<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.

Video has been used in educational settings for several years by means of videotapes. However, the use of digital video is not very common in schools and training institutions. Moreover, the literature reports not much about using online video in teacher training purposes. This current study presents an online video-based pre-service teacher training environment, offering online video cases for teacher training purposes. The first purpose is to develop an online video-based training system for teacher training. The online teacher training system includes online video cases on various topics in teacher training. The second aim of this study is to understand what student teachers think about the video cases in the online training environment. The first phase of the study was implemented as a pilot study, and the main study included two cycles of action research. The subjects of the study were students of the Faculty of Education at Middle East Technical University who were enrolled to the CEIT321 - Foundations of Distance Education course. The evaluation of video based training questionnaire was administered to the participants and individual interviews were conducted with a selected group of students according to questionnaire scores. The results of the questionnaire proposed that the students had positive attitudes for the online video cases included in the study. In the interviews, the participants indicated their comments and proposed some improvements regarding the technical aspects and the content of video cases. Participants&#039 / reflections about the video cases and course web server logs also provided parallel findings supporting the interviews and the questionnaire.

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.

In this paper we examine models that incorporate CBDT. In the first chapter, we will examine CBDT more thoroughly including a reinterpretation of the standard labor supply problem under a wage tax in a partial equilibrium model where preferences exhibit characteristics of CBDT. In the second chapter, we extend the labor supply decision under a wage tax by incorporating a household production function. Utility maximization by repeated substitution is applied as a novel approach to solving dynamic optimization problems. This approach allows us to find labor supply elasticities that evolve over the life cycle. In the third chapter, CBDT will be explored in more depth focusing on its applicability in representing people's preferences over movie rentals in the Netflix competition. This chapter builds on the theoretical model introduced in chapter 1, among other things, expressing the rating of any customer movie pair using the ratings of similar movies that the customer rated and the ratings of the movie in question by similar customers. We will also explore in detail the econometric model used in the Netflix competition which utilizes machine learning and spatial regression to estimate customer's preferences.

Data preprocessing is critical for machine learning, data mining, and pattern recognition. In particular, selecting relevant and non-redundant features in highdimensional data is important to efficiently construct models that accurately describe the data. In this work, I present SLIDER, an algorithm that weights features to reflect relevance in determining similarity between instances. Accurate weighting of features improves the similarity measure, which is useful in learning algorithms like nearest neighbor and case-based reasoning. SLIDER performs a greedy search for optimum weights in an exponentially large space of weight vectors. Exhaustive search being intractable, the algorithm reduces the search space by focusing on pivotal weights at which representative instances are equidistant to truly similar and different instances in Euclidean space. SLIDER then evaluates those weights heuristically, based on effectiveness in properly ranking pre-determined matches of a set of cases, relative to mismatches. I analytically show that by choosing feature weights that minimize the mean rank of matches relative to mismatches, the separation between the distributions of Euclidean distances for matches and mismatches is increased. This leads to a better distance metric, and consequently increases the probability of retrieving true matches from a database. I also discuss how SLIDER is used to improve the efficiency and effectiveness of case retrieval in a case-based reasoning system that automatically interprets electron density maps to determine the three-dimensional structures of proteins. Electron density patterns for regions in a protein are represented by numerical features, which are used in a distance metric to efficiently retrieve matching patterns by searching a large database. These pre-selected cases are then evaluated by more expensive methods to identify truly good matches – this strategy speeds up the retrieval of matching density regions, thereby enabling fast and accurate protein model-building. This two-phase case retrieval approach is potentially useful in many case-based reasoning systems, especially those with computationally expensive case matching and large case libraries.

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.

Project cost increase is the main concern in international construction projects which usually results in disputes and conflicts among the project participants. The aim of this thesis is to construct a database that represents risk event history regarding international construction projects and construct a cost overrun prediction model. It is hypothesized that magnitudes of project related, company related and country related risk factors can be predicted by assessing the level of vulnerability by analogical reasoning with previous projects. The vulnerability and risk factors can further be used to predict cost overrun in the bid preparation stage of international construction projects. Thus, prediction models that link vulnerability with risk factors and cost are constructed by using a dataset of 166 international construction projects, which consists of 66 real and 100 hypothetical cases. Case-based reasoning (CBR) technique is used to construct the prediction models. After testing the performance of various CBR models using different weight generation and retrieval methods, error rate of +/- 7.15 % cost increase is achieved. The utilization of CBR models in the prediction of potential risk sources and cost overrun is demonstrated by a real case study. Finally, the benefits and pitfalls of using analogical reasoning for risk and cost overrun assessment of construction projects are discussed.

The main purpose of the present study was to investigate the effect of case-based instruction based on conceptual change conditions to overcome 10th grade students&rsquo / misconceptions related to gas concepts. Moreover, the effect of this method on students&rsquo / attitudes toward chemistry and their perceived motivation was explored. The study was carried out during the 2008-2009 academic year at both a state high school and an Anatolian high school. A total of 128 tenth grade students were the participants of the study. One of the classes of the same chemistry teacher was randomly assigned as experimental group and one of them was control group in each school. Students in experimental groups were instructed by case-based learning based on conceptual change conditions while the control group students received traditionally designed chemistry instruction. Gas Concept Test, Attitude toward Chemistry and Motivated Strategies for Learning Questionnaire were administered to both groups of students as pretest and posttest to determine the students&rsquo / misconceptions and their understanding of gas concepts, their attitude toward chemistry and perceived motivation. Two two-way ANOVA based on gain scores were used to assess the effect of case-based learning on students&rsquo / understanding of gas concepts and attitudes toward chemistry. In addition, the effect of case-based learning on students&rsquo / perceived motivation was investigated by MANOVA based on gain scores. The results revealed that case-based learning was an effective method for overcoming students&rsquo / misconceptions about the gas concepts and promoting their attitudes and motivation towards chemistry in Anatolian high school.