As part of an effort to develop an intelligent tutoring system, a set of knowledge representation frameworks was proposed to represent expert domain knowledge. A general representation of time points and temporal relations was developed to facilitate temporal concept deductions as well as facilitating explanation capabilities vital in an intelligent advisor system. Conventional representations of time use a single-referenced timeline and assigns a single unique value to the time of occurrence of an event. They fail to capture the notion of events, such as changes in signal states in microcomputer systems, which do not occur at precise points in time, but rather over a range of time with some probability distribution. Time is, fundamentally, a relative quantity. In conventional representations, this relative relation is implicitly defined with a fixed reference, "time-zero", on the timeline. This definition is insufficient if an explanation of the temporal relations is to be constructed. The proposed representation of time solves these two problems by representing a time point as a time-range and making the reference point explicit. An architecture of the system was also proposed to provide a means of integrating various modules as the system evolves, as well as a modular development approach. A production rule EXPERT based on the rule framework used in the Graphic Interactive LISP tutor (GIL) [44, 45], an intelligent tutor for LISP programming, was implemented to demonstrate the inference process using this time point representation. The EXPERT is goal-driven and is intended to be an integral part of a complete intelligent tutoring system. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate

Online product reviews have become an important and influential source of information for consumers. Firms often manipulate online product reviews to influence consumer perceptions about the product, making it a research topic of urgent need for theory development and empirical investigation. In this thesis, we examine how consumers perceive and respond to the three commonly used manipulation tactics. Firstly, an exploratory pre-study via in-depth interviews with online shoppers indicates that consumers commonly have the knowledge for online review manipulations as well as for detecting them. In the first study, a survey was used to investigate the three popular manipulation tactics in terms of ethicality and deceptiveness. They rated hiding/deleting unfavorable messages as the most deceptive and unethical, followed by anonymously adding positive messages, and then offering incentives for posting favorable messages. In study 2, in a simulated field experiment, we introduce persuasion knowledge to further examine the negative influence of review manipulations on consumers’ attitudes. The results suggest that review manipulation increases suspicion of manipulations but can hardly reduce purchase intention of focal products. We also find that consumers’ persuasion knowledge enhances suspicion of manipulation, but lessens the negative impact of suspicion on purchase intention. The third study uses secondary data of a branded e-retailer and its third party website to cross-validate the effect of manipulations on product sales. The results confirm our hypotheses that review manipulation are effective in promoting sales; however, this influence would decrease over time. This research contributes to the online marketing literature by augmenting the Information Manipulation Theory and Persuasion Knowledge Model to examine the deceptive persuasion in the online context and its impact on consumer behavior. Furthermore, we also contribute to the literature of online WOM by empirically examining the influence of review manipulations on sales. Our findings provide valuable insights to practitioners and policy makers on the pitfalls of online manipulation activities and the need to ensure the healthy development of e-commerce.

An important goal for machine learning is to transfer knowledge between tasks. For example, learning to play RoboCup Keepaway should contribute to learning the full game of RoboCup soccer. Often approaches to task transfer focus on transforming the original representation to fit the new task. Such representational transformations are necessary because the target task often requires new state information that was not included in the original representation. In RoboCup Keepaway, changing from the 3 vs. 2 variant of the task to 4 vs. 3 adds state information for each of the new players. In contrast, this dissertation explores the idea that transfer is most effective if the representation is designed to be the same even across different tasks. To this end, (1) the bird’s eye view (BEV) representation is introduced, which can represent different tasks on the same two-dimensional map. Because the BEV represents state information associated with positions instead of objects, it can be scaled to more objects without manipulation. In this way, both the 3 vs. 2 and 4 vs. 3 Keepaway tasks can be represented on the same BEV, which is (2) demonstrated in this dissertation. Yet a challenge for such representation is that a raw two-dimensional map is highdimensional and unstructured. This dissertation demonstrates how this problem is addressed naturally by the Hypercube-based NeuroEvolution of Augmenting Topologies (HyperNEAT) approach. HyperNEAT evolves an indirect encoding, which compresses the representation by exploiting its geometry. The dissertation then explores further exploiting the power of such encoding, beginning by (3) enhancing the configuration of the BEV with a focus on iii modularity. The need for further nonlinearity is then (4) investigated through the addition of hidden nodes. Furthermore, (5) the size of the BEV can be manipulated because it is indirectly encoded. Thus the resolution of the BEV, which is dictated by its size, is increased in precision and culminates in a HyperNEAT extension that is expressed at effectively infinite resolution. Additionally, scaling to higher resolutions through gradually increasing the size of the BEV is explored. Finally, (6) the ambitious problem of scaling from the Keepaway task to the Half-field Offense task is investigated with the BEV. Overall, this dissertation demonstrates that advanced representations in conjunction with indirect encoding can contribute to scaling learning techniques to more challenging tasks, such as the Half-field Offense RoboCup soccer domain.

Within corporate governance research, boards of directors constitute an essential part and are described as "the apex of the internal control system" (Jensen, 1993, p.862). Several stands of research have investigated whether, and to which degree, boards’ composition, structure and processes have impact on board task performance, but board processes and specifically the use of knowledge and skills have not been thoroughly researched, yet. Simultaneously, there is a gap within organisational behaviour research on how knowledge is explored, transformed and exploited, which is conceptualised as absorptive capacity. Further, the concept of absorptive capacity has so to date not been researched in a board context. In this thesis board processes are studied by exploring the impact of absorptive capacity on board task performance. Three dimensions of absorptive capacity, exploratory learning, transformative learning and exploitative learning, are used in the analyses. The research is conducted using mixed methods (based on a survey and a case study). A quantitative analysis is based on the Norwegian Value Creating Board Survey, and a case study is conducted based on records, observations from board meetings and interviews in the Norwegian health company Healthy. The findings show that the three dimensions of absorptive capacity, positively and significantly, mediate the relation between presence of knowledge and skills and board task performance. Complementarities between the three learning processes exist with the result that the three learning processes together are a stronger mediator than a single process. The qualitative findings show that 1) information flows have an impact on absorptive capacity, 2) that the role and power of the CEO and the division of labour between the CEO and the chair, might have an impact on board task performance and 3) that a comprehensive utilisation of consensus has an impact on transformative and exploitative learning, 4) that effort norms are positively correlated to use of knowledge and skills and 5) that activation triggers have impacts on the learning processes. The research contributes to theory with an extended application of the concept of absorptive capacity to boards, responding to calls from researchers to conduct new and more extensive research to analyse and integrate the concept. The thesis further contributes by shedding new light on learning processes in boards, underpinning former conceptual models. In the case study several findings are reported which are presented in an extended and modified model of determinants of board tasks. Finally, this thesis contributes to mixed methods research in boards. The findings have implications for board practice with regard to board selections, board evaluations and learning processes in boards. Corporate governance codes should be aligned with these findings.

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Semantic interoperability is essential on the Semantic Web to enable different information systems to exchange data. Ontology matching has been recognised as a means to achieve semantic interoperability on the Web by identifying similar information in heterogeneous ontologies. Existing ontology matching approaches have two major limitations. The first limitation relates to similarity metrics, which provide a pessimistic value when considering complex objects such as strings and conceptual entities. The second limitation relates to the role of description logic reasoning. In particular, most approaches disregard implicit information about entities as a source of background knowledge. In this thesis, we first present a new similarity function, called the degree of commonality coefficient, to compute the overlap between two sets based on the similarity between their elements. The results of our evaluations show that the degree of commonality performs better than traditional set similarity metrics in the ontology matching task. Secondly, we have developed the Knowledge Organisation System Implicit Mapping (KOSIMap) framework, which differs from existing approaches by using description logic reasoning (i) to extract implicit information as background knowledge for every entity, and (ii) to remove inappropriate correspondences from an alignment. The results of our evaluation show that the use of Description Logic in the ontology matching task can increase coverage. We identify people interested in ontology matching and reasoning techniques as the target audience of this work

by Wong Ka-sing. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1998. / Includes bibliographical references. / Abstracts in English and Chinese. / Chapter Chapter One --- Introduction --- p.1 / Chapter Chapter Two --- Research in Expertise --- p.4 / Chapter A --- Inherited Traits and Acquisition of Expertise --- p.4 / Chapter B --- Expert-novice Differences : Knowledge --- p.5 / Chapter C --- Practice and Development of Expertise --- p.12 / Chapter Chapter Three --- Expertise in teaching --- p.14 / Importance of Subject Matter Expertise in Relationship to Pedagogical Reasoning and Pedagogical Content Knowledge --- p.18 / Chapter Chapter Four --- Domain knowledge and its representation --- p.24 / Knowledge Representations and Networks --- p.26 / Techniques of Eliciting and Representing Knowledge --- p.29 / Chapter Chapter Five --- The Pathfinder algorithm and research resultsin relationship to education and learning --- p.34 / Chapter A --- Validity of the Pathfinder Network --- p.36 / Chapter B --- The Interpretation of Links in Pathfinder Network --- p.38 / Chapter Chapter Six --- Research findings relevant to the present study --- p.39 / Chapter A --- "Gomez, R. L, Hadfield O. D. and Housner, L D (1996): "" Concept Maps and Simulated Teaching Episodes as Indicators of Competence in Teaching Elementary Mathematics""" --- p.39 / Chapter B --- "Goldsmith, T. E. , Johnson, P.j., and Acton, W. H (1991): "" Assessing structural knowledge""" --- p.43 / Chapter C --- "Acton, W. H., Johnson, P. j. and Goldsmith, T. E (1994): ""Structural knowledge assessment: Comparison of referent structures""" --- p.45 / Chapter D --- "Gonzalvo, P., Canas, J. J., and Bajo. M. T. (1994): "" Structural representations in knowledge acquisition""" --- p.48 / Chapter E --- "Johnson, P. J. Goldsmith, T. E, and Teague, K. W (1994).: "" Locus of predictive advantage in Pathfinder- based representations of classroom knowledge""" --- p.50 / Chapter Chapter Seven --- The research question --- p.52 / Research Hypothesis --- p.53 / The Subjects of this Study --- p.54 / Method of Data Collection --- p.57 / Concepts Used for Rating --- p.58 / Chapter Chapter Eight --- Results and analysis --- p.60 / Qualitative Analysis of Pathfinder Networks and MDS Solutions --- p.60 / Testing of Hypotheses --- p.70 / Discussion --- p.75 / Chapter Chapter Nine --- Limitations of the present study --- p.78

The construction industry's environment is continually changing. Employees are now more geographically widespread and diverse, both culturally and educationally, than ever before. A great deal of research has been carried out on knowledge acquisition and storage, but there is still a distinct lack of research into knowledge presentation and communication. Information and knowledge presentation play a significant role in daily decision-making processes, when inappropriate decisions may result from inaccurate or poorly communicated information. The simplified, filtered coherent presentation of explicit knowledge can be instrumental to a successful, profitable and safety conscious business. Wates Construction is a major construction company and employs around 1300 people directly, as well as various subcontractors on different projects. Their current turn over is around £1billion, they are based in the UK and have branches in Ireland and Abu Dhabi. Wates realised their existing information system was inefficiently conveying information to its employees and the need to provide a simplified system, to assist staff's decision-making processes. Earlier IT professionals' attempts to make the system more usable had made no significant difference to its performance.

The study addresses the automated translation of architectural drawings from 2D Computer Aided Drafting (CAD) data into a Building Information Model (BIM), with emphasis on the nature, possible role, and limitations of a drafting language Knowledge Representation (KR) on the problem and process. The central idea is that CAD to BIM translation is a complex diagrammatic interpretation problem requiring a domain (drafting language) KR to render it tractable and that such a KR can take the form of an information model. Formal notions of drawing-as-language have been advanced and studied quite extensively for close to 25 years. The analogy implicitly encourages comparison between problem structures in both domains, revealing important similarities and offering guidance from the more mature field of Natural Language Understanding (NLU). The primary insight we derive from NLU involves the central role that a formal language description plays in guiding the process of interpretation (inferential reasoning), and the notable absence of a comparable specification for architectural drafting. We adopt a modified version of Engelhard's approach which expresses drawing structure in terms of a symbol set, a set of relationships, and a set of compositional frameworks in which they are composed. We further define an approach for establishing the features of this KR, drawing upon related work on conceptual frameworks for diagrammatic reasoning systems. We augment this with observation of human subjects performing a number of drafting interpretation exercises and derive some understanding of its inferential nature therefrom. We consider this indicative of the potential range of inferential processes a computational drafting model should ideally support. The KR is implemented as an information model using the EXPRESS language because it is in the public domain and is the implementation language of the target Industry Foundation Classes (IFC) model. We draw extensively from the IFC library to demonstrate that it can be applied in this manner, and apply the MVD methodology in defining the scope and interface of the DOM and IFC. This simplifies the IFC translation process significantly and minimizes the need for mapping. We conclude on the basis of selective implementations that a model reflecting the principles and features we define can indeed provide needed and otherwise unavailable support in drafting interpretation and other problems involving reasoning with this class of diagrammatic representations.

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