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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aversa, R., Tasquier, L., Venticinque, S.: Cloud agency: A guide through the clouds. Mondo Digitale 13(49) (2014)
Barbati, M., Bruno, G., Genovese, A.: Applications of agent-based models for optimization problems: A literature review. Expert Syst. Appl. 39(5), 6020–6028 (2012)
Besanko, D., Braeutigam, R.: Microeconomics. John Wiley & Sons (2010)
Buyya, R., Yeo, C.S., Venugopal, S., Broberg, J., Brandic, I.: Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility. Future Gener. Comput. Syst. 25(6), 599–616 (2009)
Caragliu, A., Del Bo, C., Nijkamp, P.: Smart cities in Europe. J. urban Technol. 18(2), 65–82 (2011)
Davidsson, P., Persson, J.A., Holmgren, J.: On the integration of agent-based and mathematical optimization techniques. In: Agent and multi-agent systems: technologies and applications, pp. 1–10. Springer (2007)
Durfee, E.H.: Coordination of distributed problem solvers. Kluwer Academic Publishers (1988)
Kornienko, S., Kornienko, O., Priese, J.: Application of multi-agent planning to the assignment problem. Comput. Ind. 54(3), 273–290 (2004)
Kozat, U.C., Harmanci, O., Kanumuri, S., Demircin, M.U., Civanlar, M.R.: Peer assisted video streaming with supply-demand-based cache optimization. IEEE Trans. Multimedia 11(3), 494–508 (2009)
Palmieri, F., Buonanno, L., Venticinque, S., Aversa, R., Di Martino, B.: A distributed scheduling framework based on selfish autonomous agents for federated cloud environments. Future Gener. Comput. Syst. 29(6), 1461–1472 (2013)
Rosenschein, J.S., Zlotkin, G.: Designing conventions for automated negotiation. AI magazine 15(3), 29 (1994)
Shen, W., Wang, L., Hao, Q.: Agent-based distributed manufacturing process planning and scheduling: a state-of-the-art survey. IEEE Trans. Syst. Man Cybern. Part C: Appl. Rev. 36(4), 563–577 (2006)
Tasquier, L., Aversa, R.: An agent-based collaborative platform for the optimized trading of renewable energy within a community. J. Telecommun. Inf. Technol. 2014(4) (2014)
Venticinque, S., Tasquier, L., Di Martino, B.: Agents based cloud computing interface for resource provisioning and management. In: 2012 Sixth International Conference on Complex, Intelligent and Software Intensive Systems (CISIS), pp. 249–256. IEEE (2012)
Venticinque, S., Tasquier, L., Di Martino, B.: A restfull interface for scalable agents based cloud services. Int. J. Ad Hoc Ubiquitous Comput. 16(4), 219–231 (2014)
Wooldridge, M.: An introduction to multiagent systems. John Wiley & Sons (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this chapter
Cite this chapter
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-44881-7_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-44880-0
Online ISBN: 978-3-319-44881-7
eBook Packages: Computer ScienceComputer Science (R0)