Abstract
Big data collection and analysis is used in industry and public organizations to support decision-making. However, simulation as a core technology to support optimization, or the exploration of large state spaces in artificial intelligence have serious difficulties for industrial adoption. Our approach to solve these difficulties is the adoption of a modelling methodology supported by a cohesive framework based on the Petri net formalism for efficient simulation of complex discrete event systems over large computational infrastructures.
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Acknowledgments
This work was co-financed by the Aragonese Government and the European Regional Development Fund “Construyendo Europa desde Aragón” (COSMOS research group); and by the Spanish program “Programa estatal del Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i”, project PGC2018-099815-B-100.
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Arronategui, U., Bañares, J.Á., Colom, J.M. (2023). A Framework to Support Decision-Making Based on AI and Simulation of Large-Scale Models. In: Bañares, J.Á., Altmann, J., Agmon Ben-Yehuda, O., Djemame, K., Stankovski, V., Tuffin, B. (eds) Economics of Grids, Clouds, Systems, and Services. GECON 2022. Lecture Notes in Computer Science, vol 13430. Springer, Cham. https://doi.org/10.1007/978-3-031-29315-3_14
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DOI: https://doi.org/10.1007/978-3-031-29315-3_14
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