Abstract
ARIES (Artificial Intelligence for Ecosystem Services) is a large-scale project aimed to supporting environmental decision-making by quantifying and spatially mapping the flows of benefits provided by natural systems to humans. Because only ad-hoc models can reflect local conditions with enough detail to support decision making, ARIES needs to specialize its predictive models to a highly diverse set of contexts. This article describes the approach adopted in ARIES to automate the construction of ad-hoc models of variable structure, based on a set of context observations and on rules that encode the expert knowledge that associates model structure to specific environmental, social and economic conditions.
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Acknowledgements
This work is funded by the US National Science Foundation through grant 0640837 (Project ARIES). The work includes intellectual contributions from many colleagues, including but not limited to Gary W. Johnson, Jr., Sergey Krivov, Robert Snapp and Ioannis N. Athanasiadis.
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Villa, F. (2009). Semantically driven meta-modelling: automating model construction in an environmental decision support system for the assessment of ecosystem services flows. In: Athanasiadis, I.N., Rizzoli, A.E., Mitkas, P.A., Gómez, J.M. (eds) Information Technologies in Environmental Engineering. Environmental Science and Engineering(). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88351-7_2
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DOI: https://doi.org/10.1007/978-3-540-88351-7_2
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