Abstract
Multi-agent Systems (MAS) offer an alternative to handling large quantities of data with the added advantage that control is not centralized, and consequently, such systems are endowed with robustness and versatility. This paper describes a Multi-agent System for Automated Clustering with self-optimization (MASAC). The framework comprises six categories of agents: information updater agent, document uploader agent, parser agent, convertor agent, clustering agent, and subset extractor agent. A novelty and feature of MASAC is that it supports self-optimization allowing for the enhancement of the initial clusters configuration in real time, and not only after running a cluster validation agent, as in other MAS presented in the literature.
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Kadar, M., Muntean, M.V., Csabai, T. (2020). A Multi-agent System with Self-optimization for Automated Clustering (MASAC). In: Jezic, G., Chen-Burger, YH., Kusek, M., Šperka, R., Howlett, R., Jain, L. (eds) Agents and Multi-agent Systems: Technologies and Applications 2019. Smart Innovation, Systems and Technologies, vol 148. Springer, Singapore. https://doi.org/10.1007/978-981-13-8679-4_10
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DOI: https://doi.org/10.1007/978-981-13-8679-4_10
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