The Emergence of Norms via Contextual Agreements in Open Societies

This paper explores the emergence of norms in agents' societies when agents play multiple -even incompatible- roles in their social contexts simultaneously, and have limited interaction ranges. Specifically, this article proposes two reinforcement learning methods for agents to compute agreements on strategies for using common resources to perform joint tasks. The computation of norms by considering agents' playing multiple roles in their social contexts has not been studied before. To make the problem even more realistic for open societies, we do not assume that agents share knowledge on their common resources. So, they have to compute semantic agreements towards performing their joint actions. %The paper reports on an empirical study of whether and how efficiently societies of agents converge to norms, exploring the proposed social learning processes w.r.t. different society sizes, and the ways agents are connected. The results reported are very encouraging, regarding the speed of the learning process as well as the convergence rate, even in quite complex settings

An optimal rewiring strategy for cooperative multiagent social learning

Multiagent coordination is a key problem in cooperative multiagent systems (MASs). It has been widely studied in both fixed-agent repeated interaction setting and static social learning framework. However, two aspects of dynamics in real-world MASs are currently neglected. First, the network topologies can change during the course of interaction dynamically. Second, the interaction utilities can be different among each pair of agents and usually unknown before interaction. Both issues mentioned above increase the difficulty of coordination. In this paper, we consider the multiagent social learning in a dynamic environment in which agents can alter their connections and interact with randomly chosen neighbors with unknown utilities beforehand. We propose an optimal rewiring strategy to select most beneficial peers to maximize the accumulated payoffs in long-run interactions. We empirically demonstrate the effects of our approach in a variety of large-scale MASs

Automating decision making to help establish norm-based regulations

Norms have been extensively proposed as coordination mechanisms for both agent and human societies. Nevertheless, choosing the norms to regulate a society is by no means straightforward. The reasons are twofold. First, the norms to choose from may not be independent (i.e, they can be related to each other). Second, different preference criteria may be applied when choosing the norms to enact. This paper advances the state of the art by modeling a series of decision-making problems that regulation authorities confront when choosing the policies to establish. In order to do so, we first identify three different norm relationships -namely, generalisation, exclusivity, and substitutability- and we then consider norm representation power, cost, and associated moral values as alternative preference criteria. Thereafter, we show that the decision-making problems faced by policy makers can be encoded as linear programs, and hence solved with the aid of state-of-the-art solvers

Destabilising conventions using temporary interventions

Conventions are an important concept in multi-agent systems as they allow increased coordination amongst agents and hence a more efficient system. Encouraging and directing convention emergence has been the focus of much research, particularly through the use of fixed strategy agents. In this paper we apply temporary interventions using fixed strategy agents to destabilise an established convention by (i) replacing it with another convention of our choosing, and (ii) allowing it to destabilise in such a way that no other convention explicitly replaces it. We show that these interventions are effective and investigate the minimum level of intervention needed

Strategies for cooperation emergence in distributed service discovery

This is an Accepted Manuscript of an article published by Taylor & Francis in Cybernetics and Systems on APR 3 2014], available online:http://www.tandfonline.com/10.1080/01969722.2014.894848[EN] In distributed environments where entities only have a partial view of the system, cooperation plays a key issue. In the case of decentralized service discovery in open agent societies, agents only know about the services they provide and who are their direct neighbors. Therefore, they need the cooperation of their neighbors in order to locate the required services. However, cooperation is not always present in open systems. Non-cooperative agents pursuing their own goals could refuse to forward queries from other agents to avoid the cost of this action; therefore, the efficiency of the decentralized service discovery could be seriously damaged. In this paper, we propose the ombination of incentives and local structural changes in order to promote cooperation in the service discovery process. The results show that, even in scenarios where the predominant behavior is not collaborative cooperation emerges.The work was partially supported by the Spanish Ministry of Science and Innovation through grants TIN2009-13839-C03-01, TIN2012-36586-C03-01, CSD2007-0022 (CONSOLIDER-INGENIO 2010).Del Val Noguera, E.; Rebollo Pedruelo, M.; Botti, V. (2014). Strategies for cooperation emergence in distributed service discovery. Cybernetics and Systems. 45(3):220-240. https://doi.org/10.1080/01969722.2014.894848S220240453Blanc , A. , Y.K. Liu , and A. Vahdat . “Designing Incentives for Peer-to-Peer Routing.” InProceedings of the 24th Annual Joint Conference of the IEEE Computer and Communications Societies, Vol. 1, pp. 374–385, 2005 .del Val , E. “Semantic Service Management in Service-Oriented Multi-Agent Systems.” Ph.D. thesis, Departament de Sistemes Informàtics i Computació, Universitat Politècnica de València, 2013 .Del Val, E., Rebollo, M., & Botti, V. (2012). Enhancing decentralized service discovery in open service-oriented multi-agent systems. Autonomous Agents and Multi-Agent Systems, 28(1), 1-30. doi:10.1007/s10458-012-9210-0DORAN, J. E., FRANKLIN, S., JENNINGS, N. R., & NORMAN, T. J. (1997). On cooperation in multi-agent systems. The Knowledge Engineering Review, 12(3), 309-314. doi:10.1017/s0269888997003111Eguíluz, V. M., Zimmermann, M. G., Cela‐Conde, C. J., & Miguel, M. S. (2005). Cooperation and the Emergence of Role Differentiation in the Dynamics of Social Networks. American Journal of Sociology, 110(4), 977-1008. doi:10.1086/428716Griffiths , N. and M. Luck . “Changing Neighbours: Improving Tag-Based Cooperation.” InProceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1.(AAMAS'10), 249–256. Richland, SC: International Foundation for Autonomous Agents and Multiagent Systems, 2010 .Gu , B. and S. Jarvenpaa . “Are Contributions to p2p Technical Forums Private or Public Goods? An Empirical Investigation.” Paper presented at the 1st Workshop on Economics of Peer-to-Peer Systems, June 4–5, 2004, Harvard University .Hauert, C., Traulsen, A., Brandt, H., Nowak, M. A., & Sigmund, K. (2007). Via Freedom to Coercion: The Emergence of Costly Punishment. Science, 316(5833), 1905-1907. doi:10.1126/science.1141588Hofmann , L.M. , N. Chakraborty , and K. Sycara . “The Evolution of Cooperation in Self-Interested Agent Societies: A Critical Study.” InProceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems, Volume 2 , edited by K. Tumer , P. Yolum , L. Sonenberg , and P. Stone , 685–692. IFAAMAS, 2011 .Lin, W. S., Zhao, H. V., & Liu, K. J. R. (2009). Incentive Cooperation Strategies for Peer-to-Peer Live Multimedia Streaming Social Networks. IEEE Transactions on Multimedia, 11(3), 396-412. doi:10.1109/tmm.2009.2012915Nowak, M. A. (2006). Five Rules for the Evolution of Cooperation. Science, 314(5805), 1560-1563. doi:10.1126/science.1133755Nowak, M. A., & Sigmund, K. (1998). Evolution of indirect reciprocity by image scoring. Nature, 393(6685), 573-577. doi:10.1038/31225Ohtsuki, H., Hauert, C., Lieberman, E., & Nowak, M. A. (2006). A simple rule for the evolution of cooperation on graphs and social networks. Nature, 441(7092), 502-505. doi:10.1038/nature04605Santos, F. C., Santos, M. D., & Pacheco, J. M. (2008). Social diversity promotes the emergence of cooperation in public goods games. Nature, 454(7201), 213-216. doi:10.1038/nature06940Shneidman , J. and D. C. Parkes . “Rationality and Self-Interest in Peer to Peer Networks.” Paper presented at the 2nd Int. Workshop on Peer-to-Peer Systems (IPTPS’03), February 20–21, 2003, Berkeley, CA .Sigmund, K. (2007). Punish or perish? Retaliation and collaboration among humans. Trends in Ecology & Evolution, 22(11), 593-600. doi:10.1016/j.tree.2007.06.012Sigmund, K. (2009). Sympathy and similarity: The evolutionary dynamics of cooperation. Proceedings of the National Academy of Sciences, 106(21), 8405-8406. doi:10.1073/pnas.0903947106Sigmund, K., Hauert, C., & Nowak, M. A. (2001). Reward and punishment. Proceedings of the National Academy of Sciences, 98(19), 10757-10762. doi:10.1073/pnas.161155698Sun , Q. and H. Garcia-Molina . “Slic: A Selfish Link-Based Incentive Mechanism for Unstructured Peer-To-Peer Networks.” Paper presented at the 24th International Conference on Distributed Computing Systems (ICDCS’04), March 23–26, 2004, Washington, DC .Villatoro , D. , J. Sabater-Mir , and S. Sen . “Social Instruments for Robust Convention Emergence.”Proceedings of the International Joint Conference on Artificial Intelligence, edited by T. Walsh, 420–425, 2011

Automated synthesis of norms in social networks

We introduce a legislation mechanism, capable of automatically synthesise norms from a multi-agent domain, to synthesise norms in a participatory manner, namely desmon. Moreover, we have created an agent-based on-line community simulator to model the interactions within their users

Supporting cooperation and coordination in open multi-agent systems

Cooperation and coordination between agents are fundamental processes for increasing aggregate and individual benefit in open Multi-Agent Systems (MAS). The increased ubiquity, size, and complexity of open MAS in the modern world has prompted significant research interest in the mechanisms that underlie cooperative and coordinated behaviour. In open MAS, in which agents join and leave freely, we can assume the following properties: (i) there are no centralised authorities, (ii) agent authority is uniform, (iii) agents may be heterogeneously owned and designed, and may consequently have con icting intentions and inconsistent capabilities, and (iv) agents are constrained in interactions by a complex connecting network topology. Developing mechanisms to support cooperative and coordinated behaviour that remain effective under these assumptions remains an open research problem. Two of the major mechanisms by which cooperative and coordinated behaviour can be achieved are (i) trust and reputation, and (ii) norms and conventions. Trust and reputation, which support cooperative and coordinated behaviour through notions of reciprocity, are effective in protecting agents from malicious or selfish individuals, but their capabilities can be affected by a lack of information about potential partners and the impact of the underlying network structure. Regarding conventions and norms, there are still a wide variety of open research problems, including: (i) manipulating which convention or norm a population adopts, (ii) how to exploit knowledge of the underlying network structure to improve mechanism efficacy, and (iii) how conventions might be manipulated in the middle and latter stages of their lifecycle, when they have become established and stable. In this thesis, we address these issues and propose a number of techniques and theoretical advancements that help ensure the robustness and efficiency of these mechanisms in the context of open MAS, and demonstrate new techniques for manipulating convention emergence in large, distributed populations. Specfically, we (i) show that gossiping of reputation information can mitigate the detrimental effects of incomplete information on trust and reputation and reduce the impact of network structure, (ii) propose a new model of conventions that accounts for limitations in existing theories, (iii) show how to manipulate convention emergence using small groups of agents inserted by interested parties, (iv) demonstrate how to learn which locations in a network have the greatest capacity to in uence which convention a population adopts, and (v) show how conventions can be manipulated in the middle and latter stages of the convention lifecycle