Abstract: Despite the fact that social computation systems involve interaction mechanisms that closely resemble well-known models of agent coordination, current applications in this area make little or no use of the techniques the agent-based systems literature has to offer. In order to bridge this gap, this paper proposes a data-driven method for defining and deploying agent interaction protocols that is entirely based on using the standard architecture of the World Wide Web. This obviates the need of bespoke message passing mechanisms and agent platforms, thereby facilitating the use of agent coordination principles in standard Web-based applications. We describe a prototypical implementation of the architecture and experimental results that prove it can deliver the scalability and robustness required of modern social computation applications while maintaining the expressiveness and versatility of agent interaction protocols.

Citation: M. Rovatsos, D. Diochnos, and M. Craciun. Agent Protocols for Social Computation. Advances in Social Computing and Multiagent Systems, CCIS 541, Springer, 2015.

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Abstract: Society is moving towards a socio-technical ecosystem in which physical and virtual dimensions of life are intertwined and where people interactions ever more take place with or are mediated by machines. Hybrid Diversity-aware Collective Adaptive Systems (HDA-CAS) is a new generation of sociotechnical systems where humans and machines synergetically complement each other and operate collectively to achieve their goals. HDA-CAS introduce the fundamental properties of hybridity and collectiveness, hiding from the users the complexities associated with managing the collaboration and coordination of machine and human computing elements. In this paper we present an HDA-CAS system called SmartSociety, supporting computations with hybrid human/machine collectives. We describe the platform’s architecture and functionality, validate it on two real-world scenarios involving human and machine elements and present a performance evaluation.

Citation: O. Scekic, D. Miorandi, T. Schiavinotto, D. I. Diochnos, A. Hume, R. Chenu-Abente, H.-L. Truong, M. Rovatsos, I. Carreras, S. Dustdar, F. Giunchiglia, SmartSociety — A Platform for Collaborative People-Machine Computation, The 8th IEEE International Conference on Service Oriented Computing & Applications (SOCA’15), 19-21 October 2015, Rome, Italy.

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In a new article for FoCAS (a Coordination Action sponsored by the European Union Future and Emerging Technology Unit) Michael Rovatsos discusses the challenges, and possibilities, emerging from a new generation of users empowered as producers of services.

Read the original article for details on how SmartSociety approaches these challenges and what our plans are for the future.

Filmed at TEDxUniversityofEdinburgh at The Pleasance, Edinburgh on 21st February 2014.

Michael Rovatsos is a Senior Lecturer at the School of Informatics of the University of Edinburgh where he leads the Agents Group at the Centre for Intelligent Systems and their Applications. His past research has been in multi-agent systems. Part of the SmartSociety project, his research now involves social computation systems where humans and artificial agents work together to solve hard societal problems.

Smart Society’s M. Rovatsos has won second prize for best challenge and vision paper in AAMAS 2014!

Abstract: This paper proposes social computation, i.e. large-scale man-machine collaboration mediated by digital interaction media, as a vision for future intelligent systems, and as a new challenge for multiagent systems research. We claim that the study of social computation suggests a re-interpretation of many traditional AI endeavours, has huge potential application benefits, and presents the field of multiagent systems with novel, exciting research questions. We introduce an abstract model of social computation that helps capture some of its core research problems more precisely. We explore the potential contribution of multiagent systems technologies to the solution of these problems by exposing the close relationship between social computation and existing methods in multiagent systems. We describe how these methods could be reused in this novel application context, what methodological implications this has, and argue that the resulting cross-fertilisation will be highly beneficial for both sides.

Keywords: social computation, human-based computation, crowdsourcing, collective intelligence.

doi: http://dl.acm.org/citation.cfm?id=2617388.2617432

Citation: M. Rovatsos. Multiagent Systems for Social Computation (challenge paper), Thirteenth International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2014), May 5-9, 2014.

Download: http://bit.ly/1W9uFYU