Mechanical assembly is an important step during product realization, which is an integrative process that brings together the parts of the assembly, the people performing the assembly and the various technologies that are involved. Assembly planning involves deciding on the assembly sequence, the tooling and the processes to be used. Assembly planning should enable the actual assembly process to be as effective as possible.Assembly plans may have to be revised due to issues arising during assembly. Many of these revisions can be avoided at the planning stage if assembly planners have prior knowledge of these issues and how to resolve them. General guidelines to make assembly easier (e.g. Design for Assembly) are usually suited for mass-manufactured assemblies and are applied where similar issues are faced regularly. However, for very specific issues that are unique to some domains only, such as aircraft assembly, only expert knowledge in that domain can identify and resolve the issues. Assembly experts are the sources of knowledge for identifying and resolving these issues. If assembly planners could receive assembly experts’ advice about the potential issues and resolutions that are likely to occur in a given assembly situation, they could use this advice to revise the assembly plan in order to avoid these issues. This link between assembly experts and planners can be provided using knowledge based systems. Knowledge-based systems contain a knowledge base to store experts’ knowledge, and an inference engine that derives certain conclusions using this knowledge. However, knowledge acquisition for such systems is a difficult process with substantial resistance to being automated. Methods reported in literature propose various ways of addressing the problem of automating knowledge acquisition. However, there are many limitations to these methods, which have been the motivations for the research work reported in this thesis. This thesis proposes a dialog-like method of questioning an expert to automatically acquire knowledge from assembly experts. The questions are asked in the context of an assembly situation shown to them. During the interviews, the knowledge required for diagnosing potential issues and resolutions are identified. The experts were shown a situation, and asked to identify issues and suggest solutions. The above knowledge is translated into the rules for a knowledge based system. This knowledge based system can then be used to advise assembly planners about potential issues and solutions in an assembly situation. After a manual verification, the questioning procedure has been implemented on computer as a software named EXpert Knowledge Acquisition and Validation (ExKAV). A preliminary evaluation of ExKAV has been carried out, in which assembly experts interacted with the tool using the researcher as an intermediary. The results of these sessions have been discussed in the thesis and assessed against the original research objectives. The current limitations of the procedure and its implementation have been highlighted, and potential directions for improving the knowledge acquisition process are discussed.

Thesis (M.S.)--Rochester Institute of Technology, 1990. / Spine title: Identify generic tasks. Includes bibliographical references (leaves 55-57).

Interactive as well as Automatic Verification and Validation is valuable, especially when the size of a knowledge base grows and manual techniques are not feasible. It ensures the stability of the system and raises the confidence in its level of performance. In this dissertation I address the problem of verification and validation of rule based expert systems. It is a problem knowledge engineers have to deal with while building their expert systems to ensure the reliability, accuracy, and completeness of their knowledge bases. The objective of this research is to make it easy for expert systems developers to build the right system by proposing practical and simple methods for building verification and validation programs to insure the integrity and performance of large scale knowledge based systems.

The technology of knowledge-based systems undoubtedly offers potential for educational modelling, yet its practical impact on today's school classrooms is very limited. To an extent this is because the tools presently used in schools are EMYCIN -type expert system shells. The main argument of this thesis is that these shells make knowledge-based modelling unnecessarily difficult and that tools which exploit knowledge acquisition technologies empower learners to build better models. We describe how such tools can be designed. To evaluate their usability a model-building course was conducted in five secondary schools. During the course pupils built hundreds of models in a common range of domains. Some of the models were built with an EMYCIN -type shell whilst others were built with a variety of knowledge acquisition systems. The knowledge acquisition systems emerged as superior in important respects. We offer some explanations for these results and argue that although problems remain, such as in teacher education, design of classroom practice, and assessment of learning outcomes, it is clear that knowledge acquisition systems offer considerable potential to develop improved forms of educational knowledge-based modelling.

Research specific to the agriculture literacy of adult learners and, more specifically, college students, is warranted (Kovar & Ball, 2013). In the current study, I analyze the sources of knowledge that are dispersed across formal, non-formal, and informal learning settings that college students rely on to understand the various components of food systems. More specifically, I ask here, "How do college students learn about food systems?" Data were collected using a convenience sample of undergraduate students at a large research-intensive university who responded to an authentic questionnaire developed specifically for this study. Findings indicate that there is little variation between the three learning settings with respect to food system knowledge. This indicates that education with respect to food system knowledge is lacking. Implications of the study include recommendations for the development and refinement of formal and non-formal food systems curricula specific to the needs, backgrounds, and experiences of undergraduate college students.

This article is about technology search as an example of a knowledge acquisition task in industry. Technology search is about finding technology related information in structured as well as unstructured sources. This information is needed to support optimal decision making in business processes. There are new opptortunities for technology search and challenges that need to be addressed. This article outlines some of these challenges and presents two concepts to address them in a search system.

Data mining algorithms discover knowledge from data. The knowledge are commonly expressed as dependency relationships in various forms, like rules, decision trees and Bayesian Networks (BNs). Moreover, many real-world problems are multi-class problems, in which more than one of the variables in the data set are considered as classes. However, most of the rule learners available were proposed for single-class problems only and would produce cyclic rules if they are applied to multi-class ones. In addition, most of them produce rules with conflicts, i.e. more than one of the rules classify the same data items and different rules have different predictions. Similarly, existing decision trees learners cannot handle multi-class problems, and thus cannot detect and avoid cycles. In contrast, BNs represent acyclic dependency relationships among variables, but they can handle discrete values only. They cannot manage continuous, interval and ordinal values and cannot represent higher-order relationships. Consequently, BNs have higher network complexity and lower understandability when they are used for such problems. / This thesis has studied in depth discovering dependency relationships in various forms by Evolutionary Computation (EC). Through analysis of the reasons leading to the disadvantages of rules, decision trees and BNs, and their learners, we have proposed a sequence of EAs, a novel functional dependency network (FDN) and two techniques for dependency relationship learning and for multi-class problems. They are the multi-population Genetic Programming (GP) using backward chaining procedure and the GP employing co-operating scoring stage for acyclic rules learning. The dependency network with functions can manage all kinds of values and represent any kind of relationships among variables, the flexible and robust MDLGP to learn the novel dependency network and BN. Based on the FDN we have further developed the techniques to learn rules without conflict and acyclic decision trees for multi-class problems respectively. The new self-organizing map (SOM) with expanding force for clustering and data visualization for data preprocessing have also been given in the appendix. / Shum Wing Ho. / "May 2007." / Adviser: Kwong-Sak Leung. / Source: Dissertation Abstracts International, Volume: 69-01, Section: B, page: 0436. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 221-240). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.

An extension of wrapper adaptation is developed to collectively extract information from multiple Web pages. There exists mutual influence between text fragments of different Web pages and hence they should be considered collectively during extraction. Extending from the dependence model, a framework which can consider the dependence between text fragments within a single Web page and the dependence between text fragments from different pages. One characteristic of this model is that additional information can be incorporated into the model and multiple tasks earl be tackled simultaneously. As a result, a global solution which can optimize the quality of the tasks, at the same time, eliminate the conflict between them can he obtained. Experiments on product feature extraction and hot item mining from multiple auction Web sites have been conducted to demonstrate the effectiveness of this framework. / One problem of most existing Web information extraction methods is that the extraction knowledge learned from a Web site can only be applied to Web pages from the same site. This thesis first investigates the problem of wrapper adaptation which aims at adapting a wrapper previously learned from a source site to new unseen sites. A dependence model that can model the dependence between text fragments in Web pages is developed. Under this model, two types of text related features are identified. The first type of features is called site invariant features. These features likely remain unchanged in Web pages from different sites in the same domain. The second type of features is called site dependent features. These features are different in Web pages collected from different Web sites, while they are similar in Web pages originated from the same site. Based on this model, two frameworks are developed to solve the wrapper adaptation problem. The first framework is called Information Extraction Knowledge Adaptation using Machine Learning approach (IEKA-ML). Machine learning methods are employed to derive site invariant features from the previously learned extraction knowledge and items previously collected or extracted from the source Web site. Both site dependent features and site invariant features in new sites are considered for learning of new information extraction knowledge tailored to the new unseen site. / The second framework, called Information Extraction Knowledge Adaptation using Bayesian learning approach (IEKA-BAYES), solves the problem of wrapper adaptation as well as the issue of new attribute discovery. The new attribute discovery problem aims at extracting new or previously unwell attributes that are not specified in the wrapper. To harness the uncertainty, a probabilistic generative model for the generation of text fragments and layout format related to attributes in Web pages is designed. Bayesian learning and expectation-maximization (EM) techniques are developed under the proposed generative model to accomplish the wrapper adaptation task. Previously unseen attributes together with their semantic labels earl be discovered via another EM-based Bayesian learning on the generative model. Extensive experiments on over 30 real-world Web sites in three different domains and comparison between existing works have been conducted to evaluate the IEKA-ML and IEKA-BAYES frameworks. / Wong Tak Lam. / "October 2006." / Adviser: Lam Wai. / Source: Dissertation Abstracts International, Volume: 68-09, Section: B, page: 6095. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2006. / Includes bibliographical references (p. 126-135). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstract in English and Chinese. / School code: 1307.

The Meno has traditionally been viewed as "one of Plato's earliest and most noteworthy forays into epistemology." In this dialogue, and in the course of a discussion between Socrates and his young interlocutor, Meno, about the nature of virtue and whether it can be taught, “Meno raises an epistemological question unprecedented in the Socratic dialogues.” This question - or rather, dilemma - has come to be known in the philosophical literature as Meno’s Paradox of Inquiry, due its apparently containing an easy-to-detect equivocation of the word ‘know’. Immediately after the paradox, and in an apparent response to it, Socrates recounts a myth: a story told by priests and priestesses about the pre-natal existence and immortality of the soul. From this this myth, Socrates concocts the infamous theory of recollection – a theory according to which the soul has acquired knowledge of everything before it was born, while in a disincarnate state. According to the traditional reading of Meno’s paradox, this theory constitutes Plato’s response to it. The traditional reading has come under fire in recent years by advocates of the epistemological reading (ERM), who argue that the theory of recollection is not Plato’s intended response to the paradox. Instead, they suggest, Plato’s distinction between true belief and knowledge – which appears towards the end of the dialogue – is sufficient for solving the paradox; and as such, it ought to be read as Plato’s response to it. In this thesis, I argue against ERM’s claim that a mere epistemological distinction is all it takes to solve the paradox. To do so, I explore the metaphysics of change in Plato’s ontology. From this, I appeal to our everyday notion of ‘memory’ in order to show that Meno’s paradox, in fact, contains a hidden-premise, which when laid bare, reveals two distinct challenges contained within the argument: a superficial one, and a deeper one. I argue that although it appears at first blush as though the former could easily be dismissed as an equivocation, to which the epistemological distinction between belief and knowledge could provide an answer, the latter cannot. This is because the deeper challenge threatens the very preconditions of knowledge itself – that is to say, it renders cognition impossible – and, as such, it cancels out any effort to provide an epistemological response to the superficial challenge. Hence, unless the deeper-level challenge is satisfactorily disarmed, both challenges remain unanswered. I argue that although the major motivation for the theory of recollection in the Meno is indeed to provide an answer to scepticism about knowledge, nevertheless, it ought to be understood, first, as a theory of cognition – i.e. as a theory about the preconditions and atomic building blocks of knowledge – and not a theory of knowledge per se. This answer comes in the form of a radical theory of the mind and cognition – one that stands in stark opposition to our common-sense views about the mind: a view from which, Plato believed, the paradox arises. Drawing on recent debates between Nativists and Empiricists in the Cognitive Sciences, I argue that it was a great achievement of Plato’s to grasp that our common-sense view about the mind, and its concomitant process of learning, language acquisition and knowledge acquisition, might in fact be at the very root of scepticism about our ability to engage in meaningful philosophical practice, and our ability to acquire objective knowledge – especially, objective moral knowledge. The Meno’s paradox, then – so I contend - is not a puzzle whose solution rests upon merely pointing to an epistemological distinction between true belief and knowledge, as advocates of ERM have suggested. Rather, it is a puzzle about cognition. More precisely, it is a puzzle that targets the rudimentary cognitive stages of initial cognition and truth-recognition - one whose solution entails offering an account of the mind that would make these elementary cognitive processes possible. Accordingly, Plato’s theory of recollection in the Meno ought to be read as an attempt to map the structure of the mind, and as such, to provide an account of cognition. In doing so, he intended to put forward a view about the preconditions of knowledge – the sort of preconditions without which language acquisition and knowledge acquisition would simply not be possible. With this theory, Plato has the beginnings of an argument against the kind of relativism and scepticism prevalent at his time. As such, a correct interpretation of the so-called paradox of inquiry (and Plato’s proposed solution to it via the theory of recollection) should approach it as a puzzle about mind and cognition – and not solely as an epistemological one, as it has previously been treated.

Social capital is embedded in relationships and crucial for facilitating value creation. This study adopts a micro-relational perspective on social capital and addresses the gap in the literature concerning its importance for explaining the management of professional-client relationships. The research provides an in-depth analysis of professional-client relationships, considering both the structure and the attributes, as well as how social capital is utilized by professionals to create and maintain networks with new and existing clients. This research proposes a framework for examining the relationship between human capital and social capital. Human capital is understood as manifested by the extent of knowledge acquisition within three domains: market knowledge, technical knowledge and client-specific knowledge. The outcomes of social capital in professional-client relationships are examined in relation to knowledge acquisition and relationship outcome variables. Professionals employed in knowledge-intensive firms rely on both their human capital and social capital to acquire and commercialize their knowledge to deliver value in their professional services to clients. However, research to-date has insufficiently investigated the relationship between human capital and social capital, and the application of social capital theory to the study of lawyers and law firms, as proposed in this PhD research, draws attention to the importance of these social processes within professional-client relationships. Social capital, in this study, is defined as consisting of two dimensions: structural and attributive. The structural dimension of social capital refers to the idea that networks of actors can influence the outcomes of social relationships, while the attributive dimension of social capital refers to the extent of trust in relationships. Structural perspectives on social capital have been predominant in the literature, but discussion on the attributes of such ties have been lacking. As such, with limited attempts to operationalize the construct of social capital in the literature (Koka and Prescott, 2002) and the differing aspects of social capital considered in empirical investigations (Wu, 2008), existing studies on social capital have been characterized by the lack of consensus on the dimensions that compose social capital as well as the measures of social capital (Bolino et al., 2002; Wu, 2008). The theoretical framework is examined through a triangulation of cross-methods and cross-samples approaches by conducting two studies (qualitative and quantitative as different methods) with different samples. Study 1 is a qualitative study, designed to evaluate the theoretical framework through a set of exploratory interviews with Australian client organizations to reveal their views on the contracting, evaluation and continuation of legal service delivery by external law firms. Many of the organizations included in this study are large and diverse businesses, with in-house departments ranging from those managing a small team to large in-house teams working in a large corporate company. Study 1 is an exploratory investigation of the themes that emerged from the interviews, and its findings provide some preliminary evidence for the various propositions. Study 2 is a quantitative study of survey research of Australian law firms and their client organizations conducted by administering web-based questionnaires to lawyers and their clients. The questionnaire items are designed to measure the constructs presented in the theoretical framework, with data collected using both social network techniques and a conventional Likert-type format of question items. The data are analyzed using a series of statistical regression procedures to test for mediation and the effects of the control variables, as well as by conducting Partial Least Squares (PLS)-based structural equation modeling to examine the relationships between the constructs in the full structural model. The findings from Study 2 indicate support for the hypotheses presented in the theoretical framework. First, the findings support the hypothesis that there is a link between human capital and social capital. Knowledge acquired from all clients, representing the aspect of a professional’s human capital in this context, is significant in influencing both dimensions of social capital. Second, the findings of the study denote that human capital, as represented by knowledge acquired from a professional’s most important client, plays a mediating role in the relationships between social capital and the various relationship outcomes of reputation, referrals, fee income, and relationship continuity. Third, the findings reveal that the construct of the structural dimension of social capital is made up of various components, including: centrality, frequency of communication, nature of communication, the non-redundancy of ties, and network diversity. Finally, the findings also indicate a high reliability score for conceptualizing the attributive dimension of social capital as constituting of three types of trust: rational trust, moral trust, and affective trust. In conclusion, the study provides a useful means of advancing our understanding of the link between social capital, human capital (knowledge acquisition), and relationship outcomes by empirically examining these constructs in the context of professional-client relationships, and by creating a new line of research inquiry into this significant area of strategic management research.