Risk-bounded formation of fuzzy coalitions among service agents.

Cooperative autonomous agents form coalitions in order ro share and combine resources and services to efficiently respond to market demands. With the variety of resources and services provided online today, there is a need for stable and flexible techniques to support the automation of agent coalition formation in this context. This paper describes an approach to the problem based on fuzzy coalitions. Compared with a classic cooperative game with crisp coalitions (where each agent is a full member of exactly one coalition), an agent can participate in multiple coalitions with varying degrees of involvement. This gives the agent more freedom and flexibility, allowing them to make full use of their resources, thus maximising utility, even if only comparatively small coalitions are formed. An important aspect of our approach is that the agents can control and bound the risk caused by the possible failure or default of some partner agents by spreading their involvement in diverse coalitions

09201 Abstracts Collection -- Self-Healing and Self-Adaptive Systems

From May 10th 2009 to May 15th 2009 the Dagstuhl Seminar 09201 ``Self-Healing and Self-Adaptive Systems\u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar are put together in this paper. Links to extended abstracts or full papers are provided, if available. A description of the seminar topics, goals and results in general can be found in a separate document ``Executive Summary\u27\u27

Anytime Coalition Structure Generation on Scale-Free and Community Networks

We consider the coalition structure generation (CSG) problem on synergy graphs, which arises in many practical applications where communication constraints, social or trust relationships must be taken into account when forming coalitions. We propose a novel representation of this problem based on the concept of edge contraction, and an innovative branch and bound approach (CFSS), which is particularly efficient when applied to a general class of characteristic functions. This new model provides a non-redundant partition of the search space, hence allowing an effective parallelisation. We evaluate CFSS on two benchmark functions, the edge sum with coordination cost and the collective energy purchasing functions, comparing its performance with the best algorithm for CSG on synergy graphs: DyCE. The latter approach is centralised and cannot be efficiently parallelised due to the exponential memory requirements in the number of agents, which limits its scalability (while CFSS memory requirements are only polynomial). Our results show that, when the graphs are very sparse, CFSS is 4 orders of magnitude faster than DyCE. Moreover, CFSS is the first approach to provide anytime approximate solutions with quality guarantees for very large systems (i.e., with more than 2700 agents).Cerquides and Rodríguez-Aguilar are funded by projects COR (TIN2012-38876-C02-01), AT (CSD2007-0022), and the Generalitat of Catalunya grant 2009-SGR-1434. This work was supported by the EPSRC-Funded ORCHID Project EP/I011587/1Peer Reviewe

Професорові П.Ю. Гриценку шістдесят

У ці світлі осінні дні наукова спільнота святкує славний ювілей — 60-річчя директора Інституту української мови Національної академії наук України, завідувача відділу діалектології, доктора філологічних наук, професора Павла Юхимовича Гриценка

Multiagent cooperation for solving global optimization problems: an extendible framework with example cooperation strategies

This paper proposes the use of multiagent cooperation for solving global optimization problems through the introduction of a new multiagent environment, MANGO. The strength of the environment lays in itsflexible structure based on communicating software agents that attempt to solve a problem cooperatively. This structure allows the execution of a wide range of global optimization algorithms described as a set of interacting operations. At one extreme, MANGO welcomes an individual non-cooperating agent, which is basically the traditional way of solving a global optimization problem. At the other extreme, autonomous agents existing in the environment cooperate as they see fit during run time. We explain the development and communication tools provided in the environment as well as examples of agent realizations and cooperation scenarios. We also show how the multiagent structure is more effective than having a single nonlinear optimization algorithm with randomly selected initial points

Multi-Agent Systems

[EN] With the current advance of technology, agent-based applications are becoming a standard in a great variety of domains such as e-commerce, logistics, supply chain management, telecommunications, healthcare, and manufacturing. Another reason for the widespread interest in multi-agent systems is that these systems are seen as a technology and a tool that helps in the analysis and development of new models and theories in large-scale distributed systems or in human-centered systems. This last aspect is currently of great interest due to the need for democratization in the use of technology that allows people without technical preparation to interact with the devices in a simple and coherent way. In this Special Issue, different interesting approaches that advance this research discipline have been selected and presented.Julian Inglada, VJ.; Botti V. (2019). Multi-Agent Systems. Applied Sciences. 9(7):1-7. https://doi.org/10.3390/app9071402S1797Kravari, K., & Bassiliades, N. (2015). A Survey of Agent Platforms. Journal of Artificial Societies and Social Simulation, 18(1). doi:10.18564/jasss.2661Baldoni, M., Baroglio, C., May, K., Micalizio, R., & Tedeschi, S. (2018). Computational Accountability in MAS Organizations with ADOPT. Applied Sciences, 8(4), 489. doi:10.3390/app8040489Boissier, O., Bordini, R. H., Hübner, J. F., Ricci, A., & Santi, A. (2013). Multi-agent oriented programming with JaCaMo. Science of Computer Programming, 78(6), 747-761. doi:10.1016/j.scico.2011.10.004Challenger, M., Tezel, B., Alaca, O., Tekinerdogan, B., & Kardas, G. (2018). Development of Semantic Web-Enabled BDI Multi-Agent Systems Using SEA_ML: An Electronic Bartering Case Study. Applied Sciences, 8(5), 688. doi:10.3390/app8050688Challenger, M., Demirkol, S., Getir, S., Mernik, M., Kardas, G., & Kosar, T. (2014). On the use of a domain-specific modeling language in the development of multiagent systems. Engineering Applications of Artificial Intelligence, 28, 111-141. doi:10.1016/j.engappai.2013.11.012Boztepe, İ., & Erdur, R. (2018). Linked Data Aware Agent Development Framework for Mobile Devices. Applied Sciences, 8(10), 1831. doi:10.3390/app8101831Shoham, Y., Powers, R., & Grenager, T. (2007). If multi-agent learning is the answer, what is the question? Artificial Intelligence, 171(7), 365-377. doi:10.1016/j.artint.2006.02.006Duan, K., Fong, S., Zhuang, Y., & Song, W. (2018). Artificial Neural Networks in Coordinated Control of Multiple Hovercrafts with Unmodeled Terms. Applied Sciences, 8(6), 862. doi:10.3390/app8060862Zhang, Q., Yao, J., Yin, Q., & Zha, Y. (2018). Learning Behavior Trees for Autonomous Agents with Hybrid Constraints Evolution. Applied Sciences, 8(7), 1077. doi:10.3390/app8071077Cook, D. J., Augusto, J. C., & Jakkula, V. R. (2009). Ambient intelligence: Technologies, applications, and opportunities. Pervasive and Mobile Computing, 5(4), 277-298. doi:10.1016/j.pmcj.2009.04.001Kranz, M., Holleis, P., & Schmidt, A. (2010). Embedded Interaction: Interacting with the Internet of Things. IEEE Internet Computing, 14(2), 46-53. doi:10.1109/mic.2009.141Gershenfeld, N., Krikorian, R., & Cohen, D. (2004). The Internet of Things. Scientific American, 291(4), 76-81. doi:10.1038/scientificamerican1004-76Atzori, L., Iera, A., & Morabito, G. (2010). The Internet of Things: A survey. Computer Networks, 54(15), 2787-2805. doi:10.1016/j.comnet.2010.05.010Costa, A., Novais, P., Corchado, J. M., & Neves, J. (2011). Increased performance and better patient attendance in an hospital with the use of smart agendas. Logic Journal of IGPL, 20(4), 689-698. doi:10.1093/jigpal/jzr021Tapia, D. I., & Corchado, J. M. (2009). An Ambient Intelligence Based Multi-Agent System for Alzheimer Health Care. International Journal of Ambient Computing and Intelligence, 1(1), 15-26. doi:10.4018/jaci.2009010102Barriuso, A., De la Prieta, F., Villarrubia González, G., De La Iglesia, D., & Lozano, Á. (2018). MOVICLOUD: Agent-Based 3D Platform for the Labor Integration of Disabled People. Applied Sciences, 8(3), 337. doi:10.3390/app8030337Rosales, R., Castañón-Puga, M., Lara-Rosano, F., Flores-Parra, J., Evans, R., Osuna-Millan, N., & Gaxiola-Pacheco, C. (2018). Modelling the Interaction Levels in HCI Using an Intelligent Hybrid System with Interactive Agents: A Case Study of an Interactive Museum Exhibition Module in Mexico. Applied Sciences, 8(3), 446. doi:10.3390/app8030446Ramos, J., Oliveira, T., Satoh, K., Neves, J., & Novais, P. (2018). Cognitive Assistants—An Analysis and Future Trends Based on Speculative Default Reasoning. Applied Sciences, 8(5), 742. doi:10.3390/app8050742SATOH, K. (2005). Speculative Computation and Abduction for an Autonomous Agent. IEICE Transactions on Information and Systems, E88-D(9), 2031-2038. doi:10.1093/ietisy/e88-d.9.2031Miyashita, K. (2017). Incremental Design of Perishable Goods Markets through Multi-Agent Simulations. Applied Sciences, 7(12), 1300. doi:10.3390/app7121300Albino, V., Berardi, U., & Dangelico, R. M. (2015). Smart Cities: Definitions, Dimensions, Performance, and Initiatives. Journal of Urban Technology, 22(1), 3-21. doi:10.1080/10630732.2014.942092Roscia, M., Longo, M., & Lazaroiu, G. C. (2013). Smart City by multi-agent systems. 2013 International Conference on Renewable Energy Research and Applications (ICRERA). doi:10.1109/icrera.2013.6749783Lozano, Á., De Paz, J., Villarrubia González, G., Iglesia, D., & Bajo, J. (2018). Multi-Agent System for Demand Prediction and Trip Visualization in Bike Sharing Systems. Applied Sciences, 8(1), 67. doi:10.3390/app8010067Jordán, J., Palanca, J., del Val, E., Julian, V., & Botti, V. (2018). A Multi-Agent System for the Dynamic Emplacement of Electric Vehicle Charging Stations. Applied Sciences, 8(2), 313. doi:10.3390/app8020313Billhardt, H., Fernández, A., Lujak, M., & Ossowski, S. (2018). Agreement Technologies for Coordination in Smart Cities. Applied Sciences, 8(5), 816. doi:10.3390/app805081

Supporting Internet-Scale Multi-Agent Systems

Although current research on agent systems focuses on relatively small-scale agent systems, soon vast numbers of agents will be deployed in large-scale agent systems, e.g. on the Internet. Large-scale agent systems need to be extensible