SeisFinder : A Web Application for Extraction of Data from Computationally Intensive Earthquake Resilience Calculations

Overview of SeisFinder SeisFinder is an open-source web service developed by QuakeCoRE and the University of Canterbury, focused on enabling the extraction of output data from computationally intensive earthquake resilience calculations. Currently, SeisFinder allows users to select historical or future events and retrieve ground motion simulation outputs for requested geographical locations. This data can be used as input for other resilience calculations, such as dynamic response history analysis. SeisFinder was developed using Django, a high-level python web framework, and uses a postgreSQL database. Because our large-scale computationally-intensive numerical ground motion simulations produce big data, the actual data is stored in file systems, while the metadata is stored in the database. The basic SeisFinder architecture is shown in Figure 1

Utility-Based Mechanism for Structural Self-Organization in Service-Oriented MAS

Structural relations established among agents influence the performance of decentralized service discovery process in multiagent systems. Moreover, distributed systems should be able to adapt their structural relations to changes in environmental conditions. In this article, we present a service-oriented multiagent systems, where agents initially self-organize their structural relations based on the similarity of their services. During the service discovery process, agents integrate a mechanism that facilitates the self-organization of their structural relations to adapt the structure of the system to the service demand. This mechanism facilitates the task of decentralized service discovery and improves its performance. Each agent has local knowledge about its direct neighbors and the queries received during discovery processes. With this information, an agent is able to analyze its structural relations and decide when it is more appropriate to modify its direct neighbors and select the most suitable acquaintances to replace them. The experimental evaluation shows how this self-organization mechanism improves the overall performance of the service discovery process in the system when the service demand changesThis work is partially supported by the Spanish Ministry of Science and Innovation through grants CSD2007-0022 (CONSOLIDER-INGENIO 2010), TIN2012-36586-C03-01, TIN2012-36586-C03-01, TIN2012-36586-C03-02, PROMETEOII/2013/019, and FPU grant AP-2008-00601 awarded to E. Del Val.Del Val Noguera, E.; Rebollo Pedruelo, M.; Vasirani, M.; Fernández, A. (2014). Utility-Based Mechanism for Structural Self-Organization in Service-Oriented MAS. ACM Transactions on Autonomous and Adaptive Systems. 9(3):1-24. https://doi.org/10.1145/2651423S12493Sherief Abdallah and Victor Lesser. 2007. 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Socially-Inspired Mechanisms for Restricting Exploitation in Artificial Agent Societies

Human societies have long cultivated the ability to organise themselves into groups and have also established formal or informal rules of behaviour that are expected within these groups. In the field of multi-agent systems, researchers are inspired by this ability of human societies to form groups and establish social control, and they have applied them to solve some of the problems in artificial agent societies. One of the problems in artificial agent societies is the problem of non-cooperation, where individuals have motivations for not cooperating with other agents. An example of non-cooperation is the issue of freeriding, where some agents do not contribute to the welfare of the society but do consume valuable resources. This can be likened to the “commons” problem. The way to address this problem is by imposing strict rules by centralised institutions. However, centralised solutions suffer from performance bottlenecks, and their scalability is poor. Towards this end, our first objective of this thesis is to investigate decentralised mechanisms for facilitating social control in agent societies. Our second objective is associated with an important attribute of modern artificial societies, which is the openness of such societies. Agents may join and/or leave these societies at any time. Towards this end, our second objective of this thesis is to investigate mechanisms which can handle the dynamism of open agent societies. Another key aspect in facilitating social control lies in employing appropriate mechanisms that can facilitate such control. In this thesis we are inspired by decentralised social practices found in human societies. This thesis investigates mechanisms that contribute towards the formation (via self-organisation) of different groups in an agent society based on their cooperativeness. It demonstrates that these mechanisms help in achieving the separation of good agents (cooperators) from bad agents (noncooperators) without expelling them from the society. It demonstrates how the concepts of tags can be used for group formation and how the information about the cooperativeness of agents in the society can be spread based on using socially-inspired mechanisms. It also investigates how monitoring and control mechanisms such as referrals, voting, gossip, resource restriction, and ostracism can be used in artificial agent societies. Thus our focus of this thesis is to develop socially-inspired mechanisms to facilitate self-organisation of groups in agent societies to restrict exploitation. We demonstrate that the formation of groups shield “bad” agents from taking advantage of “good” agents. We also demonstrate that the society is better off if the groups are organised based on their cooperativeness. Overall, the goal of this thesis is to investigate and demonstrate the new socially-inspired mechanisms for the self-organisation of groups in open, decentralized agent societies. This thesis initially systematically explores closed, centralized societies and gradually moves on to open, decentralised societies, since many real-life societies lie somewhere between these two ends of the open spectrum, with more and more societies lying closer to the end of full openness. We believe the mechanisms explored in this thesis can be applied to open, decentralized agent societies, such as electronic file-sharing societies to help avoid the problem of freeriding. The mechanisms proposed in this thesis could also be applied to organise agents into groups based on their behaviour, in virtual worlds and other online communities

Tag based model for knowledge sharing in agent society

In this paper we discuss a tag-based model that facilitates knowledge sharing in the context of agents playing the knowledge sharing game. Sharing the knowledge incurs a cost for the sharing agent, and thus non-sharing is the preferred option for selfish agents. Through agent-based simulations we show that knowledge sharing is possible even in the presence of non-sharing agents in the population. We also show that the performance of an agent society can be better when some agents bear the cost of sharing instead of the whole group sharing the cost.Unpublished[1] Holland, J.H.: The Effect of Labels (Tags) on Social interactions. Vol. SFI Working Paper 93-10-064, Santa Fe Institute, Santa Fe, NM (1993) [2] YouTube, www.youtube.com [3] CiteSeer, http://citeseer.ist.psu.edu [4] Riolo, R.L., M.D. Cohen, and R. Axelrod.: Cooperation without Reciprocity. Nature 414, 2001: pp. 441--443 (2001). [5] Hales, D.: Evolving Specialisation, Altruism and Group-Level Optimisation Using Tags. Multi-Agent-Based Simulation II: Third International Workshop, MABS 2002, Bologna, Italy, July 15-16, 2002, Vol. 2581, Lecture notes in computer science, pp. 26--35, Springer Berlin / Heidelberg (2003) [6] Hales, D.: Tag Based Co-operation in Artificial Societies. Ph.D. Thesis, Department of Computer Science, University of Essex, UK, 2001. [7] Folksonomy , http://en.wikipedia.org/wiki/Folksonomy [8] Riolo, R.L.: The Effects of Tag-Mediated Selection of Partners in Evolving Populations Playing the Iterated Prisoner's Dilemma. 1997, Santa Fe Institute. [9] Nowak, M.A. and K. Sigmund.: Evolution of indirect reciprocity by image scoring, Nature vol. 393, pp. 573--577 (1998) [10] Trivers, R: The Evolution of Reciprocal Altruism, Quarterly Review of Biology 46 pp.35-57 (1971) [11] Hamilton, W. D.: The genetical evolution of social behaviour. I, Journal of Theoretical Biology, 1964 Jul; 7(1):1-16. [12] Savarimuthu, S., Purvis, M. A., Purvis, M. K., “Altruistic Sharing using Tags”, Proceedings of the 6th International Workshop on Agents and Peer-to-Peer Computing, Estoril, Portugal, May 2008 (in press). [13] Németh, A. and K. Takács.: The Evolution of Proximity Based Altruism, Department of Sociology and Social Policy, 2006, Corvinus University of Budapest, Budapest. [14] Clutten-Brock, T. H., and Parker, G. A.: Punishment in animal societies. Nature 373 (1995), 209 – 216. [15] Savarimuthu, S., Purvis, M. A., Purvis, M. K., “Emergence of Sharing Behavior in a Multi-agent Society using Tags”, Proceedings of IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2008), Sydney, Australia, December 2008 (in press)

Tag based model for knowledge sharing in agent society

In this paper we discuss a tag-based model that facilitates knowledge sharing in the context of agents playing the knowledge sharing game. Sharing the knowledge incurs a cost for the sharing agent, and thus non-sharing is the preferred option for selfish agents. Through agent-based simulations we show that knowledge sharing is possible even in the presence of non-sharing agents in the population. We also show that the performance of an agent society can be better when some agents bear the cost of sharing instead of the whole group sharing the cost.Unpublished[1] Holland, J.H.: The Effect of Labels (Tags) on Social interactions. Vol. SFI Working Paper 93-10-064, Santa Fe Institute, Santa Fe, NM (1993) [2] YouTube, www.youtube.com [3] CiteSeer, http://citeseer.ist.psu.edu [4] Riolo, R.L., M.D. Cohen, and R. Axelrod.: Cooperation without Reciprocity. Nature 414, 2001: pp. 441--443 (2001). [5] Hales, D.: Evolving Specialisation, Altruism and Group-Level Optimisation Using Tags. Multi-Agent-Based Simulation II: Third International Workshop, MABS 2002, Bologna, Italy, July 15-16, 2002, Vol. 2581, Lecture notes in computer science, pp. 26--35, Springer Berlin / Heidelberg (2003) [6] Hales, D.: Tag Based Co-operation in Artificial Societies. Ph.D. Thesis, Department of Computer Science, University of Essex, UK, 2001. [7] Folksonomy , http://en.wikipedia.org/wiki/Folksonomy [8] Riolo, R.L.: The Effects of Tag-Mediated Selection of Partners in Evolving Populations Playing the Iterated Prisoner's Dilemma. 1997, Santa Fe Institute. [9] Nowak, M.A. and K. Sigmund.: Evolution of indirect reciprocity by image scoring, Nature vol. 393, pp. 573--577 (1998) [10] Trivers, R: The Evolution of Reciprocal Altruism, Quarterly Review of Biology 46 pp.35-57 (1971) [11] Hamilton, W. D.: The genetical evolution of social behaviour. I, Journal of Theoretical Biology, 1964 Jul; 7(1):1-16. [12] Savarimuthu, S., Purvis, M. A., Purvis, M. K., “Altruistic Sharing using Tags”, Proceedings of the 6th International Workshop on Agents and Peer-to-Peer Computing, Estoril, Portugal, May 2008 (in press). [13] Németh, A. and K. Takács.: The Evolution of Proximity Based Altruism, Department of Sociology and Social Policy, 2006, Corvinus University of Budapest, Budapest. [14] Clutten-Brock, T. H., and Parker, G. A.: Punishment in animal societies. Nature 373 (1995), 209 – 216. [15] Savarimuthu, S., Purvis, M. A., Purvis, M. K., “Emergence of Sharing Behavior in a Multi-agent Society using Tags”, Proceedings of IEEE/WIC/ACM International Conference on Intelligent Agent Technology (IAT 2008), Sydney, Australia, December 2008 (in press)