924 resultados para knowledge systems


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This paper presents the knowledge model of a distributed decision support system, that has been designed for the management of a national network in Ukraine. It shows how advanced Artificial Intelligence techniques (multiagent systems and knowledge modelling) have been applied to solve this real-world decision support problem: on the one hand its distributed nature, implied by different loci of decision-making at the network nodes, suggested to apply a multiagent solution; on the other, due to the complexity of problem-solving for local network administration, it was useful to apply knowledge modelling techniques, in order to structure the different knowledge types and reasoning processes involved. The paper sets out from a description of our particular management problem. Subsequently, our agent model is described, pointing out the local problem-solving and coordination knowledge models. Finally, the dynamics of the approach is illustrated by an example.

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This paper describes a general approach for real time traffic management support using knowledge based models. Recognizing that human intervention is usually required to apply the current automatic traffic control systems, it is argued that there is a need for an additional intelligent layer to help operators to understand traffic problems and to make the best choice of strategic control actions that modify the assumption framework of the existing systems.

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Linguistic systems are the human tools to understand reality. But is it possible to attain this reality? The reality that we perceive, is it just a fragmented reality of which we are part? In this paper the authors present is an attempt to address this question from an epistemological and philosophic linguistic point of view.

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Decision support systems (DSS) support business or organizational decision-making activities, which require the access to information that is internally stored in databases or data warehouses, and externally in the Web accessed by Information Retrieval (IR) or Question Answering (QA) systems. Graphical interfaces to query these sources of information ease to constrain dynamically query formulation based on user selections, but they present a lack of flexibility in query formulation, since the expressivity power is reduced to the user interface design. Natural language interfaces (NLI) are expected as the optimal solution. However, especially for non-expert users, a real natural communication is the most difficult to realize effectively. In this paper, we propose an NLI that improves the interaction between the user and the DSS by means of referencing previous questions or their answers (i.e. anaphora such as the pronoun reference in “What traits are affected by them?”), or by eliding parts of the question (i.e. ellipsis such as “And to glume colour?” after the question “Tell me the QTLs related to awn colour in wheat”). Moreover, in order to overcome one of the main problems of NLIs about the difficulty to adapt an NLI to a new domain, our proposal is based on ontologies that are obtained semi-automatically from a framework that allows the integration of internal and external, structured and unstructured information. Therefore, our proposal can interface with databases, data warehouses, QA and IR systems. Because of the high NL ambiguity of the resolution process, our proposal is presented as an authoring tool that helps the user to query efficiently in natural language. Finally, our proposal is tested on a DSS case scenario about Biotechnology and Agriculture, whose knowledge base is the CEREALAB database as internal structured data, and the Web (e.g. PubMed) as external unstructured information.

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"Mode 3" allows and emphasizes the co-existence and co-evolution of different knowledge and innovation paradigms: the competitiveness and superiority of a knowledge system is highly determined by its adaptive capacity to combine and integrate different knowledge and innovation modes via co-evolution, co-specialization and coopetition [sic] of knowledge stock and flow dynamics. What results is an emerging fractal knowledge and innovation ecosystem, well-configured for the knowledge economy and society. The intrinsic litmus test of the capacity of such an ecosystem to survive and prosper in the context of continually glocalizing [sic] and intensifying competition represents the ultimate competitiveness benchmark with regards to the robustness and quality of the ecosystem's knowledge and innovation architecture and topology.

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"UILU-ENG 80 1704."

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The performance of most operations systems is significantly affected by the interaction of human decision-makers. A methodology, based on the use of visual interactive simulation (VIS) and artificial intelligence (AI), is described that aims to identify and improve human decision-making in operations systems. The methodology, known as 'knowledge-based improvement' (KBI), elicits knowledge from a decision-maker via a VIS and then uses AI methods to represent decision-making. By linking the VIS and AI representation, it is possible to predict the performance of the operations system under different decision-making strategies and to search for improved strategies. The KBI methodology is applied to the decision-making surrounding unplanned maintenance operations at a Ford Motor Company engine assembly plant.

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This paper makes a case for taking a systems view of knowledge management within health-care provision, concentrating on the emergency care process in the UK National Health Service. It draws upon research in two casestudy organizations (a hospital and an ambulance service). The case-study organizations appear to be approaching knowledge (and information) management in a somewhat fragmented way. They are trying to think more holistically, but (perhaps) because of the ways their organizations and their work are structured, they cannot ‘see’ the whole of the care process. The paper explores the complexity of knowledge management in emergency health care and draws the distinction for knowledge management between managing local and operational knowledge, and global and clinical knowledge.

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This paper will outline a research methodology informed by theorists who have contributed to actor network theory (ANT). Research informed from such a perspective recognizes the constitutive role of accounting systems in the achievement of broader social goals. Latour, Knoor Cetina and others argue that the bringing in of non-human actants, through the growth of technology and science, has added immeasurably to the complexity of modern society. The paper ‘sees’ accounting and accounting systems as being constituted by technological ‘black boxes’ and seeks to discuss two questions. One concerns the processes which surround the establishment of ‘facts’, i.e. how ‘black boxes’ are created or accepted (even if temporarily) within society. The second concerns the role of existing ‘black boxes’ within society and organizations. Accounting systems not only promote a particular view of the activities of an organization or a subunit, but in their very implementation and operation ‘mobilize’ other organizational members in a particular direction. The implications of such an interpretation are explored in this paper. Firstly through a discussion of some of the theoretic constructs that have been proposed to frame ANT research. Secondly an attempt is made to relate some of these ideas to aspects of the empirics in a qualitative case study. The case site is in the health sector and involves the implementation of a casemix accounting system. Evidence from the case research is used to exemplify aspects of the theoretical constructs.

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Purpose - To consider the role of technology in knowledge management in organizations, both actual and desired. Design/methodology/approach - Facilitated, computer-supported group workshops were conducted with 78 people from ten different organizations. The objective of each workshop was to review the current state of knowledge management in that organization and develop an action plan for the future. Findings - Only three organizations had adopted a strongly technology-based "solution" to knowledge management problems, and these followed three substantially different routes. There was a clear emphasis on the use of general information technology tools to support knowledge management activities, rather than the use of tools specific to knowledge management. Research limitations/implications - Further research is needed to help organizations make best use of generally available software such as intranets and e-mail for knowledge management. Many issues, especially human, relate to the implementation of any technology. Participation was restricted to organizations that wished to produce an action plan for knowledge management. The findings may therefore represent only "average" organizations, not the very best practice. Practical implications - Each organization must resolve four tensions: Between the quantity and quality of information/knowledge, between centralized and decentralized organization, between head office and organizational knowledge, and between "push" and "pull" processes. Originality/value - Although it is the group rather than an individual that determines what counts as knowledge, hardly any previous studies of knowledge management have collected data in a group context.

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The social processes involved in engaging small groups of 3-15 managers in their sharing, organising, acquiring, creating and using knowledge can be supported with software and facilitator assistance. This paper introduces three such systems that we have used as facilitators to support groups of managers in their social process of decision-making by managing knowledge during face-to-face meetings. The systems include Compendium, Group Explorer (with Decision Explorer) and V*I*S*A. We review these systems for group knowledge management where the aim is for better decision-making, and discuss the principles of deploying each in a group meeting. © 2006 Operational Research Society Ltd. All rights reserved.