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We present e-Workbench, a prototype intelligent system, which uses a semi-automated approach for acquiring knowledge to support decision making in collaborative e-Work. Our focus is to enable an e-Work environment to acquire knowledge of the domain of work in order to augment workers' capabilities by proactively providing strategic level cognition support in the form of intelligent explanations and expert-level justifications to assist in selecting the best problem solving strategy and facilitating collaborative decision making. [ABSTRACT FROM AUTHOR]

Many complex networks have a small-world topology characterized by dense local clustering of connections between neighbouring nodes yet a short path length between any (distant) pair of nodes due to the existence of relatively few long-range connections. This is an attractive model for the organization of terrorist networks because small-world topology can support segregated and integrated information processing. In this article, we empirically tested a number of indicative terrorist networks, we discovered that most of the networks have low connectedness and high closeness, that is, the networks contain small-world characteristics. [ABSTRACT FROM AUTHOR]

This paper describes a method for assignment and deployment of sensors in a virtual environment using constraint programming. We extend an existing model (multiple knapsack problem) to implement this assignment and placement, according to a given set of requirements (modelled as a utility extension). [ABSTRACT FROM AUTHOR]

The RSS news Aggregators is becoming more and more popular among Internet users. These Aggregators download news feeds from online news websites and provide an interface for users to view and organize them. Users can subscribe to numerous feeds. When they add more sources the amount of news feeds becomes more difficult to manage. The users then experience information overload. In order to tackle the RSS overload problem, Fido, an interface agent, is designed to filter news based on user preferences and feedback and presents personalized RSS news items to the users. This paper presents the main features of Fido and design rationale behind it. [ABSTRACT FROM AUTHOR]

This article deals with how metaknowledge can improve rule-based system and presents a new Reflexive System Inference Engine (RSIE) which not only enables the activation of rules, making it belong to systems managing metaknowledge. The experimentation section shows a rule-based system named IDRES with a structure which has been modified to use metaknowledge. [ABSTRACT FROM AUTHOR]

The vast majority of existing Intrusion Detection Systems (IDS) incorporates static knowledge bases, which contain information corresponding to specific attack patterns. Although such knowledge bases can gradually expand, to be able to detect new attacks, this requires the maintenance of an expert. This paper describes a potential application of computationally evolving intelligent behaviour in conjunction with network intrusion detection. Our aim is to develop a standalone Network Intrusion Detection System (NIDS), capable of working in offline and online mode by evolving its structure and parameters in order to prevent both known and novel intrusions. [ABSTRACT FROM AUTHOR]

Copyright of Applications & Innovations in Intelligent Systems XV is the property of Springer eBooks and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

One of the most desired aspects for power suppliers is the acquisition/sell of energy in a future time. This paper presents a study for power supply forecasting of the residential class, based on time series methods and neural networks, considering short and long term forecast, both of great importance for power suppliers in order to define the future power consumption of a given region. [ABSTRACT FROM AUTHOR]

Creative design is very hard to model or imitate by computers. However, there exist a variety of artificial intelligence techniques that can be applied to highly constrained, well-defined design tasks. Creative evolutionary design [1] is one such group of techniques with reported success. Here we present our genetic programming based method for automatic design of lace knitting stitch patterns. First we devise a genetic representation of knitting charts that accurately reflects their usage for hand knitting the pattern. We then apply a basic evolutionary algorithm for generating the patterns, where the key of success is evaluation. We propose automatic evaluation of the patterns, without interaction with the user. We present some patterns generated by the method and then discuss further possibilities for bringing automatic evaluation closer to human evaluation. [ABSTRACT FROM AUTHOR]

This paper documents our ongoing efforts in devising efficient strategies in motion control of the brittle star-typed robot. As part of the control framework, each robotic leg consisting of series of homogenous modules is modeled as a neural network. The modules representative of neurons are interconnected via synaptic weights. The principle operation of the module involves summing the weighted input stimulus and using a sinusoidal activation function to determine the next phase angle. Motion is achieved by propagating phase information from the modules closest to the main body to the remainder of the modules in the leg via the synaptic weights. Genetic algorithm was used to evolve near optimal control parameters. Simulations results indicate that the current neural network inspired control model produces better motion characteristics than the previous cellular automata-based control model as well as addresses other issues such as fault tolerance. [ABSTRACT FROM AUTHOR]

We present a robust road detection and tracking method using multiple vanishing points and the condensation filter. We represent the road using an extended hyperbola model with an added non-linear term to handle transitions between straight and curved road segments. The parameters of the road model are estimated using multiple vanishing points located in road segments. A vanishing line is then determined using a robust iterative curve fitting technique to recover parameters of the road model. These are then fed into a robust condensation tracker [1] to track the road. The tracker is able to deal with difficult road conditions. Experiments using real road videos demonstrate the suitability of our approach for real-time applications. A comparison with the Kalman filtering technique demonstrates the robustness of our approach. [ABSTRACT FROM AUTHOR]

This paper describes the multi-agent system used to control a package of four Uninhabited Air Vehicles (UAVs). The system has recently been used in a series of test flights where the pilot of a fast jet controlled a team of four UAVs (one real, three simulated) carrying out a representative mission. The structure of the system is described and the re-organisation of the agents as the mission progresses is illustrated with an example. The paper concludes by describing the importance of whole system issues and the integration and test cycle for getting AI techniques working and accepted in an application. [ABSTRACT FROM AUTHOR]

In recent years, the fast evolution in computer games has moved government organizations to investigate how they can be exploited as virtual environments to simulate scenarios which could be expensive or even dangerous to set up in real life, in particular when collaboration among humans is required in order to carry out a shared plan. The proposed system allows the tracking of progresses achieved by each plan participant within the planning domain, by mapping its steps to states and humans' actions in the virtual environment. It also permits to render the environment and the planning software loosely coupled and to provide flexible responses to participants' actions in the form of different alternatives to the same plan, such that goals can be achieved following different courses of action. [ABSTRACT FROM AUTHOR]

We propose a knowledge based decision support system for fund raising management, which uses fuzzy logic to evaluate the most promising strategies. Our approach exploits economic modelling and operational results, which stress that donors' profiles affect the probability of giving, allowing a more effcient management of the information on potential donors, and improving current approaches. The first experiments show that knowledge based systems are particularly suitable to solve this class of problems both for their ability to manage also qualitative and uncertain information, and to explicitly represent the knowledge of the fund raiser. The results, obtained with simulated data, show the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

This paper deals with data mining applications for the supply chain inventory management. It describes the use of business intelligence (BI) tools, coupled with data warehouse to employ data mining technology to provide accurate and up-to-date information for better inventory management decisions. The methodology is designed to provide out-of-stock forecasts at the store/product level. The first phase of the modelling process consists of clustering stores in the supply chain based upon aggregate sales patterns. After quality store-cluster models have been constructed, these clusters are used to more accurately make out-of-stock predictions at the store/product level using the decision trees and neural network mining algorithms. The methods for evaluation and accuracy measurement are described. Also, the specialized front-end BI web portal that offers integrated reporting, web analytics, personalization, customization and collaboration is described. [ABSTRACT FROM AUTHOR]

DNA and fingerprint identifications continue to form an integral part of the detection of a wide range of crime types, especially volume crime such as burglary and auto crime. More than ten years ago, researchers first commented on the lack of emphasis on ‘outcome' (i.e. crime detection) related performance indicators for UK police forces. Since then much work has been carried out, mainly by the Association of Chief Police Officers of England & Wales and the Home Office, to produce a framework of forensic science performance indicators that reflect accurately the contribution made by forensic science to crime detection. In this paper, we consider the data currently being collected by five UK police forces that use popular proprietary computer based data collection systems. The accuracy of the data collection has been analysed using a neural network and has identified collection errors in all five forces. These errors are such that they could adversely affect the accuracy and interpretation of the national collection of forensic science data conducted by the Home Office. We propose using this neural network to check the accuracy of data collection and also to provide a ‘front end' collator for national forensic science data returns to the Home Office. Such an approach would improve the accuracy of data collection nationally and also provide some reassurance over the consistency of data recording by individual forces. [ABSTRACT FROM AUTHOR]

Registration is the process of geometrically aligning two images taken from different sensors, viewpoints or instances in time. It plays a key role in the detection of defects or anomalies for automated visual inspection. A multiagent distributed blackboard system has been developed for intensity-based image registration. The images are divided into segments and allocated to individual agents on separate processors, allowing parallel computation of a similarity metric that measures the degree of likeness between reference and sensed images after the application of a transform. The need for a dedicated control module is removed by coordination of Distributor, Manager, and Worker agents through communication via the blackboard. Tests show that the system achieves large-scale registration with substantial speedups, provided the communication capacity of the blackboard is not saturated. The success of the approach is demonstrated in the detection of manufacturing defects on screen-printed plastic bottles and printed circuit boards. [ABSTRACT FROM AUTHOR]

Diagnosis of Sinus condition is considered a difficult task in medical clinics due to the similar nature of the symptoms and the complexity of the images (e.g. plane of image, resolution) obtained using either CT-Scan. Discussions with consultant doctors and radiologists working in this area pointed at the need for a computer-based analysis and diagnosis tool that could be used as an aid to experts for diagnosing sinus diseases. There are a number of tools using traditional image processing techniques that are primarily useful for enhancing images. For an integrated system with potential diagnostic abilities artificial neural networks are good candidates that can combine image processing and diagnostic abilities in a single system. This paper presents the background and preliminary results in the development of an automated tool for the analysis and diagnosis of sinus conditions. The data used is in the form of CT scan images of sinus. Technology based on traditional image processing and Artificial Neural Networks (SOM) are explored for image processing and diagnosis. Anonymous CT-images of Sinuses were obtained from a local hospital. Preliminary results show that the proposed system has the potential to be a useful tool for clinicians in the areas of diagnosis and training of junior doctors. [ABSTRACT FROM AUTHOR]

We are concerned with methods to investigate homogeneous patterns among clinical heterogeneous complex diseases. This methodology involves (1) a cluster analysis to group individuals by similar disease patterns, (2) a visualization step to characterize the cluster patterns and (3) an evaluation step to ascertain the reliability of discovered patterns. It will be applied to individuals affected by osteo arthritis (OA) at multiple joint sites. Here, we present and compare two methods that are used to find groups of individuals sharing similar OA patterns. The first approach uses hierarchical clustering to derive the groups, model based clustering to assess their reliability and heat maps to characterize them. The second approach uses model based clustering to derive the groups, BIC to select the optimal model and heat maps to characterize each group. Our experimental results show that for this data set the second approach, which uses model based clustering and heat maps, works much better. [ABSTRACT FROM AUTHOR]

Copyright of Applications & Innovations in Intelligent Systems XV is the property of Springer eBooks and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper describes a system which incorporates natural language technologies, database manipulation and educational theories in order to offer learners a Negotiated Learner Model, for integration into an Intelligent Tutoring System. The system presents the learner with their learner model, offering them the opportunity to compare their own beliefs regarding their capabilities with those inferred by the system. A conversational agent, or "chatbot" has been developed to allow the learner to negotiate over the representations held about them using natural language. The system aims to support the metacognitive goals of self-assessment and reflection, which are increasingly seen as key to learning and are being incorporated into UK educational policy. The paper describes the design of the system, and reports a user trial, in which the chatbot was found to support users in increasing the accuracy of their self-assessments, and in reducing the number of discrepancies between system and user beliefs in the learner model. Some lessons learned in the development have been highlighted and future research and experimentation directions are outlined. [ABSTRACT FROM AUTHOR]

In several domains spatio-temporal data consisting of references to both space and time are collected in large volumes. Textual summaries of spatio-temporal data will complement the map displays used in Geographical Information Systems (GIS) to present data to decision makers. In the RoadSafe project we are working on developing Natural Language Generation (NLG) techniques to generate textual summaries of spatiotemporal numerical weather prediction data. Our approach exploits existing video processing techniques to analyse spatio-temporal weather prediction data and uses Qualitative Spatial Reasoning(QSR) techniques to reason with geographical data in order to compute the required content (information) for generating descriptions of geographically located events. Our evaluation shows that our approach extracts information similar to human experts. [ABSTRACT FROM AUTHOR]

Acquiring case representations from textual sources remains an interesting challenge for CBR research. Approaches based on methods in information retrieval require large amounts of data and typically result in knowledge-poor representations. The costs become prohibitive if an expert is engaged to manually craft cases or hand tag documents for learning. Thus there is a need for tools that automatically create knowledge-rich case representations from textual sources without the need to access large volumes of tagged data. Hierarchically structured case representations allow for comparison at different levels of specificity thus resulting in more effective retrieval than can be achieved with a fiat structure. In this paper, we present a novel method for automatically creating, hierarchically structured, knowledge-rich cases from textual reports in the Smart-House domain. Our system, SMART, uses a set of anchors to highlight key phrases in the reports. The key phrases are then used to learn a hierarchically structured case representation onto which reports are mapped to create the corresponding structured cases. SMART does not require large sets of tagged data for learning, and the concepts in the case representation are interpretable, allowing for expert refinement of knowledge. [ABSTRACT FROM AUTHOR]

In medicine many exceptions occur. In medical practice and in knowledge-based systems too, it is necessary to consider them and to deal with them appropriately. In medical studies and in research, exceptions shall be explained. We present a system that helps to explain cases that do not fit into a theoretical hypothesis. Our starting points are situations where neither a well-developed theory nor reliable knowledge nor a case base is available at the beginning. So, instead of reliable theoretical knowledge and intelligent experience, we have just some theoretical hypothesis and a set of measurements. In this paper, we propose to combine CBR with a statistical model. We use CBR to explain those cases that do not fit the model. The case base has to be set up incrementally, it contains the exceptional cases, and their explanations are the solutions, which can be used to help to explain further exceptional cases. [ABSTRACT FROM AUTHOR]

As the number of available Web services increases there is a growing demand to realise complex business processes by combining and reusing available Web services. In this context, the Ontology Web Language for Services (OWL-S) can be used to specify semantic types of the input and output data of a Web service and its functionality. This paper uses OWL-S to describe Web services and takes advantage of a XML syntax based on the OWL Web Ontology Language to encode OWL domain ontology fragments and SWRL rule fragments as the inputs and outputs of Web services. The approach presented outlines the use of the OWL's XML presentation syntax to obtain Web services that provide reasoning support and easily deal with facts and rules. To validate the proposal, the research has focused on Clinical Practice Guidelines (GLs) related to the biomedical field. This paper highlights the benefits and drawbacks found when applying the approach to obtain Web services that are intended to be used in clinical decision-making and rely on GLs. As an example of use, this paper concentrates on a services-based application for diagnosis and clinical management of Diabetic Retinopathy, where the end-users are health professionals who are not familiarized with Semantic Web technologies. [ABSTRACT FROM AUTHOR]

In the oceanic context, the aim of Target Motion Analysis (TMA) is to estimate the state, i.e. location, bearing and velocity, of a sound-emitting object. These estimates are based on a series of passive measures of both the angle and the distance between an observer and the source of sound, which is called the target. These measurements are corrupted by noise and false readings, which are perceived as outliers. Usually, sequences of measurements are taken and statistical methods, like the Least Squares method or the Annealing M-Estimator, are applied to estimate the target's state by minimising the residual in range and bearing for a series of measurements. In this research, an ACO-Estimator, a novel hybrid optimisation algorithm based on Ant Colony Optimisation, has been developed and applied to the TMA problem and its effectiveness was compared with standard estimators. It was shown that the new algorithm outperforms conventional estimators by successfully removing outliers from the measurements. [ABSTRACT FROM AUTHOR]

This paper presents the results of an investigation into the use of machine learning methods for the identification of narcotics from Raman spectra. The classification of spectral data and other high dimensional data, such as images, gene-expression data and spectral data, poses an interesting challenge to machine learning, as the presence of high numbers of redundant or highly correlated attributes can seriously degrade classification accuracy. This paper investigates the use of Principal Component Analysis (PCA) to reduce high dimensional spectral data and to improve the predictive performance of some well known machine learning methods. Experiments are carried out on a high dimensional spectral dataset. These experiments employ the NIPALS (Non-Linear Iterative Partial Least Squares) PCA method, a method that has been used in the field of chemometrics for spectral classification, and is a more efficient alternative than the widely used eigenvector decomposition approach. The experiments show that the use of this PCA method can improve the performance of machine learning in the classification of high dimensionsal data. [ABSTRACT FROM AUTHOR]

Meridian-collateral knowledge is a profound and complex part of the whole traditional Chinese medicine (TCM). It is the basis for many TCM-related computer applications. This work aimed to develop a sharable knowledge base of the human meridian-collateral system for those applications. We began the work by building a frame ontology of the meridian-collateral system (called OMCAP); and then developed a large-scale sharable instance base (called IMCAP), which was, with the aid of the tool OKEE. The OMCAP consists of 89 categories and 38 slots, and the IMCAP contains 1549 instance frames. [ABSTRACT FROM AUTHOR]

Web-based teaching via Intelligent Tutoring Systems (ITSs) is considered as one of the most successful enterprises in artificial intelligence. Indeed, there is a long list of ITSs that have been tested on humans and have proven to facilitate learning, among which we may find the well-tested and known tutors of algebra, geometry, and computer languages. These ITSs use a variety of computational paradigms, as production systems, Bayesian networks, schema-templates, theorem proving, and explanatory reasoning. The next generation of ITSs are expected to go one step further by adopting not only more intelligent interfaces but will focus on integration. This article will describe some particularities of a tutoring system that we are developing to simulate conversational dialogue in the area of Medicine, that enables the integration of highly heterogeneous sources of information into a coherent knowledge base, either from the tutor's point of view or the development of the discipline in itself, i.e. the system's content is created automatically by the physicians as their daily work goes on. This will encourage students to articulate lengthier answers that exhibit deep reasoning, rather than to deliver straight tips of shallow knowledge. The goal is to take advantage of the normal functioning of the health care units to build on the fly a knowledge base of cases and data for teaching and research purposes. [ABSTRACT FROM AUTHOR]

In this paper, we present an application of Artificial Intelligence to the implementation of Virtual Automobile Manufacturing Enterprise. We provide a multi autonomous agent based framework. Our agent based architecture leads to flexible design of a spectrum of virtual enterprises by distributing computation and by providing a unified interface to data and programs. Autonomous agents are intelligent enough and provide autonomy, simplicity of communication, computation, and a well developed semantics. The steps of design and implementation are discussed in depth, in particular an ontology, the agent model, and interaction pattern between agents are given. We have developed mechanisms for coordination between agents using a language, which we call Virtual Enterprise Modeling Language (VEML). VEML is a dialect of Java and includes Knowledge Query and Manipulation Language (KQML) primitives. We have implemented a multi autonomous agent based system, which we call VE System. VE System provides application programmers with potential to globally develop different kinds of VEs based on their requirements and applications. We provide case study of automobile manufacturing enterprise and demonstrate efficacy of our system by discussing its salient features. [ABSTRACT FROM AUTHOR]

Objects of interest are represented in the brain simultaneously in different frames of reference. Knowing the positions of one's head and eyes, for example, one can compute the body-centred position of an object from its perceived coordinates on the retinae. We propose a simple and fully trained attractor network which computes head-centred coordinates given eye position and a perceived retinal object position. We demonstrate this system on artificial data and then apply it within a fully neurally implemented control system which visually guides a simulated robot to a table for grasping an object. The integrated system has as input a primitive visual system with a what-where pathway which localises the target object in the visual field. The coordinate transform network considers the visually perceived object position and the camera pan-tilt angle and computes the target position in a body-centred frame of reference. This position is used by a reinforcement-trained network to dock a simulated PeopleBot robot at a table for reaching the object. Hence, neurally computing coordinate transformations by an attractor network has biological relevance and technical use for this important class of computations. [ABSTRACT FROM AUTHOR]

WISE Expert is a general-purpose system that can be used for monitoring or controlling, in real time, complex systems that have recurring sub-structures. The system has been developed using a unique schematic development tool that ensures coherency of structure during design and construction. The design of the Expert System takes advantage of a distinction between the monitored system structure and expert knowledge so that the structure description can be used to generate specific rules for the system automatically. The system has been tested as an overseer during the running of trainee mariner exercises with a liquid cargo simulator and is now operational at over 35 customer sites throughout the world. [ABSTRACT FROM AUTHOR]

Bayesian Networks (BNs) have been recently employed to solve meteorology problems. In this paper, the application of BNs for mining a real-world weather dataset is described. The employed dataset discriminates between "wet fog" instances and "other weather conditions" instances, and it contains many missing data. Therefore, BNs were employed not only for classifying instances, but also for filling missing data. In addition, the Markov Blanket concept was employed to select relevant attributes. The efficacy of BNs to perform the aforementioned tasks was assessed by means of several experiments. In summary, more convincing results were obtained by taking advantage of the fact that BNs can directly (i.e. without data preparation) classify instances containing missing values. In addition, the attributes selected by means of the Markov Blanket provide a simpler, faster, and equally accurate classifier. [ABSTRACT FROM AUTHOR]

Ripple-Down Rules (RDR) is an approach to building knowledge-based systems (KBS) incrementally, while the KBS is in routine use. Domain experts build rules as a minor extension to their normal duties, and are able to keep refining rules as KBS requirements evolve. Commercial RDR systems are now used routinely in some Chemical Pathology laboratories to provide interpretative comments to assist clinicians make the best use of laboratory reports. This paper presents usage data from one laboratory where, over a 29 month period, over 16,000 rules were added and 6,000,000 cases interpreted. The clearest evidence that this facility is highly valuable to the laboratory is the on-going addition of new knowledge bases and refinement of existing knowledge bases by the chemical pathologists. [ABSTRACT FROM AUTHOR]

This paper details the application of geometric proportional analogies in the sub-classification of polygons within a topographic (land cover) map. The first part of this paper concerns geometric proportional analogies that include attributes (e.g. fill-pattern and fill-colour). We describe an extension to the standard theory of analogy that incorporates attributes into the analogical mapping process. We identify two variants on this "attribute matching" extension, which is required to solve different types of geometric proportional analogy problems. In the second part of this paper we describe how we use the simpler of these algorithms to generate inferences in topographic maps. We detail the results of identifying a number of different structures on a sample topographic map. [ABSTRACT FROM AUTHOR]

Engineering design requires extensive decomposition and integration activities relying on a multidisciplinary basis. A design structure matrix (DSM) can be used as a representation and analysis tool in order to manage the design process under diverse perspectives. The design outcome is always subject to the abstract nature, the subjectivity and the low availability of the required design knowledge. This paper addresses the DSM as a communicating design tool among multiple designers. A fuzzy-logical inference mechanism permits the collaboration among designers on the qualitative definition of the interrelations among the design problem's entities or tasks and the resulting DSM may be then utilized for various tasks (partitioning, clustering, tearing etc.) depending on the problem under consideration. A DSM is deployed for the case of the parametric design of an oscillating conveyor where two (2) designers are collaboratively involved. [ABSTRACT FROM AUTHOR]

The aim of this study is to evaluate the effectiveness of using external indicators, such as commodity prices and currency exchange rates, in predicting movements in the Dow Jones Industrial Average index. The performance of each technique is evaluated using different domain specific metrics. A comprehensive evaluation procedure is described, involving the use of trading simulations to assess the practical value of predictive models, and comparison with simple benchmarks that respond to underlying market growth. In the experiments presented here, basing trading decisions on a neural network trained on a range of external indicators resulted in a return on investment of 23.5% per annum, during a period when the DJIA index grew by 13.03% per annum. A substantial dataset has been compiled and is available to other researchers interested in analysing financial time series. [ABSTRACT FROM AUTHOR]

SkillSum is an Artificial Intelligence (AI) and Natural Language Generation (NLG) system that produces short feedback reports for people who are taking online tests which check their basic literacy and numeracy skills. In this paper, we describe the SkillSum system and application, focusing on three challenges which we believe are important ones for many systems which try to generate feedback reports from Web-based tests: choosing content based on very limited data, generating appropriate texts for people with varied levels of literacy and knowledge, and integrating the web-based system with existing assessment and support procedures. [ABSTRACT FROM AUTHOR]

The concept of the Knowledge Bazaar as a paradigm for the development of Expert Systems, whereby knowledge bases are created dynamically using knowledge supplied by self appointed Internet communities is proposed. The idea espouses the creation of individual Knowledge Bazaars, operating in specific domains, but all operating through a generic Knowledge Bazaar XML Web application. Issues addressed include the provision of the service, XML rule representations and rule integrity. The concept is illustrated with a demonstration gardening Knowledge Bazaar that is currently operational. [ABSTRACT FROM AUTHOR]

We apply a variant of Simulated Annealing (SA) as a standard black-box optimisation algorithm to the colour quantisation problem. The main advantage of black-box optimisation algorithms is that they do not require any domain specific knowledge yet are able to provide a near optimal solution. To further improve the performance of the algorithm we combine the SA technique with a standard k-means clustering technique. We evaluate the effectiveness of our approach by comparing its performance with several specialised colour quantisation algorithms. The results obtained show that our hybrid SA algorithm clearly outperforms standard quantisation algorithms and provides images with superior image quality. [ABSTRACT FROM AUTHOR]

In this paper, we present ISOR, a Case-Based Reasoning system for long-term therapy support in the endocrine domain and in psychiatry. ISOR performs typical therapeutic tasks, such as computing initial therapies, initial dose recommendations, and dose updates. Apart from these tasks ISOR deals especially with situations where therapies become ineffective. Causes for inefficacy have to be found and better therapy recommendations should be computed. In addition to the typical Case-Based Reasoning knowledge, namely former already solved cases, ISOR uses further knowledge forms, especially medical histories of query patients themselves and prototypical cases (prototypes). Furthermore, the knowledge base consists of therapies, conflicts, instructions etc. So, retrieval does not only provide former similar cases but different forms and steps of retrieval are performed, while adaptation occurs as an interactive dialog with the user. Since therapy inefficacy can be caused by various circumstances, we propose searching for former similar cases to get ideas about probable reasons that subsequently should be carefully investigated. We show that ISOR is able to successfully support such investigations. [ABSTRACT FROM AUTHOR]

Knowledge engineers have been working in the legal domain since the rise of their discipline in the mid-eighties of the last century. Traditionally their main focus was capturing and distributing knowledge by means of the knowledge-based systems, thus improving legal access. More and more legal knowledge engineering has become an analytical approach that helps to improve legal quality. An example is the POWER-approach developed in a research programme that is now finished. This programme was run by the Dutch Tax and Customs Administration (DTCA in Dutch: Belastingdienst) and some partners (see e.g. Van Engers et al., 1999, 2000, 2001, 2003 and 2004). The POWER-approach helped to improve quality of (new) legislation and codify the knowledge used in the translation processes in which legislation and regulations are transformed into procedures, computer programs and other designs. We experienced that despite these clear benefits implementation proved to be far from easy. In fact the implementation phase still continues. Adapting research results in public administrations is a tedious process that takes lots and lots of energy and requires continuous management attention. Learning at organisational level proved to be much harder than we thought. [ABSTRACT FROM AUTHOR]

This paper presents the speech recognition accuracy testing and usability evaluation of a mixed-initiative dialogue multimodal interface for the ATTAIN* travel information system [1]. Experimental results show that although the speech recognition accuracy of the interface is less (sample accuracy rate 74.5%) than that of an equivalent directed-dialogue interface (sample accuracy rate 88.5%), the usability is seen to be significantly improved in terms of effectiveness, efficiency, learnability and user satisfaction. [ABSTRACT FROM AUTHOR]

The development of automated methods for chemical synthesis as well as for chemical analysis has inundated chemistry with huge amounts of experimental data. To refine them into information, the field of chemoinformatics applies techniques from artificial intelligence, pattern recognition and machine learning. A key task concerning organic chemistry is structure elucidation. NMR spectra have become accessible at low expenses of time and sample size, they also are predictable with good precision, and they are directly related to structural properties of the molecule. So the classical approach of ranking structure candidates by comparison of NMR spectra works well, but since the structural space is huge, more sophisticated approaches are in demand. Bayesian networks are promising in this concern, as they allow for contemplation in a dual way: provided an appropriate model, conclusions can be drawn from a given spectrum regarding the corresponding structure or vice versa, since the same interrelations hold in both directions. The development of such a model is documented, and first results are shown supporting the applicability of Bayesian networks to structure elucidation. [ABSTRACT FROM AUTHOR]

Within incident management an important aspect is the analysis of log files describing traces of incident management processes and the errors made in them. Automated support of such an analysis can be helpful. In this paper some results are shown on automated support for analysis of errors in traces of incident management. For such traces it can be checked automatically which dynamic properties describing good functioning hold and which fail. The potential of the approach is shown in the formal analysis of a given empirical trace. The approach can also be applied in conjunction with simulation experiments. [ABSTRACT FROM AUTHOR]

Case Based Reasoning (CBR) systems solve new problems by reusing solutions of similar past problems. For knowledge intensive tasks such as design it is not sufficient to merely retrieve and present similar past experiences. This is because the user requires an explanation of the solution in order to judge its validity and identify any deficiencies. Case retrieval with k-nearest neighbour relies heavily on the availability of cases, knowledge about important problem features and the similarity metric. However, much of this information, utilised by the system, is not transparent to the user. Consequently there is a need for tools that can help instil confidence in the system by providing useful explanations to the user. This paper proposes an approach that explains the CBR retrieval process by visualising implicit system design knowledge. This is achieved by visualising the immediate neighbour hood and by highlighting features that contribute to similarity and to differences. The approach is demonstrated on a pharmaceutical tablet formulation problem with a tool called FormuCaseViz. An expert evaluation provides evidence to support our approach. [ABSTRACT FROM AUTHOR]

Typically, complex engineering artifacts are designed by teams who may not all be located in the same building or even city. Additionally, besides having to design a part of an artifact to be consistent with the specification, it must also be consistent with the company's design standards. The Designers' Workbench supports designers by checking that their configurations satisfy both physical and organisational constraints. The system uses an ontology to describe the available elements in a configuration task. Configurations are composed of features, which can be geometric or nongeometric, physical or abstract. Designers can select a class of feature (e.g. Bolt) from the ontology, and add an instance of that class (e.g. a particular bolt) to their configuration. Properties of the instance can express the parameters of the feature (e.g. the size of the bolt), and also describe connections to other features (e.g. what parts the bolt is used to hold together). [ABSTRACT FROM AUTHOR]