Hierarchical representation of complex systems for supporting human decision making |
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| Affiliation: | 1. Laboratoire d''Automatique de Grenoble, CNRS-INPG-UJF, BP 46 38402 St Martin d''Hères, France;2. Commissariat à l''Energie Atomique (CEA), URC EMA-CEA, Site EERIE, Parc Scientifique G. Besse 30035 Nîmes, Cedex, France;1. Centre for Addiction and Mental Health, Toronto, Ontario, Canada;2. University of Toronto, Toronto, Ontario, Canada;1. Copernicus Institute for Sustainable Development and Innovation, Utrecht University, The Netherlands;2. SPINlab/Department of Spatial Economics, VU University Amsterdam, The Netherlands;3. Instituto de Ingeneria Rural, CIA Instituto Nacional de Tecnologia Agropecuaria INTA, Buenos Aires, Argentina;4. Instituto de Clima y Agua, CIRN Instituto Nacional de Tecnologia Agropecuaria INTA, Buenos Aires, Argentina;5. Laboratorio de Teledetección y SIG, Instituto Nacional de Tecnologia Agropecuaria INTA, Salta, Argentina;1. Environmental Change Institute, University of Oxford, South Parks Road, Oxford OX1 3QY, UK;2. Department of Environmental Sciences and Policy, Central European University, Nádor u. 9, Budapest 1051, Hungary;3. Department of Environmental Sciences, Wageningen University, Droevendaalsesteeg 3, Wageningen 6708 PB, The Netherlands;4. International Institute for Sustainable Development, 325-111 Lombard Avenue, Winnipeg MB R3B 0T4, Canada;5. Institute of Meteorology and Climate Research (IMK-IFU), Karlsruhe Institute of Technology, Kreuzeckbahnstrasse 19, Garmisch-Partenkirchen 82467, Germany;6. School of GeoSciences, University of Edinburgh, Drummond Street, Edinburgh EH8 9XP, UK;7. Centre for Ecology & Hydrology, Library Avenue, Lancaster LA1 4AP, UK;1. Department of Economics and Business Engineering, Karlsruhe Institute of Technology, Germany;2. Centre on Asia and Globalisation, Lee Kuan Yew School of Public Policy, National University of Singapore, Singapore;1. Dept of Food, Agricultural and Resource Economics, University of Guelph, Canada;2. Dept of Geography, University of Guelph, Canada |
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| Abstract: | The work presented in this paper is devoted to intelligent on-line supervision tools. In the proposed approach, the human operator remains in the decision loop, at the highest level, and acts on the process. To help operators make decisions, process knowledge is represented with a model whose complexity can be adapted on line to the operation needs at the request of the operator. The model thus helps to focus only on the phenomena that are relevant at a given time. To give the model explanatory capacity, it is represented as a causal directed graph, and allows the representation of temporal phenomena, which is fundamental for dynamic monitoring. A hierarchical representation of the functional properties of the process is proposed. The conception of a hierarchy of causal models with a top-down analysis is discussed. Path algebra is used to construct a higher-level graph on-line at the request of the operator from the most detailed graph, while conserving the semantics of the latter. No intermediate level is defined a priori; only the highest and lowest level graphs are fixed: the others are constructed dynamically. Finally, a study of how graphs can convey information on the dynamics of the process for approximate temporal reasoning that is largely sufficient for supervision purposes is analyzed. An example of a causal graph hierarchy for a nuclear process illustrates the method. As a final point, the use of such causal graphs in advanced industrial supervision tools is considered. |
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