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In this thesis we are concerned with the role of formal argumentation in artificial intelligence, in particular in the field of knowledge engineering. The intuition behind argumentation is that one can reason with imperfect information by constructing and weighing up arguements intended to give support in favour or against alternative conclusions. In dynamic argumentation, such arguements may be revised and strengthened in order yo increase to decrease the acceptability of controversial positions. This thesis studies the theory, architecture, development and applications of formal arguementation systems from the procedural perspective of actually generating argumentation processes. First, the types of problems that can be tackled via the argumentation paradigm in knowledge engineering are characterised. Second, an abstract formal framework are built from an underlying set of axioms, represented here as executatble logic programs. Finally an architecture for dynamic arguementation systems is defined, and domain-specific applications are presented within different domaind, thus grounding problems with very distinctive characteristics into a similar source in argumentation. The methods and definitions desribed in this thesis have been assessed on various bases, including the reconstruction of informal arguements and of arguments captured by existing formalisms, the relation between our framework and these formalisms, and examples of dynamic argumentation applications in the safety-engineering and multi-agent domains.

Knowledge Based System (KBS) development is a difficult and challenging task, in particular in knowledge intensive domains. The traditional view of knowledge engineering is one of mining experts' knowledge and somehow transforming it into a machine usable form. This process, in general, suffers from insufficient or misconstrued representation of experts' problem solving behaviour. It is also unstructured and unduly biased at an early stage by design and implementation issues - normally in the form of incremental prototyping. We believe that both knowledge acquisition and KBS development for real life applications will require a 'structured' approach. This approach should harness a KBS developer's ability in extracting knowledge and developing systems. The structure should also be sufficiently flexible to allow the knowledge engineer to use his sense of creativity in developing a KBS. This thesis puts forward such a structured approach, in which KBS development is carried out in an engineering fashion. A process in which the worker is provided with an environment for developing knowledge based systems as an engineering process, as opposed to that of an artform or crafting. The main emphasis of this work is that part of the process which deals with the analysis and design phases in developing KBS. The analysis is performed at an 'epistemological' level, not coloured by design or implementation issues. The output of this phase captures both an expert's problem solving capability, and the business constraints placed upon the intended system. This is then used by the design process in order to create an optimal, workable, and elegant design architecture for the ultimate system.

In the new economy, innovation is regarded as one of the solutions for almost every organisation to survive in the new business era. Universities, especially in terms of research activities, are no difference since they strive for novelties which potentially lead to innovation. An experienced researcher in the university has continually created tacit knowledge in a specific domain, but typically found it difficult to share this tacit knowledge among other researchers for the problem solving purpose. To overcome this problem and to better stimulate knowledge sharing activities among university researchers, Knowledge Management and Knowledge Engineering, particularly KADS, are utilised in this paper to assist a group of different domain researchers in putting their experiences together. In this way, each researcher can make explicit his or her tacit knowledge into KADS task, inference and domain knowledge models. The structured knowledge models captured from different researchers can then be merged together. In this paper, the research in Knowledge Management is selected as a case study, and the results show that the relevant tacit knowledge has been made explicit from a researcher and allow other researchers to share the knowledge as well as to add their own knowledge. Hence, their common research theme is effectively created, and also maintained by a group of researchers.

In product development, lead-time reduction, cost reduction, and quality improvement are issues that companies want to improve on to increase competitiveness. One recent approach to reach this - particularly in the aerospace industry where the complexity of product offers is steadily increasing - is to manage risk by forming virtual enterprises. A virtual enterprise is a network of partner companies that join on equal terms when an opportunity arises to develop a product offer, e.g. a jet engine offer, in a more agile manner than if any of the partners would realise it by themselves. They therefore team up to share risk, investment and resources - to in return also share revenue and profit. A driver for the formation of the virtual enterprise is the ability to effectively utilise partner knowledge assets. However, when sharing and managing knowledge effectively across the virtual enterprise, current practices have yet to evolve to meet the needs of knowledge workers, who may come from different aerospace companies, have different roles, belong to different disciplines and that may also be situated in geographically dispersed locations. Improving product development includes allowing developers from all disciplines to know - as early as possible in the product development process - more about the customer needs, the desired product properties, and the downstream impact of the decisions they choose to make throughout the process. Knowing about the impact in downstream phases would allow for significant time and cost savings due to the avoidance of unnecessary and expensive rework that would otherwise occur much further on in the product's life cycle. Among other things, a virtual enterprise can start organising and mapping the knowledge assets available in their teams, and information overload can be managed by assuring that the right knowledge ends up with the right person, to mention but a few things that can facilitate the everyday work of engineers and their colleagues. When working in a product development project, the virtual enterprise needs to assess the quality of the created knowledge as early as possible to devise the correct actions early. In this thesis, a Gated Maturity Assessment technique including the concept of knowledge maturity has been developed as an example of an improved stage-gate decision-making process. With this approach development teams are able to assess the knowledge maturity level in the content and rationale that is put forward as a basis for a decision - as opposed to only assessing the raw data of the results (i.e. thrust, weight, fuel burn, etc.). Knowledge maturity is used to support decision makers when in the process of assessing a decision base to make a decision whether to go ahead, abort the process, or order rework to be done. Naturally, if the decision base is poor, a decision to go ahead should probably not be taken, as the consequences might be negative. In assessing maturity, decision makers can determine at decision points if the knowledge base is good enough to move forward to the next step in the jet engine component design, if there is need for rework, and what specific areas need to be improved. Decision makers can divert and focus resources to areas of importance due to, for instance, too low maturity levels. Knowledge maturity is a way to - using a criteria scale that prescribes the knowledge needed at each level - help development teams assess and visualise how well they know what they know, and subsequently, what they need to know. This thesis explores the feasibility of using knowledge maturity as a way of supporting knowledge engineering in the context of a development process in aeronautics. / Faste Laboratory

The purpose of this study was to validate the problem behavior evaluation section of an expert system computer program, Class.BO. Class.BO was developed to assist special education personnel in determining whether students qualify for special education services as behaviorally disordered/severely emotionally disturbed students. The subjects were six Utah who regularly individuals from the state of 1) work with behaviorally disordered/severely emotionally disturbed students and 2) participate in multidisciplinary assessment teams. Three of the subjects were special educators, and three were school psychologists. Specifically, this study investigated the impact of five behavioral factors on the subjects' ratings of the seriousness of problem behaviors. The five behavioral factors were 1) the severity or nature of the problem behavior, 2) the frequency with which the problem behavior occurs, 3) the duration over which the problem behavior has been occurring, 4) the generality of the problem behavior or the number of school environments the behavior occurs in , and 5) the percentage of the student's peers who engage in the same behavior. For each behavioral factor, three levels of that factor were determined: high, moderate, and low. Problem behavior descriptions were developed by the researcher, each of which presented the five behavioral factors at a predetermined combination of levels. Of 65 problem behavior descriptions, 3 3 described externalized problem behaviors and 32 described internalized problem behaviors. Subjects were asked to rate the seriousness of each problem behavior description on an 11 point scale, where l=mild and ll=severe. The results showed high levels of agreement among subjects on ratings of seriousness of problem behaviors. There was also high agreement between the subjects' ratings and ratings generated by the Class.BO expert system. Thus, Class. BD was validated. Further, the subjects gave highly similar ratings to descriptions of externalized and internalized problem behaviors. The results also indicated that the severity of the problem behaviors had the most impact on subjects' ratings. Subjects discriminated three levels of severity but only two levels of frequency, duration, generality, and percentage of peers. Finally, the results provided support for the use of analysis of variance as a viable method of knowledge engineering, i.e., extracting information about how experts make decisions. Its superiority over traditional interview methods is discussed.

Yes / The domain of cultural variations in interpersonal communication is becoming increasingly important in various areas, including human-human interaction (e.g. business settings) and humancomputer interaction (e.g. during simulations, or with social robots). User generated content (UGC) in social media can provide an invaluable source of culturally diverse viewpoints for supporting the understanding of cultural variations. However, discovering and organizing UGC is notoriously challenging and laborious for humans, especially in ill-defined domains such as culture. This calls for computational approaches to automate the UGC sensemaking process by using tagging, linking and exploring. Semantic technologies allow automated structuring and qualitative analysis of UGC, but are dependent on the availability of an ontology representing the main concepts in a specific domain. For the domain of cultural variations in interpersonal communication, no ontological model exists. This paper presents the first such ontological model, called AMOn+, which defines cultural variations and enables tagging culture-related mentions in textual content. AMOn+ is designed based on a novel interdisciplinary approach that combines theoretical models of culture with crowdsourced knowledge (DBpedia). An evaluation of AMOn+ demonstrated its fitness-for-purpose regarding domain coverage for annotating culture-related concepts mentioned in text corpora. This ontology can underpin computational models for making sense of UGC.