Abstract
The presented work aims at defining high-level interaction paradigms to model different optimization problems which rely on negotiation and collaboration mechanisms: the models will address both Individual and Collective Intelligence implementing them by means of agent based interaction paradigms. In the Individual Intelligence, the interactions of an individual within the community are aimed at meeting the objectives of the individual, using a selfish approach; by the contrary in the Collective Intelligence the interaction of an individual with other entities of the same community, or with the external environment, is not only aimed at satisfying individual goals but also the ones of the community to which it belongs. Due to its reactivity and proactivity characteristics and for its adaptability to the environment, the agent based model is one of the most suitable paradigms that can embody and implement the aforementioned interaction paradigms. In order to validate the proposed models, the agent-based architectures are presented within different scenarios: the first case study that is used to validate the Individual Intelligence model is Cloud Computing, with particular application to IaaS level. The second case study has been used to validate Collective Intelligence model: the proposed scenario is related to Smart Cities.
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Aversa, R., Tasquier, L. (2016). Agent-Based High-Level Interaction Patterns for Modeling Individual and Collective Optimizations Problems. In: Pop, F., Kołodziej, J., Di Martino, B. (eds) Resource Management for Big Data Platforms. Computer Communications and Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-44881-7_4
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DOI: https://doi.org/10.1007/978-3-319-44881-7_4
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