Diagrams are increasingly used in many design methods, and are being taught in a variety of contexts in higher education such as database conceptual design or software design in computer science. They are an important part of many assessments. Currently computer aided assessments are widely used for multiple choice questions. They lack the ability to assess a student's knowledge in a more comprehensive way, which is required for diagram-type student work. The aim of this research is to develop a semi-automatic assessment framework, which enables the use of computer to support the assessment process of diagrammatic solutions, with the focus of ensuring the consistency of grades and feedback on solutions. A novel trace model, that captures design traces of student solutions, was developed as a part of the framework and was used to provide the matching criteria for grouping the solutions. A new marking style, partial marking, was developed to mark these solution groups manually. The Case-Based Reasoning method is utilised in the framework to mark some of the groups automatically. A guideline for scenario writing was proposed to increase the efficiency of automatic marking. A prototype diagram editor, a marking tool and scenario writing environment were implemented for the proposed framework in order to demonstrate proof of concept. The results of experiments show that the framework is feasible to use in the formative assessment and it provides consistent marking and personalised feedback to the students. The framework also has the potential to significantly reduce the time and effort required by the examiner to mark student diagrams. Although the constructed framework was specifically used for the assessment of database diagrams, the framework is generic enough to be used for other types of graph-based diagram.

Cette thèse propose deux approches pour améliorer la qualité des réponses d'un système de raisonnement à partir de cas (RàPC) utilisant des connaissances produites par une communauté en ligne. La première approche concerne la mise en œuvre d'un modèle permettant de gérer la fiabilité des connaissances produites par la communauté sous la forme d'un score. Ce score de fiabilité est utilisé d'une part pour filtrer les connaissances non fiables afin qu'elles ne soient pas utilisées par le système de RàPC et d'autre part pour classer les réponses retournées par le système. La deuxième approche concerne la représentation de la typicalité entre sous-classes et classes dans une organisation hiérarchique. La typicalité est alors utilisée pour réorganiser les connaissances hiérarchiques utilisées par le système de RàPC. L'apport de ces deux approches a été évalué dans le cadre de eTaaable, un système de RàPC qui adapte des recettes de cuisine en utilisant des connaissances produites par une communauté en ligne. L'évaluation montre que la gestion de la fiabilité des connaissances produites par la communauté améliore la qualité des réponses retournées par eTaaable. De même, l'évaluation montre que l'utilisation par eTaaable des hiérarchies des connaissances réorganisées en exploitant la typicalité améliore également la qualité des réponses / This research work presents two approaches to improve the quality of the results returned by a case-based reasoning system (CBR) exploiting knowledge produced by an e-community. The first approach relies on a new model to manage the trustworthiness of the knowledge produced by the e community. In this model, the trustworthiness is represented through a score which is used to filter untrustworthy knowledge so that the CBR system will not use it anymore. Moreover, the trustworthiness score is also used to rank the CBR results. The second approach addresses the issue of representing the typicality between subclasses and classes in a hierarchy. The typicality is used to change the hierarchical organization used by the CBR system. Both approaches have been evaluated in the framework of eTaaable, a CBR system which adapts cooking recipes using knowledge coming from an e-community. The evaluations show that managing the trustworthiness of the knowledge produced by an e-community improves the quality of the results returned by eTaaable. The evaluations also shows that eTaaable returns also better results when using knowledge reorganized according to typicality.

<p>Increased stress is a continuing problem in our present world. Especiallynegative stress could cause serious health problems if it remainsundiagnosed/misdiagnosed and untreated. In the stress medicine, clinicians’measure blood pressure, ECG, finger temperature and breathing rate during anumber of exercises to diagnose stress-related disorders. One of the physiologicalparameters for quantifying stress levels is the finger temperature that helps theclinicians in diagnosis and treatment of stress. However, in practice, it is difficultand tedious for a clinician to understand, interpret and analyze complex, lengthysequential sensor signals. There are only few experts who are able to diagnose andpredict stress-related problems. A system that can help the clinician in diagnosingstress is important, but the large individual variations make it difficult to build sucha system.This research work has attempted to investigate several artificial Intelligencetechniques to develop an intelligent, integrated sensor system for diagnosis andtreatment plan in the Psychophysiological domain. To diagnose individualsensitivity to stress, case-based reasoning is applied as a core technique to facilitateexperience reuse by retrieving previous similar cases. Further, fuzzy techniques arealso employed and incorporated into the case-based reasoning system to handlevagueness, uncertainty inherently existing in clinicians reasoning process. Thevalidation of the approach is based on close collaboration with experts andmeasurements from twenty four persons used as reference.Thirty nine time series from these 24 persons have been used to evaluate theapproach (in terms of the matching algorithms) and an expert has ranked andestimated similarity which shows a level of performance close to an expert. Theproposed system could be used as an expert for a less experienced clinician or as asecond option for an experienced clinician to their decision making process.</p> / Integrated Personal Health Optimizing System (IPOS)

<p>The history of oil well engineering applications has revealed that the frequent operational problems are still common in oil well practice. Well blowouts, stuck pipes, well leakages are examples of the repeated problems in the oil well engineering industry. The main reason why these unwanted problems are unavoidable can be the complexity and uncertainties of the oil well processes. Unforeseen problems happen again and again, because they are not fully predictable, which could be due to lack of sufficient data or improper modelling to simulate the real conditions in the process. Traditional mathematical models have not been able to totally eliminate unwanted oil well problems because of the many involved simplifications, uncertainties, and incomplete information. This research work proposes a new approach and breakthrough for overcoming these challenges. The main objective of this study is merging two scientific fields; artificial intelligence and petroleum engineering in order to implement a new methodology.</p><p>Case-Based Reasoning (CBR) and Model-Based Reasoning (MBR), two branches of the artificial intelligence science, are applied for solving complex oil well problems. There are many CBR and MBR modelling tools which are generally used for different applications for implementing and demonstrating CBR and MBR methodologies; however, in this study, the Creek system which combines CBR and MBR has been utilized as a framework. One specific challenging task related to oil well engineering has been selected to exemplify and examine the methodology. To select a correct candidate for this application was a challenging step by itself. After testing many different issues in the oil well engineering, a well integrity issue has been chosen for the context. Thus, 18 leaking wells, production and injection wells, from three different oil fields have been analysed in depth. Then, they have been encoded and stored as cases in an ontology model given the name Wellogy.</p><p>The challenges related to well integrity issues are a growing concern. Many oil wells have been reported with annulus gas leaks (called internal leaks) on the Norwegian Continental Shelf (NCS) area. Interventions to repair the leaking wells or closing and abandoning wells have led to: high operating cost, low overall oil recovery, and in some cases unsafe operation. The reasons why leakages occur can be different, and finding the causes is a very complex task. For gas lift and gas injection wells the integrity of the well is often compromised. As the pressure of the hydrocarbon reserves decreases, particularly in mature fields, the need for boosting increases. Gas is injected into the well either to lift the oil in the production well or to maintain pressure in the reservoir from the injection well. The challenge is that this gas can lead to breakdown of the well integrity and cause leakages. However, as there are many types of leakages that can occur and due to their complexity it can be hard to find the cause or causal relationships. For this purpose, a new methodology, the Creek tool, which combines CBR and MBR is applied to investigate the reasons for the leakages. Creek is basically a CBR system, but it also includes MBR methods.</p><p>In addition to the well integrity cases, two complex cases (knowledge-rich cases) within oil well engineering have also been studied and analysed through the research work which is part of the PhD. The goal here is to show how the knowledge stored in two cases can be extracted for the CBR application.</p><p>A model comprising general knowledge (well-known rules and theories) and specific knowledge (stored in cases) has been developed. The results of the Wellogy model show that the CBR methodology can automate reasoning in addition to human reasoning through solving complex and repeated oil well problems. Moreover, the methodology showed that the valuable knowledge gained through the solved cases can be sustained and whenever it is needed, it can be retrieved and reused. The model has been verified for unsolved cases by evaluating case-matching results. The model gives elaborated explanations of the unsolved cases through the building of causal relationships. The model also facilitates knowledge acquisition and learning curves through its growing case base.</p><p>The study showed that building a CBR model is a rather time-consuming process due to four reasons:</p><p>1. Finding appropriate cases for the CBR application is not straightforward</p><p>2. Challenges related to constructing cases when transforming reported information to symbolic entities</p><p>3. Lack of defined criteria for amount of information (number of findings) for cases</p><p>4. Incomplete data and information to fully describe problems of the cases at the knowledge level</p><p>In this study only 12 solved cases (knowledge-rich cases) have been built in the Wellogy model. More cases (typically hundreds for knowledge-lean cases and around 50 for knowledge-rich cases) would be required to have a robust and efficient CBR model. As the CBR methodology is a new approach for solving complex oil well problems (research and development phase), additional research work is necessary for both areas, i.e. developing CBR frameworks (user interfaces) and building CBR models (core of CBR). Feasibility studies should be performed for implemented CBR models in order to use them in real oil field operations. So far, the existing Wellogy model has showed some benefits in terms of; representing the knowledge of leaking well cases in the form of an ontology, retrieving solved cases, and reusing pervious cases.</p>

<p>As long as there have been computers, one goal has been to be able to communicate with them using natural language. It has turned out to be very hard to implement a dialog system that performs as well as a human being in an unrestricted domain, hence most dialog systems today work in small, restricted domains where the permitted dialog is fully controlled by the system.</p><p>In this thesis we present two dialog systems for communicating with an autonomous agent:</p><p>The first system, the WITAS RDE, focuses on constructing a simple and failsafe dialog system including a graphical user interface with multimodality features, a dialog manager, a simulator, and development infrastructures that provides the services that are needed for the development, demonstration, and validation of the dialog system. The system has been tested during an actual flight connected to an unmanned aerial vehicle.</p><p>The second system, CEDERIC, is a successor of the dialog manager in the WITAS RDE. It is equipped with a built-in machine learning algorithm to be able to learn new phrases and dialogs over time using past experiences, hence the dialog is not necessarily fully controlled by the system. It also includes a discourse model to be able to keep track of the dialog history and topics, to resolve references and maintain subdialogs. CEDERIC has been evaluated through simulation tests and user tests with good results.</p> / Report code: LiU{Tek{Lic{2006:29.

<p>This work focuses on the socio-technical aspects of artificial intelligence, namely how (specific types of) intelligent systems function in human workplace environments. The goal is first to get a better understanding of human needs and expectations when it comes to interaction with intelligent systems, and then to make use of the understanding gained in the process of designing and implementing such systems.</p><p>The work presented focusses on a specific problem in developing intelligent systems, namely how the artefacts to be developed can fit smoothly into existing socio-cultural settings. To achieve this, we make use of theories from the fields of organisational psychology, sociology, and linguistics. This is in line with approaches commonly found in AI. However, most of the existing work deals with individual aspects, like how to mimic the behaviour or emulate methods of reasoning found in humans, whereas our work centers around the social aspect. Therefore, we base our work on theories that have not yet gained much attention in intelligent systems design. To be able to make them fruitful for intelligent systems research and development, we have to adapt them to the specific settings, and we have to transform them to suit the practical problems at hand.</p><p>The specific theoretical frameworks we draw on are first and foremost activity theory and to a lesser degree semiotics. Activity theory builds on the works of Leont'ev. It is a descriptive tool to help understand the unity of consciousness and activity. Its focus lies on individual and collective work practise. One of its strengths, and the primary reason for its value in AI development, is the ability to identify the role of material artefacts in the work process. Halliday's systemic functional theory of language (SFL) is a social semiotic theory that sets out from the assumption that humans are social beings that are inclined to interact and that this interaction is inherently multimodal. We interact not just with each other, but with our own constructions and with our natural world. These are all different forms of interaction, but they are all sign processes.</p><p>Due to the obvious time and spatial constraints, we cannot address all of the challenges that we face when building intelligent artefacts. In reducing the scope of the thesis, we have focused on the problem of explanation, and here in particular the problem of explanation from a user perspective. By putting social theories to work in the field of artificial intelligence, we show that results from other fields can be beneficial in understanding what explanatory capabilities are needed for a given intelligent system, and to ascertain in which situations an explanation should be delivered. Besides lessons learned in knowledge based system development, the most important input comes from activity theory.</p><p>The second focus is the challenge of contextualisation. Here we show that work in other scientific fields can be put to use in the development of context aware or ambient intelligent systems. Again, we draw on results from activity theory and combine this with insights from semiotics.</p><p>Explanations are themselves contextual, so the third challenge is to explore the space spanned by the two dimensions ability to explain and contextualisation. Again, activity theory is beneficial in resolving this issue.</p><p>The different theoretical considerations have also led to some practical approaches. Working with activity theory helps to better understand what the relevant contextual aspects of a given application are and helps to develop models of context which are both grounded in the tradition of context aware systems design and are plausible from a cognitive point of view.</p><p>Insights from an analysis of research in the knowledge based system area and activity theory have further lead to the amendment of a toolbox for requirements engineering, so called problem frames. New problem frames that target explanation aware ambient intelligent systems are presented. This is supplemented with work looking at the design of an actual system after the requirements have been elicited and specified. Thus, the socio-technical perspective on explanations is coupled with work that addresses knowledge representation issues, namely how to model sufficient knowledge to be able to deliver explanations.</p>

The history of oil well engineering applications has revealed that the frequent operational problems are still common in oil well practice. Well blowouts, stuck pipes, well leakages are examples of the repeated problems in the oil well engineering industry. The main reason why these unwanted problems are unavoidable can be the complexity and uncertainties of the oil well processes. Unforeseen problems happen again and again, because they are not fully predictable, which could be due to lack of sufficient data or improper modelling to simulate the real conditions in the process. Traditional mathematical models have not been able to totally eliminate unwanted oil well problems because of the many involved simplifications, uncertainties, and incomplete information. This research work proposes a new approach and breakthrough for overcoming these challenges. The main objective of this study is merging two scientific fields; artificial intelligence and petroleum engineering in order to implement a new methodology. Case-Based Reasoning (CBR) and Model-Based Reasoning (MBR), two branches of the artificial intelligence science, are applied for solving complex oil well problems. There are many CBR and MBR modelling tools which are generally used for different applications for implementing and demonstrating CBR and MBR methodologies; however, in this study, the Creek system which combines CBR and MBR has been utilized as a framework. One specific challenging task related to oil well engineering has been selected to exemplify and examine the methodology. To select a correct candidate for this application was a challenging step by itself. After testing many different issues in the oil well engineering, a well integrity issue has been chosen for the context. Thus, 18 leaking wells, production and injection wells, from three different oil fields have been analysed in depth. Then, they have been encoded and stored as cases in an ontology model given the name Wellogy. The challenges related to well integrity issues are a growing concern. Many oil wells have been reported with annulus gas leaks (called internal leaks) on the Norwegian Continental Shelf (NCS) area. Interventions to repair the leaking wells or closing and abandoning wells have led to: high operating cost, low overall oil recovery, and in some cases unsafe operation. The reasons why leakages occur can be different, and finding the causes is a very complex task. For gas lift and gas injection wells the integrity of the well is often compromised. As the pressure of the hydrocarbon reserves decreases, particularly in mature fields, the need for boosting increases. Gas is injected into the well either to lift the oil in the production well or to maintain pressure in the reservoir from the injection well. The challenge is that this gas can lead to breakdown of the well integrity and cause leakages. However, as there are many types of leakages that can occur and due to their complexity it can be hard to find the cause or causal relationships. For this purpose, a new methodology, the Creek tool, which combines CBR and MBR is applied to investigate the reasons for the leakages. Creek is basically a CBR system, but it also includes MBR methods. In addition to the well integrity cases, two complex cases (knowledge-rich cases) within oil well engineering have also been studied and analysed through the research work which is part of the PhD. The goal here is to show how the knowledge stored in two cases can be extracted for the CBR application. A model comprising general knowledge (well-known rules and theories) and specific knowledge (stored in cases) has been developed. The results of the Wellogy model show that the CBR methodology can automate reasoning in addition to human reasoning through solving complex and repeated oil well problems. Moreover, the methodology showed that the valuable knowledge gained through the solved cases can be sustained and whenever it is needed, it can be retrieved and reused. The model has been verified for unsolved cases by evaluating case-matching results. The model gives elaborated explanations of the unsolved cases through the building of causal relationships. The model also facilitates knowledge acquisition and learning curves through its growing case base. The study showed that building a CBR model is a rather time-consuming process due to four reasons: 1. Finding appropriate cases for the CBR application is not straightforward 2. Challenges related to constructing cases when transforming reported information to symbolic entities 3. Lack of defined criteria for amount of information (number of findings) for cases 4. Incomplete data and information to fully describe problems of the cases at the knowledge level In this study only 12 solved cases (knowledge-rich cases) have been built in the Wellogy model. More cases (typically hundreds for knowledge-lean cases and around 50 for knowledge-rich cases) would be required to have a robust and efficient CBR model. As the CBR methodology is a new approach for solving complex oil well problems (research and development phase), additional research work is necessary for both areas, i.e. developing CBR frameworks (user interfaces) and building CBR models (core of CBR). Feasibility studies should be performed for implemented CBR models in order to use them in real oil field operations. So far, the existing Wellogy model has showed some benefits in terms of; representing the knowledge of leaking well cases in the form of an ontology, retrieving solved cases, and reusing pervious cases.

This work focuses on the socio-technical aspects of artificial intelligence, namely how (specific types of) intelligent systems function in human workplace environments. The goal is first to get a better understanding of human needs and expectations when it comes to interaction with intelligent systems, and then to make use of the understanding gained in the process of designing and implementing such systems. The work presented focusses on a specific problem in developing intelligent systems, namely how the artefacts to be developed can fit smoothly into existing socio-cultural settings. To achieve this, we make use of theories from the fields of organisational psychology, sociology, and linguistics. This is in line with approaches commonly found in AI. However, most of the existing work deals with individual aspects, like how to mimic the behaviour or emulate methods of reasoning found in humans, whereas our work centers around the social aspect. Therefore, we base our work on theories that have not yet gained much attention in intelligent systems design. To be able to make them fruitful for intelligent systems research and development, we have to adapt them to the specific settings, and we have to transform them to suit the practical problems at hand. The specific theoretical frameworks we draw on are first and foremost activity theory and to a lesser degree semiotics. Activity theory builds on the works of Leont'ev. It is a descriptive tool to help understand the unity of consciousness and activity. Its focus lies on individual and collective work practise. One of its strengths, and the primary reason for its value in AI development, is the ability to identify the role of material artefacts in the work process. Halliday's systemic functional theory of language (SFL) is a social semiotic theory that sets out from the assumption that humans are social beings that are inclined to interact and that this interaction is inherently multimodal. We interact not just with each other, but with our own constructions and with our natural world. These are all different forms of interaction, but they are all sign processes. Due to the obvious time and spatial constraints, we cannot address all of the challenges that we face when building intelligent artefacts. In reducing the scope of the thesis, we have focused on the problem of explanation, and here in particular the problem of explanation from a user perspective. By putting social theories to work in the field of artificial intelligence, we show that results from other fields can be beneficial in understanding what explanatory capabilities are needed for a given intelligent system, and to ascertain in which situations an explanation should be delivered. Besides lessons learned in knowledge based system development, the most important input comes from activity theory. The second focus is the challenge of contextualisation. Here we show that work in other scientific fields can be put to use in the development of context aware or ambient intelligent systems. Again, we draw on results from activity theory and combine this with insights from semiotics. Explanations are themselves contextual, so the third challenge is to explore the space spanned by the two dimensions ability to explain and contextualisation. Again, activity theory is beneficial in resolving this issue. The different theoretical considerations have also led to some practical approaches. Working with activity theory helps to better understand what the relevant contextual aspects of a given application are and helps to develop models of context which are both grounded in the tradition of context aware systems design and are plausible from a cognitive point of view. Insights from an analysis of research in the knowledge based system area and activity theory have further lead to the amendment of a toolbox for requirements engineering, so called problem frames. New problem frames that target explanation aware ambient intelligent systems are presented. This is supplemented with work looking at the design of an actual system after the requirements have been elicited and specified. Thus, the socio-technical perspective on explanations is coupled with work that addresses knowledge representation issues, namely how to model sufficient knowledge to be able to deliver explanations.

Now a days where technological advancements are growing at a rapid pace, it has become a common norm for all the manufacturing companies to be abreast with these advancements for being competitive in market. This thesis deals with development of one such common norm for one of the products (Roof rack component) for company Thule. The main aim of the thesis is to curtail the products lead time to market and this was achieved by using an artificial intelligence technique i.e., Case-based reasoning (CBR). Roof rack component which is mounted on car roof is mainly constituted by two parts foot pad and bracket, this thesis main interest was concerned with only brackets and its geometry. This thesis is based on contemplating the already implemented concepts in this context, designer requirements and exploring better solutions. The methods of implementation adopted here was using CBR concept which is based on indexing , retrieve, adapt, review, retain and employing these concepts in form of an algorithm. The concept for developing the algorithm was based on Iterative closest point (ICP) approach which emphasise on assigning lower weight to pairs with greater point to point distance. The results portrayed are with respect to geometry and also with respect to application interface developed, which both together provides us a better solution.

Peter Drucker said that ¡§knowledge is the most valuable property in the enterprise¡¨. While global economy model shifting to knowledge-based economy, it turns knowledge into the most important resource and strategy in an organization. Enterprises nowadays can not only transfer the invisible knowledge from employees to visible with the implementation of knowledge management, but also create higher values for the enterprises through the transferring, sharing, expanding, and value-adding of knowledge. This research is based on knowledge management, and its impacts and effects to enterprises. With reforming and remodeling process of a management consulting firm in the knowledge intensive industry, we explore its knowledge management strategies and its advances in the flows and architectures of knowledge management. Upon following the steps of knowledge definition, collection and filtering, the case-base is built with consulting and case practices. Based on the case-base, case-base reasoning (CBR) method is used to improve the diagnostic effects of the management consulting company. This knowledge management model could be expended to the other flows of consulting operations, in order to progress the overall consulting operation effects.