Studying the Impact of Negotiation Environments on Negotiation Teams' Performance

[EN] In this article we study the impact of the negotiation environment on the performance of several intra-team strategies (team dynamics) for agent-based negotiation teams that negotiate with an opponent. An agent-based negotiation team is a group of agents that joins together as a party because they share common interests in the negotiation at hand. It is experimentally shown how negotiation environment conditions like the deadline of both parties, the concession speed of the opponent, similarity among team members, and team size affect performance metrics like the minimum utility of team members, the average utility of team members, and the number of negotiation rounds. Our goal is identifying which intra-team strategies work better in different environmental conditions in order to provide useful knowledge for team members to select appropriate intra-team strategies according to environmental conditions.This work is supported by TIN2011-27652-C03-01, TIN2009-13839-C03-01, CSD2007-00022 of the Spanish Government, and FPU Grant AP2008-00600 awarded to Victor Sanchez-Anguix. We would also like to thank anonymous reviewers and assistants of AAMAS 2011 who helped us to improve our previous work, making this present work possible.Sanchez-Anguix, V.; Julian Inglada, VJ.; Botti, V.; García-Fornes, A. (2013). Studying the impact of negotiation environments on negotiation teams' performance. Information Sciences. 219:17-40. https://doi.org/10.1016/j.ins.2012.07.017S174021

Supporting Dynamicity in Emergency Response Applications

Multiagent Systems are a promising paradigm for software development. It is feasible to model such systems with many components where each one can solve a specific problem. This division of responsibilities allows multiagent systems to work in dynamically changing environments. An example of an environment that is very changeable is related with emergencies management. Emergency management systems depend on the cooperation of all their components due to their specialization. In order to obtain this cooperation, the components need to interact with each other and adapt their interactions depending on their purpose and the system components they are interacting with. Also, new components may arrive on the scene, which must be informed about the interaction policies that original components are using. Although Multiagent Systems are suited to managing scenarios of this kind, their effectiveness depends on their capacity to dynamically modify and adapt the protocols that control the interactions among agents in the system. In this paper, an infrastructure to support dynamically changing interaction protocols is presented

Argumentation for multi-party privacy management

Social network services enable users to share large quantities of private information. Often, the shared information concerns individuals who are members of the social network but did not upload the information to the service. In such situations, inappropriate sharing preferences can cause conflict and threaten users’ privacy. Since related studies suggest that users prefer to solve multi-party privacy conflicts through negotiation, we introduce a novel approach based on negotiation through arguments. In our approach, users propose privacy settings and support their proposals with logical arguments. The final decision is based on a setting supported by sound arguments

BFF: A Tool for Eliciting Tie Strength and User Communities in Social Networking Services

López Fogués, R.; Such Aparicio, JM.; Espinosa Minguet, AR.; García-Fornes, A. (2014 Abstract The use of social networking services (SNSs) such as Facebook has explosively grown in the last few years. Users see these SNSs as useful tools to find friends and interact with them. Moreover, SNSs allow their users to share photos, videos, and express their thoughts and feelings. However, users are usually concerned about their privacy when using SNSs. This is because the public image of a subject can be affected by photos or comments posted on a social network. In this way, recent studies demonstrate that users are demanding better mechanisms to protect their privacy. An appropriate approximation to solve this could be a privacy assistant software agent that automatically suggests a privacy policy for any item to be shared on a SNS. The first step for developing such an agent is to be able to elicit meaningful information that can lead to accurate privacy policy predictions. In particular, the information needed is user communities and the strength of users' relationships, which, as suggested by recent empirical evidence, are the most important factors that drive disclosure in SNSs. Given the number of friends that users can have and the number of communities they may be involved on, it is infeasible that users are able to provide this information without the whole eliciting process becoming confusing and time consuming. In this work, we present a tool called Best Friend Forever (BFF) that automatically classifies the friends of a user in communities and assigns a value to the strength of the relationship ties to each one. We also present an experimental evaluation involving 38 subjects that showed that BFF can significantly alleviate the burden of eliciting communities and relationship strength

BFF: A tool for eliciting tie strength and user communities in social networking services

The final publication is available at Springer via http://dx.doi.org/ 10.1007/s10796-013-9453-6The use of social networking services (SNSs) such as Facebook has explosively grown in the last few years. Users see these SNSs as useful tools to find friends and interact with them. Moreover, SNSs allow their users to share photos, videos, and express their thoughts and feelings. However, users are usually concerned about their privacy when using SNSs. This is because the public image of a subject can be affected by photos or comments posted on a social network. In this way, recent studies demonstrate that users are demanding better mechanisms to protect their privacy. An appropriate approximation to solve this could be a privacy assistant software agent that automatically suggests a privacy policy for any item to be shared on a SNS. The first step for developing such an agent is to be able to elicit meaningful information that can lead to accurate privacy policy predictions. In particular, the information needed is user communities and the strength of users' relationships, which, as suggested by recent empirical evidence, are the most important factors that drive disclosure in SNSs. Given the number of friends that users can have and the number of communities they may be involved on, it is infeasible that users are able to provide this information without the whole eliciting process becoming confusing and time consuming. In this work, we present a tool called Best Friend Forever (BFF) that automatically classifies the friends of a user in communities and assigns a value to the strength of the relationship ties to each one. We also present an experimental evaluation involving 38 subjects that showed that BFF can significantly alleviate the burden of eliciting communities and relationship strength.This work has been partially supported by CONSOLIDER-INGENIO 2010 under grant CSD2007-00022, and TIN 2008-04446 and PROMETEO II/2013/019 projects. This article has been developed as a result of a mobility stay funded by the Erasmus Mundus Programme of the European Comission under the Transatlantic Partnership for Excellence in Engineering - TEE Project.López Fogués, R.; Such Aparicio, JM.; Espinosa Minguet, AR.; García-Fornes, A. (2014). BFF: A tool for eliciting tie strength and user communities in social networking services. Information Systems Frontiers. 16:225-237. https://doi.org/10.1007/s10796-013-9453-6S22523716Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008.Boyd, D., & Hargittai, E. (2010). Facebook privacy settings: who cares? First Monday, 15(8).Burt, R. (1995). Structural holes: the social structure of competition. Harvard University Pr.Culotta, A., Bekkerman, R., McCallum, A. (2004). 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(2005). Information revelation and privacy in online social networks. In Proceedings of the 2005 ACM workshop on privacy in the electronic society (pp. 71–80). ACM.Johnson, M., Egelman, S., Bellovin, S. (2012). Facebook and privacy: it’s complicated. In Proceedings of the eighth symposium on usable privacy and security (p. 9). ACM .Kahanda, I., & Neville, J. (2009). Using transactional information to predict link strength in online social networks. In Proceedings of the third international conference on weblogs and social media (ICWSM).Lancichinetti, A., & Fortunato, S. (2009). Community detection algorithms: a comparative analysis. Physical Review E, 80, 056–117.Lancichinetti, A., Fortunato, S., Kertsz, J. (2009). Detecting the overlapping and hierarchical community structure in complex networks. New Journal of Physics, 11(3), 033–015.Lin, N., Ensel, W., Vaughn, J. (1981). Social resources and strength of ties: Structural factors in occupational status attainment. 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The personality of popular facebook users. In Proceedings of the ACM 2012 conference on computer supported cooperative work (CSCW’12).Rosvall, M., & Bergstrom, C. (2008). Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences, 105(4), 1118–1123.Sharma, G., Qiang, Y., Wenjun, S., Qi, L. (2013). Communication in virtual world: Second life and business opportunities. Information Systems Frontiers, 15(4), 677–694.Shen, K., Song, L., Yang, X., Zhang, W. (2010). A hierarchical diffusion algorithm for community detection in social networks. In 2010 international conference on cyber-enabled distributed computing and knowledge discovery (CyberC) (pp. 276–283). IEEE.Sierra, C., & Debenham, J. (2007). The LOGIC negotiation model. In AAMAS ’07: proceedings of the 6th international joint conference on autonomous agents and multiagent systems (pp. 1–8). ACM.Staddon, J., Huffaker, D., Brown, L., Sedley, A. (2012). Are privacy concerns a turn-off?: engagement and privacy in social networks. In Proceedings of the eighth symposium on usable privacy and security (p. 10). ACM.Strater, K., & Lipford, H.R. (2008). Strategies and struggles with privacy in an online social networking community. In Proceedings of the 22nd British HCI group annual conference on people and computers: culture, creativity, interaction, BCS-HCI ’08 (Vol. 1, pp. 111–119). Swinton: British Computer Society.Wellman, B., & Wortley, S. (1990). Different strokes from different folks: Community ties and social support. American Journal of Sociology, 558–588.Wiese, J., Kelley, P., Cranor, L., Dabbish, L., Hong, J., Zimmerman, J. (2011). Are you close with me? are you nearby? investigating social groups, closeness, and willingness to share. In Proceedings of the 13th international conference on Ubiquitous computing (pp. 197–206). ACM.Xiang, R., Neville, J., Rogati, M. (2010). Modeling relationship strength in online social networks. 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Challenges for adaptation in agent societies

The final publication is available at Springer via http://dx.doi.org/[insert DOIAdaptation in multiagent systems societies provides a paradigm for allowing these societies to change dynamically in order to satisfy the current requirements of the system. This support is especially required for the next generation of systems that focus on open, dynamic, and adaptive applications. In this paper, we analyze the current state of the art regarding approaches that tackle the adaptation issue in these agent societies. We survey the most relevant works up to now in order to highlight the most remarkable features according to what they support and how this support is provided. In order to compare these approaches, we also identify different characteristics of the adaptation process that are grouped in different phases. Finally, we discuss some of the most important considerations about the analyzed approaches, and we provide some interesting guidelines as open issues that should be required in future developments.This work has been partially supported by CONSOLIDER-INGENIO 2010 under grant CSD2007-00022, the European Cooperation in the field of Scientific and Technical Research IC0801 AT, and projects TIN2009-13839-C03-01 and TIN2011-27652-C03-01.Alberola Oltra, JM.; Julian Inglada, VJ.; García-Fornes, A. (2014). Challenges for adaptation in agent societies. Knowledge and Information Systems. 38(1):1-34. https://doi.org/10.1007/s10115-012-0565-yS134381Aamodt A, Plaza E (1994) Case-based reasoning; foundational issues, methodological variations, and system approaches. AI Commun 7(1):39–59Abdallah S, Lesser V (2007) Multiagent reinforcement learning and self-organization in a network of agents. 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An Agent Infrastructure for Privacy-Enhancing Agent-Based E-commerce Applications

Privacy is of crucial importance in order for agent-based e-commerce applications to be of broad use. Privacy can be enhanced minimizing data identifiability, i.e., the degree by which personal information can be directly attributed to a particular individual. However, minimizing data identifiability may directly impact other crucial issues in agent-based e-commerce, such as accountability, trust, and reputation. In this paper, we present an agent infrastructure for agent-based e-commerce applications. This agent infrastructure enhances privacy without compromising accountability, trust, and reputation

Los foraminiferos de ls depositos lacustres Miocenos de la cubeta de Azuara (Margen Meridional de la depresión del Ebro, Zaragoza)

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