Spelling suggestions: "subject:"aknowledge engineering"" "subject:"asknowledge engineering""
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Learning organisation based intelligent tutoring system for power utilitiesChakpitak, Nopasit January 2003 (has links)
No description available.
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The determination of relevance through metaphor in knowledge acquisitionFunes, Maria del Valle January 1993 (has links)
No description available.
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Virtuality, community and individuality : developing new insights into learning and knowledge in the small businessNolan, Terence Louis January 2003 (has links)
No description available.
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Design method and management utility enabling the concurrent exercise of distributed expertiseTsiotsias, Andreas Stylianos January 1994 (has links)
No description available.
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Dynamics in formal argumentationCarbogim, Daniela Vasconcelos January 2000 (has links)
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.
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Knowledge based system development as an engineering processDavoodi, M. January 1989 (has links)
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.
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Enhancing university research activities with knowledge management.Chandarasupsang, T., Harnpornchai, N., Chakpitak, N., Dahal, Keshav P. January 2006 (has links)
Yes / 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.
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Knowledge engineering in the virtual enterprise : exploring a maturity-based decision supportJohansson, Christian January 2007 (has links)
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
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A Method for Knowledge Engineering in Clinical Decision MakingGiere, Sheila S. 01 May 1989 (has links)
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.
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Automated experience-based learning for plug and produce assembly systemsScrimieri, Daniele, Antzoulatos, N., Castro, E., Ratchev, S.M. 04 March 2020 (has links)
Yes / This paper presents a self-learning technique for adapting modular automated assembly systems. The technique consists of automatically analysing sensor data and acquiring experience on the changes made on an assembly system to cope with new production requirements or to recover from disruptions. Experience is generalised into operational knowledge that is used to aid engineers in future adaptations by guiding them throughout the process. At each step, applicable changes are presented and ranked based on: (1) similarity between the current context and those in the experience base; (2) estimate of the impact on system performance. The experience model and the self-learning technique reflect the modular structure of the assembly machine and are particularly suitable for plug and produce systems, which are designed to offer high levels of self-organisation and adaptability. Adaptations can be performed and evaluated at different levels: from the smallest pluggable unit to the whole assembly system. Knowledge on individual modules can be reused when modules are plugged into other systems. An experimental evaluation has been conducted on an industrial case study and the results show that, with experience-based learning, adaptations of plug and produce systems can be performed in a shorter time. / European Union [grant number 314762].
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