utilized to normalize the features and reduce computation quantity. In the second stage, the artificial neural network is used to train the signal features and engine fault conditions. In order to verify the effect of the proposed probability neural network (PNN) in fault diagnosis, two conventional neural networks that included the back-propagation (BP) network and radial-basic function (RBF) network are compared with the proposed PNN network. The experimental results indicated that the proposed PNN network achieved the best performance in the present fault diagnosis system.

Abstract: Product development is an important but also dynamic, lengthy and risky phase in the life of a new product. The optimisation of the product development phase through extensive knowledge of the involved procedures is believed to reduce the risks and improve the final product quality. Artificial intelligence and expert systems have been used successfully in optimising the development phase of some new products as it will be demonstrated by the first sections of this publication. This paper presents the first module of an expert system, a neural network architecture that could predict the reliability performance of a vehicle at later stages of its life by using only information from a first inspection after the vehicle's prototype production. The paper demonstrates how a tool like neural networks can be designed and optimised for use in reliability performance predictions. Also, this paper presents an optimisation methodology that enabled the neural network to deal with the limited amount of available training data, common during new product development, and to finally achieve acceptable prediction performance with small error. A case example is presented to demonstrate the methodology.

Abstract: A computer implemented method, data processing system, and computer program product for monitoring system events and providing real-time response to security threats. System data is collected by monitors in the computing system. The expert system of the present invention compares the data against information in a knowledge base to identify a security threat to a system resource in a form of a system event and an action for mitigating effects of the system event. A determination is made as to whether a threat risk value of the system event is greater than an action risk value of the action for mitigating the system event. If the threat risk value is greater, a determination is made as to whether a trust value set by a user is greater than the action risk value. If the trust value is greater, the expert system executes the action against the security threat.

Abstract: Alert correlation is a very useful mechanism to reduce the high volume of reported alerts and to detect complex and coordinated attacks. Existing approaches either require a large amount of expert knowledge or use simple similarity measures that prevent detecting complex attacks. They also suffer from high computational issues due, for instance, to a high number of possible scenarios. In this paper, we propose a Naive Bayes approach to alert correlation. Our modeling only needs a small part of expert knowledge. It takes advantage of available historical data, and provides efficient algorithms for detecting and predicting most plausible scenarios. Our approach is illustrated using the well known DARPA 2000 data set.

Abstract: This paper presents a fuzzy expert system for the creation of landslide possibility maps using change of land-use data from Earth observation, as well as historical, rainfall, and earthquake data stored in a geographic information system, as input. The difference with other systems is in the use of change (differential) input data. The method is tested with 16 documented landslides. The fuzzy neural network (NN) developed can predict the crowns of 13 out of the 16 landslides to be among the 5% most at-risk pixels that are identified in the area of study, which covers 100 km2. The fuzzy expert system considers the rules that increase the possibility of a landslide, as supplied by experts, and expresses them in the form of an empirical algebraic formula. It then fuzzifies the various thresholds they rely on and, in conjunction with uncertainties that are reported by the classifier that decides the land-use change, produces a fuzzy algebraic formula that may be used to identify the range of uncertainty in the possibility of a landslide in terms of the ranges of uncertainty in the input variables. This formula is used to train an Ishibuchi fuzzy NN, which has been designed to capture uncertainty in the rules and uncertainty in the input variables. It is this Ishibuchi NN that acts as a fuzzy expert system.

Abstract: Lost circulation is the most common problem encountered while drilling oil wells This paper describes a distributed fuzzy expert system, called Smart-Drill, aimed in helping petroleum engineers to diagnose and solve lost circulation problems To represent and manipulate perception-based evaluations of uncertainties of facts and rules, the expert system uses an uncertainty model with qualitative scales of plausibility values and multi-set-based fuzzy algebra of strict monotonic operations Its realization in inference procedures permits taking into account the change of plausibility of premises in expert systems rules Original tools like CAPNET Expert System Shell, Knowledge Acquisition Tool and WITSML Converter implementing the proposed model were used for the development of the Smart-Drill Overall, the system architecture is discussed and implementation details are provided Both desktop and Web-based implementations permit petroleum engineers benefit from the system working out in the field The system is currently at field testing phase in PEMEX, Mexican Oil Company

Abstract: Several technologies are emerging that provide new ways to capture, store, present and use knowledge. This book is the first to provide a comprehensive introduction to five of the most important of these technologies: Knowledge Engineering, Knowledge Based Engineering, Knowledge Webs, Ontologies and Semantic Webs. For each of these, answers are given to a number of key questions (What is it? How does it operate? How is a system developed? What can it be used for? What tools are available? What are the main issues?). The book is aimed at students, researchers and practitioners interested in Knowledge Management, Artificial Intelligence, Design Engineering and Web Technologies. During the 1990s, Nick worked at the University of Nottingham on the application of AI techniques to knowledge management and on various knowledge acquisition projects to develop expert systems for military applications. In 1999, he joined Epistemics where he worked on numerous knowledge projects and helped establish knowledge management programmes at large organisations in the engineering, technology and legal sectors. He is author of the book "Knowledge Acquisition in Practice", which describes a step-by-step procedure for acquiring and implementing expertise. He maintains strong links with leading research organisations working on knowledge technologies, such as knowledge-based engineering, ontologies and semantic technologies.