Abstract
The development of ubiquity in computing demands more intelligence from connected devices to perform tasks better. Users usually lookout for devices that proactively aid in an environment, making decisions as themselves. Such cognitive models for hardware agents have increased in recent years. However, although numerous strategies emulate intelligent behavior in hardware, some problems are still to overcome, such as developing a hardware agents’ preference system with small computing capabilities. The present research proposes two novel cognitive preference models viable for hardware with few memory cells and small processing capacity; it also analyzes the approaches’ achieved performance.
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Acknowledgements
The authors want to thank the support from CONACYT projects 3058 Cátedras CONACyT 2014, A1-S-11012 Ciencia Básica 2017–2018, and 312397 PAACTI 2020-1. Also thank the support from TecNM Project 5797.19-P, and from Laboratorio Nacional de Tecnologías de Información (LaNTI) del TecNM/Campus ITCM.
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Castillo-Ramírez, C., Rangel-Valdez, N., Gómez-Santillán, C., Lucila Morales-Rodríguez, M., Cruz-Reyes, L., Fraire-Huacuja, H.J. (2021). Performance Analysis of Decision Aid Mechanisms for Hardware Bots Based on ELECTRE III and Compensatory Fuzzy Logic. In: Zapata-Cortes, J.A., Alor-Hernández, G., Sánchez-Ramírez, C., García-Alcaraz, J.L. (eds) New Perspectives on Enterprise Decision-Making Applying Artificial Intelligence Techniques. Studies in Computational Intelligence, vol 966. Springer, Cham. https://doi.org/10.1007/978-3-030-71115-3_10
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DOI: https://doi.org/10.1007/978-3-030-71115-3_10
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