Abstract: Tracking-by-detection based on segmentation, Kalman predictions and LSAP association.Occlusion management: SVM kernel metric for GCH+LBP+HOG image features.Overall performance near to 85% while tracking under occlusions in CAVIAR dataset.Human behavior analysis (exits, loitering, etc.) in naturalistic scenes in shops.Real-time multi-camera performance with a processing capacity near to 50fps/camera. Expert video-surveillance systems are a powerful tool applied in varied scenarios with the aim of automatizing the detection of different risk situations and helping human security officers to take appropriate decisions in order to enhance the protection of assets. In this paper, we propose a complete expert system focused on the real-time detection of potentially suspicious behaviors in shopping malls. Our video-surveillance methodology contributes several innovative proposals that compose a robust application which is able to efficiently track the trajectories of people and to discover questionable actions in a shop context. As a first step, our system applies an image segmentation to locate the foreground objects in scene. In this case, the most effective background subtraction algorithms of the state of the art are compared to find the most suitable for our expert video-surveillance application. After the segmentation stage, the detected blobs may represent full or partial people bodies, thus, we have implemented a novel blob fusion technique to group the partial blobs into the final human targets. Then, we contribute an innovative tracking algorithm which is not only based on people trajectories as the most part of state-of-the-art methods, but also on people appearance in occlusion situations. This tracking is carried out employing a new two-step method: (1) the detections-to-tracks association is solved by using Kalman filtering combined with an own-designed cost optimization for the Linear Sum Assignment Problem (LSAP); and (2) the occlusion management is based on SVM kernels to compute distances between appearance features such as GCH, LBP and HOG. The application of these three features for recognizing human appearance provides a great performance compared to other description techniques, because color, texture and gradient information are effectively combined to obtain a robust visual description of people. Finally, the resultant trajectories of people obtained in the tracking stage are processed by our expert video-surveillance system for analyzing human behaviors and identifying potential shopping mall alarm situations, as are shop entry or exit of people, suspicious behaviors such as loitering and unattended cash desk situations. With the aim of evaluating the performance of some of the main contributions of our proposal, we use the publicly available CAVIAR dataset for testing the proposed tracking method with a success near to 85% in occlusion situations. According to this performance, we corroborate in the presented results that the precision and efficiency of our tracking method is comparable and slightly superior to the most recent state-of-the-art works. Furthermore, the alarms given off by our application are evaluated on a naturalistic private dataset, where it is evidenced that our expert video-surveillance system can effectively detect suspicious behaviors with a low computational cost in a shopping mall context.

Abstract: Bayesian inference networks, a synthesis of statistics and expert systems, have advanced reasoning under uncertainty in medicine, business, and social sciences. This innovative volume is the first comprehensive treatment exploring how they can be applied to design and analyze innovative educational assessments. Part I develops Bayes nets foundations in assessment, statistics, and graph theory, and works through the real-time updating algorithm. Part II addresses parametric forms for use with assessment, model-checking techniques, and estimation with the EM algorithm and Markov chain Monte Carlo (MCMC). A unique feature is the volumes grounding in Evidence-Centered Design (ECD) framework for assessment design. This design forward approach enables designers to take full advantage of Bayes nets modularity and ability to model complex evidentiary relationships that arise from performance in interactive, technology-rich assessments such as simulations. Part III describes ECD, situates Bayes nets as an integral component of a principled design process, and illustrates the ideas with an in-depth look at the BioMass project: An interactive, standards-based, web-delivered demonstration assessment of science inquiry in genetics. This book is both a resource for professionals interested in assessment and advanced students. Its clear exposition, worked-through numerical examples, and demonstrations from real and didactic applications provide invaluable illustrations of how to use Bayes nets in educational assessment. Exercises follow each chapter, and the online companion site provides a glossary, data sets and problem setups, and links to computational resources.

Abstract: Runtime verification (RV) consists in part of checking execution traces against user-provided formalized specifications. Throughout the last decade many new systems have emerged, most of which support specification notations based on state machines, regular expressions, temporal logic, or grammars. The field of artificial intelligence (AI) has for an even longer period of time studied rule-based production systems, which at a closer look appear to be relevant for RV, although seemingly focused on slightly different application domains, such as, for example, business processes and expert systems. The core algorithm in many of these systems is the Rete algorithm. We have implemented a rule-based system, named LogFire, for runtime verification, founded on the Rete algorithm, as an internal DSL in the Scala programming language (in essence a library). Using Scala's support for defining DSLs allows to write rules elegantly as part of Scala programs. This combination appears attractive from a practical point of view. Our contribution is part conceptual in arguing that such rule-based frameworks originating from AI are suited for RV. Our contribution is technical by implementing an internal rule DSL in Scala; by illustrating how specification patterns can easily be encoded that generate rules, and by adapting and optimizing the Rete algorithm for RV purposes. An experimental evaluation is performed comparing to six other trace analysis systems. LogFire is currently being used to process telemetry from the Mars Curiosity rover at NASA's Jet Propulsion Laboratory.

Abstract: Sustainability has become an important initiative discussed and undertaken, not only by private buildings, but also by public buildings which both dealing with residential, office, commercial as well as hospital. Sustainable building is the practice of designing, constructing, operating, maintaining, and removing buildings in ways that conserve natural resources and reduce pollution. Rating systems provide effective framework for assessing building environmental performance and they measure a building's sustainability by applying a set of criteria organized in different categories. A good Green Building Rating System (GBRS) should cover key indicators reflecting a building's characteristics and keep their performance in balance. This paper proposed a knowledge-based expert system as a tool to assess the performance level of a green building based on assessment factors of green building rating systems. Analytic Hierarchy Process (AHP) and fuzzy logic is adopted in order to develop the knowledge-based expert system. The data for this research collected from the experts in the field via pair-wise and Likert-based questionnaires. Using AHP, the most important parameters of rating systems according to their weights selected to be incorporated in the Fuzzy Inferences System (FIS) of fuzzy logic model. The fuzzy rules (knowledge) discovered from the collected data for FIS to assess the performance level of the green buildings from the Environmental, Social and Economical perspectives denoted as SE2. The outcome of this research is accordingly a performance assessment tool that analyzes the effect of factors in developing the sustainable building.

Abstract: Automating the development of construction schedules has been an interesting topic for researchers around the world for almost three decades. Researchers have approached solving scheduling problems with different tools and techniques. Whenever a new artificial intelligence or op- timization tool has been introduced, researchers in the con- struction field have tried to use it to find the answer to one of their key problems—the "better" construction schedule. Each researcher defines this "better" slightly different. This article reviews the research on automation in construction scheduling from 1985 to 2014. It also covers the topic using different approaches, including case-based reasoning, knowledge-based approaches, model-based approaches, ge- netic algorithms, expert systems, neural networks, and other methods. The synthesis of the results highlights the share of the aforementioned methods in tackling the scheduling chal- lenge, with genetic algorithms shown to be the most dominant approach. Although the synthesis reveals the high applicabil- ity of genetic algorithmstothe different aspects ofmanaginga project, including schedule, cost, and quality, it exposed a more limited project management application for the other methods.

Abstract: This book presents the current methodologies and practical applications of managing pavements. The book contains seven parts that include : Part I: The Evolution of Pavement Management; Part II: Data Requirements; Part III: Determining Present and Future Needs and Priority Programming of Rehabilitation and Maintenance; Part IV: Structural Design and Economic Analysis: Project Level; Part V: Implementation of Pavement Management Systems; Part VI: Examples of Working Systems; and Part VII: Looking Ahead. The chapters are: Introduction; Birth and Teen Years of Pavement Management; Pavement Management Development from 2010; Setting the Stage; Overview of Pavement Management Data Needs; Inventory Data Needs; Characterizing Pavement Performance; Evaluation of Pavement Structural Capacity; Evaluation of Pavement Surface Distress Condition Surveys; Evaluation of Pavement Safety; Combined Measures of Pavement Quality; Data Base Management; Communicating the Present Status of Pavement Networks; Establishing Criteria; Prediction Models for Pavement Deterioration; Determining Needs; Rehabilitation and Maintenance Alternatives; Priority Programming of Rehabilitation and Maintenance; Developing Combined Programs of Maintenance and Rehabilitation; A Framework for Pavement Design; The Mechanistic-Empirical Pavement Design Guide (MEPDG) Process for Pavement Design; The MEPDG for Design of New and Reconstructed Rigid Pavements; Rehabilitation of Existing Pavements; MEPDG in Practice; Economic Evaluation of Alternative Pavement Design Strategies and Selection of an Optimal Strategy; Steps and Key Components of Implementation; Role of Construction; Role of Maintenance; Research Management; Basic Features of Working Systems; Network Level Examples of Pavement Management; Project Level Examples of PMS Software; Highway Design Manual-4 (HDM-4) the Upgraded World Bank Model; City and County Pavement Management Systems; Airport Pavement Management; Analyzing Special Problems; Applications of Expert Systems Technology; New and Emerging Technologies; Institutional Issues and Barriers Related to Pavement Management Implementation; Cost and Benefits of Pavement Management; Future Direction and Need for Innovation in Pavement Management; and Developments in Asset Management.

Abstract: This paper defines expert systems and discusses their relationship with artificial intelligence and decision support systems. Included in this paper is a detailed description of the steps to follow in expert system de-velopment. The advantages and problems associated with the use of expert systems are delineated, followed by a description of a multitude of current marketing applications of expert systems. The paper concludes by examining the future direction of expert system development.

Abstract: The development of Artificial Intelligent (AI) technology system can be a wide scope; for an instant, there are rule-based expert system, frame-based expert system, fuzzy logic, neural network, genetic algorithm, etc. The remarkable achievement applications of AI has been reported in different disciplines including field of medicals, militaries, chemistry, engineering, manufacturing, management, and others. Its’ discoveries and contributions through of AI study since the early 1970s were be significant step to enhance better performance of human work activities and probably replaced by these technologies. Today, there a lot of intelligent machine is available in everywhere such as airport gate scanner, movie theater counter ticket, vending machine, ATM machine, washing machine, etc. Expert system has been used widely in many areas and industries. This paper is described the current research and development of expert system.

Abstract: This paper describes an artificial intelligence-based software system that augments public affairs reporters’ ability to sort through data and identify investigative storytelling opportunities. A prototype of the model was developed and was used to analyze education data. The successful prototype and the social impact of the stories derived from the prototype suggest this approach as a valid option for newsrooms that seek to tell more compelling, data-rich stories about public affairs issues.

Abstract: For certain problems of predictive medicine, early and differential diagnosis in construction of relevant expert systems are best solved using methods of fuzzy decision-making adapted to classification problems. For selection of shape and parameters of membership functions of studied classes of states and methods of their aggregation, the use of the methodology of exploratory analysis followed by unification of particular decision rules into fuzzy groups that provide the best quality of classification is proposed. Recommendations are given for the synthesis of partial fuzzy decision rules and their groups to build a knowledge base of medical expert systems.

Abstract: Software requirements engineering is a critical discipline in the software development life cycle. The major problem in software development is the selection and prioritization of the requirements in order to develop a system of high quality. This research analyzes the issues associated with existing software requirement prioritization techniques. One of the major issues in software requirement prioritization is that the existing techniques handle only toy projects or software projects with very few requirements. The current techniques are not suitable for the prioritization of a large number of requirements in projects where requirements may grow to the hundreds or even thousands. The research paper proposes an expert system, called the Priority Handler (PHandler), for requirement prioritization. PHandler is based on the value-based intelligent requirement prioritization technique, neural network and analytical hierarchical process in order to make the requirement prioritization process scalable. The back-propagation neural network is used to predict the value of a requirement in order to reduce the extent of expert biases and make the PHandler efficient. Moreover, the analytical hierarchy process is applied on prioritized groups of requirements in order to enhance the scalability of the requirement prioritization process.

Abstract: Expert systems (ES) are a branch of applied artificial intelligence. The basic idea behind ES is simply that expertise, which is the vast body of task-specific knowledge, is transferred from a human to a computer. ES provide powerful and flexible means for obtaining solutions to a variety of problems that often cannot be dealt with by other, more traditional and orthodox methods. Thus, their use is proliferating to many sectors of our social and technological life, where their applications are proving to be critical in the process of decision support and problem solving. Earthquake professionals for many decades have recognized the benefits to society from reliable earthquake predictions, but uncertainties regarding source initiation, rupture phenomena, and accuracy of both the timing and magnitude of the earthquake occurrence have often times seemed either very difficult or impossible to overcome. This research proposes and implements an expert system to predict earthquakes from previous data. This is achieved by applying association rule mining on earthquake data from 1972 to 2013. These associations are polished using predicate-logic techniques to draw stimulating production-rules to be used with a rule-based expert system. The proposed expert system was able to predict all earthquakes which actually occurred within 12h at-most.

Abstract: Systematic interdisciplinary literature review on behavioral operations.Latent Semantic Analysis applied for review and knowledge extraction methodology.Text analysis and mining to combine statistical methods and expert judgment.Knowledge extracted in the form of key latent factors.Text mining approach aids taxonomy of research on power influences in supply chain. This study aims to systematically review the cross disciplinary literature covering the time period from 1934 to January 2013 on behavioral operations in supply chain in order to identify and define the taxonomy of the research on power influences in supply chain. A list of noted journals and search results from Science Direct and Web of Knowledge, IEEE Xplore, and INFORMS (approximately 11,000 journal articles) is used to prepare content collection. Latent Semantic Analysis (LSA) is applied as the review and knowledge extraction methodology. Using the text analysis and mining method we can combine statistical methods and expert human judgment to extract knowledge in the form of key latent factors. The LSA based analysis gives the study a scientific grounding which helps to overcome the subjectivity of collective opinion about the trends. This approach allows proposing taxonomy of the research on power influences in supply chain. The adopted systems approach is used to find research gaps in each class of taxonomy. An emerging trend is noticed in the research of behavioral operations in supply chain. Understanding such a scholarly structure and future trends will assist researchers to assimilate the divergent developments of this multidisciplinary research in one place. This review will be beneficial for practitioners as they consider behavioral aspects in decision making. We have also studied articles related to supply chain published in Expert Systems with Applications (ESWA) journal. We have speculated what an ESWA-related community would like to see in future publications. This will encourage researchers to explore the recommended areas and publish to these outlets.

Abstract: Interest in the use of (big) company data and data-mining models to guide decisions exploded in recent years. In many domains there are human experts whose knowledge is essential in building, interpreting and applying these models. However, the impact of integrating expert opinions into the decision-making process has not been sufficiently investigated. This research gap deserves attention because the triangulation of information sources is critical for the success of analytical projects. This paper contributes to the decision-making literature by (a) detailing the natural advantages of the Bayesian framework for fusing multiple information sources into one decision support system (DSS), (b) confirming the necessity for adjusted methods in this data-explosion era, and (c) opening the path to future applications of Bayesian DSSs in other organizational research contexts. In concrete, we propose a Bayesian decision support framework that formally fuses subjective human expert opinions with more objective organizational information. We empirically test the proposed Bayesian fusion approach in the context of a customer-satisfaction prediction study and show how it improves the prediction performance of the human experts and a data-mining model ignoring expert information. This paper introduces a decision support framework to fuse information sources.Fusing big data with human opinions ensures higher-quality decisions.The paper demonstrates the advantage of the Bayesian machinery for information fusion.

Abstract: Little knowledge exists on the impact and results associated with e-government projects in many specific-use domains. Therefore, it is necessary to evaluate the efficiency and effectiveness of e-government systems. Because the development of e-government is a continuous process of improvement, it requires continuous evaluation of the overall e-government system as well as evaluation of its various dimensions such as determinants, characteristics and results. E-government development is often complex, with multiple stakeholders, large user bases and complex goals. Consequently, even experts have difficulties in evaluating these systems, especially in an integrated and comprehensive way, as well as on an aggregate level. Expert systems are a candidate solution to evaluate such complex e-government systems. However, it is difficult for expert systems to cope with uncertain evaluation data that are vague, inconsistent, highly subjective or in other ways, challenging to formalize. This paper presents an approach that can handle uncertainty in e-government evaluation: the combination of Belief Rule Base knowledge representation and Evidential Reasoning. This approach is illustrated with a concrete prototype, known as the Belief Rule Based Expert System BRBES and implemented in the local e-government of Bangladesh. The results have been compared with a recently developed method of evaluating e-government, and it is demonstrated that the results of the BRBES are more accurate and reliable. The BRBES can be used to identify the factors that need to be improved to achieve the overall aim of an e-government project. In addition, various 'what if' scenarios can be generated, and developers and managers can obtain a foretaste of the outcomes. Thus, the system can be used to facilitate decision-making processes under uncertainty.

Abstract: There is a wide scope of interdisciplinary crossing points between Artificial Intelligence (AI) and Cybersecurity. On one hand, AI advancements, for example, deep learning, can be introduced into cybersecurity to develop smart models for executing malware classification and intrusion detection and threatening intelligent detecting. Then again, AI models will confront different cyber threats, which will affect their sample, learning, and decision making. Along these lines, AI models need specific cybersecurity defense and assurance advances to battle ill-disposed machine learning, preserve protection in AI, secure united learning, and so forth. Because of the above two angles, we audit the crossing point of AI and Cybersecurity. To begin with, we sum up existing research methodologies regarding fighting cyber threats utilizing artificial intelligence, including receiving customary AI techniques and existing deep learning solutions. At that point, we analyze the counterattacks from which AI itself may endure, divide their qualities, and characterize the relating protection techniques. And finally, from the aspects of developing encrypted neural networks and understanding safe deep learning, we expand the current analysis on the most proficient method to develop a secure AI framework. This paper centers mainly around a central question: "By what means can artificial intelligence applications be utilized to upgrade cybersecurity?" From this question rises the accompanying set of sub-questions: What is the idea of artificial intelligence and what are its fields? What are the main areas of artificial intelligence that can uphold cybersecurity? What is the idea of data mining and how might it be utilized to upgrade cybersecurity? Hence, this paper is planned to reveal insight into the idea of artificial intelligence and its fields, and how it can profit by applications of AI brainpower to upgrade and improve cybersecurity. Using an analytical distinct approach of past writing on the matter, the significance of the need to utilize AI strategies to improve cybersecurity was featured and the main fields of application of artificial intelligence that upgrade cybersecurity, for example, machine learning, data mining, deep learning, and expert systems.

Abstract: Diseases Ear, Nose and Throathas become adisease that issuffered by the world community. ENT disease progression and higher, it is not accompanied by anumber of experts. In this case, an analysis should be doneto speed up the process of diagnosis. The refore it is necessary to use the expert system is a computer application that behaves like an expert. Expert system capable of solving problems that typically can only be solved by an expert using the knowledge base, facts and reasoning techniques. In this analysis using aforward chaining inference engine. In this approach, starting from the information entered and then draws conclusions, tracking the forefind facts in accordance with the IF-THEN rules. Based on the test system accuracy rate forward chaining method to detect ENT disease that is 100%, which according to the data obtained from the ENT specialist to determine disease based on symptoms exist.

Abstract: A fuzzy rule based expert system for seismic signal classification is proposed.Relevant discriminant features are extracted from seismic signal.The system exploits the information derived from both expert's knowledge and data.Adding weights to fuzzy rules generally improves the classification results.Vote by multiple rule fuzzy reasoning method shows the best performance. Automatic classification of seismic events is of great importance due to the large amount of data received continuously. Seismic analysts classify events by visual inspection and calculation of event signal characteristics. This process is subjective and demands hard work as well as a significant amount of time and considerable experience. A reliable automatic classification task considerably reduces the effort required and makes classification faster and more objective. The aim of this study is to develop a fuzzy rule based expert classification system that is able to imitate human reasoning and incorporate the analyst's knowledge of seismic event classification. The fundamental idea behind using this approach was motivated by the way in which human analysts classify seismic events based on a set of experiential rules. Additionally, this approach was chosen due to its interpretability and adjustability, as well as its ability to manage the complexity of real data. Relevant discriminant features are extracted from event signal. Using these features, the classification system was built based on the vote by multiple rule fuzzy reasoning method with three types of rules. Comparison of this method with the single winner classical fuzzy reasoning model was carried out. Classification results on real seismic data showed the robustness of the classifier and its capability to operate in on-line classification.

Abstract: Work objective was to map tacit knowledge into agents of the manufacturing process.It was possible to map the known type to be elicited by the use of Pareto Chart tool.The mapping of critical issues before the decision to elicit tacit knowledge.Bayesian networks based on production rules to predict adverse situations in process. The paper presents the application of a Technical Mapping and tacit knowledge elicitation in industry in order to promote the modeling of tacit knowledge to explicit and represent it in the form of production rules for use in manufacturing processes. The technique was applied with the involved people in the lithographic process in a Metallurgical Company located in southern Brazil. Knowledge of two production coordinators were modeled. For the process of knowledge acquisition and mapping of attributes and values to feed the knowledge base of an expert system, were used quality tools such as Brainstorming, Pareto Chart and Ishikawa Diagram associated with knowledge elicitation techniques such as unstructured interview, rating chips, observation technique, limitation of information and protocol analysis. Quality tools and techniques of knowledge elicitation were systematized to promote process mapping and the elicitation of tacit knowledge, with the aim of representing knowledge by means of production rules. We constructed two knowledge bases with the same methods of production, one in a non-probabilistic expert system (knowledge-based system) and the other in a probabilistic expert system (Bayesian networks) in order to perform comparisons and simulations of the results found. Expert systems perform systematic analysis from the answers given by those involved in lithographic labels process while the defect is identified in order to support the user in diagnosing the root cause of the failure process. From simulations of changes in process variables was possible to prove the hypothesis of the use of probabilistic expert system as industrial support tool in preventing the occurrence of defects in the process and result in a productivity gain.

Abstract: Expert system for passive sound source localization that makes use of multiple GPUs.Fine spatial grids and a high number of microphones provide excellent localization.GPU resources for managing a large expert system are described.A complete set of simulations evaluates the performance of the expert system.Excellent localization accuracy is achieved even in adverse environments. Sound source localization is an important topic in expert systems involving microphone arrays, such as automatic camera steering systems, human-machine interaction, video gaming or audio surveillance. The Steered Response Power with Phase Transform (SRP-PHAT) algorithm is a well-known approach for sound source localization due to its robust performance in noisy and reverberant environments. This algorithm analyzes the sound power captured by an acoustic beamformer on a defined spatial grid, estimating the source location as the point that maximizes the output power. Since localization accuracy can be improved by using high-resolution spatial grids and a high number of microphones, accurate acoustic localization systems require high computational power. Graphics Processing Units (GPUs) are highly parallel programmable co-processors that provide massive computation when the needed operations are properly parallelized. Emerging GPUs offer multiple parallelism levels; however, properly managing their computational resources becomes a very challenging task. In fact, management issues become even more difficult when multiple GPUs are involved, adding one more level of parallelism. In this paper, the performance of an acoustic source localization system using distributed microphones is analyzed over a massive multichannel processing framework in a multi-GPU system. The paper evaluates and points out the influence that the number of microphones and the available computational resources have in the overall system performance. Several acoustic environments are considered to show the impact that noise and reverberation have in the localization accuracy and how the use of massive microphone systems combined with parallelized GPU algorithms can help to mitigate substantially adverse acoustic effects. In this context, the proposed implementation is able to work in real time with high-resolution spatial grids and using up to 48 microphones. These results confirm the advantages of suitable GPU architectures in the development of real-time massive acoustic signal processing systems.

Abstract: The problem of predicting crystal structures is discussed in the context of artificial intelligence systems. The steps of creation of an expert system are considered as applied to crystal design, where the crucial step is the invention of new structure descriptors. A number of such descriptors proposed quite recently are listed; most of them characterize local coordination or overall topology of the structure network. An important part of the expert system is the knowledge database that contains correlations between the descriptors; it is used by a computer analyzer, the inference machine, to make a conclusion about the possibility of obtaining a particular structural motif. All the steps for developing the expert system are illustrated with the analysis of 811 cyanide complexes and examples of the structure prediction are given.

Abstract: The diseases in glands of human bodies have been increased with high rate in the last decade. Thyroid is one of these glands that its disease has spread worldwide. The main function of thyroid gland is to balance the metabolism and cells' activity. Since it does its own task abnormally, thyroid disorders will occur and the negligence of them may cause irreparable events. Because of inaccessibility of Endocrinologist experts for most people, modeling and developing an expert system for diagnosis thyroid's disease that can be accessible in everywhere is vital. This paper presents a fuzzy rule-based expert system for diagnosis thyroid's disease. This proposed system includes three steps: pre-processing (feature selection), neuro-fuzzy classification and system evaluating. In the proposed system, the process of diagnosis encounters with vagueness and uncertainty in final decision. So, we handled the imprecise knowledge by using fuzzy logic. In neuro-fuzzy classification step, we generated initial fuzzy rules by k-means algorithm and then scaled conjugate gradient algorithm (SCG) was used to determine the optimum values of parameters. In the last step, we used the generated fuzzy rules to model and evaluate the system. This system can help non-experts who are suspicious of their thyroid function or it can be used as a diagnosis assistance system to help experts for assuring their diagnosis.

Abstract: A probabilistic fuzzy system for innovative project risk assessment is proposed.A system as the extension of Mamdani probabilistic fuzzy system is described.A new method of system identification using parametric family of t-norms is proposed.The algorithm uses assumptions of fuzzy association rules.The approach uses analysis of probability of fuzzy events with technical risk factors. This paper presents the properties, identification issues and utilisation of a new concept of probabilistic fuzzy system for the innovative project risk assessment. This system constitutes the extension of Mamdani probabilistic fuzzy system. For this purpose, a group of risk factors, which influence risk variables, has been chosen. Linguistic risk variables are inputs to the innovation risk assessment system. The structure of fuzzy sets for linguistic values takes into account knowledge of a number of experts. Knowledge is presented as fuzzy IF-THEN rules together with probability measures of fuzzy events occurrence in the antecedent and conclusion of rules. The paper presents a new method of identification of the analysed system. The method uses parametric family of triangular t-norms, which facilitates inference parameters optimisation, enables flexible adjustment of a system to empirical data and makes the system more precise. The modified FP-Growth algorithm to create probabilistic fuzzy rule base is used. Using assumption of the minimal support of rules enables decreasing of knowledge base complexity while preserving the level of identification quality, comparable to the system with full marginal and conditional probability distributions. The results of the system inference have been compared with regression model and Mamdani fuzzy inference system. Finally, the numerical experiments show more precision of system inference than the compared method. The example of analytical use of created probabilistic fuzzy knowledge base in the context of technical innovation risk assessment is also presented.The constructed expert system has an identification character and it can be develop as a tool to help the assessment of applications for funding the implementation of innovative projects by the institutions established for this purpose.

Abstract: This paper presents an approach and architecture for Dynamic Course Generation, based on applying AI planning techniques to a structured representation of the domain knowledge and allowing explicit representation of teaching expertise. An individual course is generated automatically for a given teaching goal and is dynamically adapted at run-time to a student's individual progress and preferences according to the teaching expertise. The separate representation of the teaching materials from the domain structure allows an easier updating and re-use of ready CAL materials. In this way our approach provides an alternative to traditional CALauthoring. An implementation in a simple engineering domain is described . An evaluation of the benefits of this approach in terms of cost-effectiveness for authoring is shown.