Group Formation Among Peer-to-Peer Agents: Learning Group Characteristics

This paper examines the decentralized formation of groups within a peer-to-peer multi-agent system. More specifically, it frames group formation as a clustering problem, and examines how to determine cluster characteristics such as area and density in the absence of information about the entire data set, such as the number of points, the number of clusters, or the maximum distance between points, that are available to centralized clustering algorithms. We develop a method in which agents individually search for other agents with similar characteristics in a peer-to-peer manner. These agents group into small centrally controlled clusters which learn cluster parameters by examining and improving their internal composition over time. We show through simulation that this method allows us to find clusters of a wide variety of sizes without adjusting agent parameters

Curious Negotiator

n negotiation the exchange of information is as important as the exchange of offers. The curious negotiator is a multiagent system with three types of agents. Two negotiation agents, each representing an individual, develop consecutive offers, supported by information, whilst requesting information from its opponent. A mediator agent, with experience of prior negotiations, suggests how the negotiation may develop. A failed negotiation is a missed opportunity. An observer agent analyses failures looking for new opportunities. The integration of negotiation theory and data mining enables the curious negotiator to discover and exploit negotiation opportunities. Trials will be conducted in electronic business

An ontology-based framework for describing discoverable data services

Data-services are applications in charge of retrieving certain data when they are called. They are found in different communities such as the Internet Of Things, Cloud Computing, Big Data, etc. So, there is a real need to discover how can an application that requires some data automatically find a data-service which is providing it. To our knowledge, the problem of automatically discovering these data-services is still open. To make a step forward in this direction, we propose an ontology-based framework to address this problem. In our framework, input and output values of the request are mapped into concepts of the domain ontology. Then, data-services specify how to obtain the output from the input by stating the relationship between the mapped concepts of the ontology.Peer ReviewedPostprint (author's final draft

Flexible provisioning of Web service workflows

Web services promise to revolutionise the way computational resources and business processes are offered and invoked in open, distributed systems, such as the Internet. These services are described using machine-readable meta-data, which enables consumer applications to automatically discover and provision suitable services for their workflows at run-time. However, current approaches have typically assumed service descriptions are accurate and deterministic, and so have neglected to account for the fact that services in these open systems are inherently unreliable and uncertain. Specifically, network failures, software bugs and competition for services may regularly lead to execution delays or even service failures. To address this problem, the process of provisioning services needs to be performed in a more flexible manner than has so far been considered, in order to proactively deal with failures and to recover workflows that have partially failed. To this end, we devise and present a heuristic strategy that varies the provisioning of services according to their predicted performance. Using simulation, we then benchmark our algorithm and show that it leads to a 700% improvement in average utility, while successfully completing up to eight times as many workflows as approaches that do not consider service failures

Semantic Web Services for Multi-Agent Systems Interoperability

Agent-based technologies are often used including existing web services. The outputs of some services are also frequently used as inputs for other services, including other MAS. However, while agent-based technologies can be used to provide services, these are not described using the same semantic web technologies web services use, which makes it difficult to discover, invoke and compose them with web services seamlessly. In this paper, we analyse different agent-based technologies and how these can be described using extensions to OWL-S. Additionally, we propose an architecture that facilitates these services’ usage, where services of any kind can be registered and executed (semi-)automatically.The present work has been developed under the PIANISM Project (ANI|P2020 40125) and has received funding from FEDER Funds through NORTE2020 program and from National Funds through Fundação para a Ciência e a Tecnologia (FCT) under the project UID/EEA/00760/2019. Gabriel Santos is supported by national funds through FCT PhD studentship with reference SFRH/BD/118487/2016.info:eu-repo/semantics/publishedVersio

Information fusion in multi-agent system based on reliability criterion

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-00369-6_13The paper addresses the problem of information fusion in Multi-Agent System. Since the knowledge of the process state is distributed between agents, the efficiency of the task performance depends on a proper information fusion technique applied to the agents. In this paper we study the case in which each agent has its own sensing device and is able to collect information with some certainty. Since the same information can be detected by multiple agents, the global certainty about the given fact derives from the fusion of information exchanged by interconnecting agents. The key issue in the method proposed, is an assumption that each agent is able to asses its own reliability during the task performance. The method is illustrated by the pick-up-and-collection task example. The effectiveness of the method proposed is evaluated using relevant simulation experiments.Mellado Arteche, M.; Skrzypczyk, K. (2013). Information fusion in multi-agent system based on reliability criterion. En Vision Based Systemsfor UAV Applications. Springer. 207-217. doi:10.1007/978-3-319-00369-6_13S207217Cheng, X., Shen, J., Liu, H., Gu, G.: Multi-robot Cooperation Based on Hierarchical Reinforcement Learning. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007, Part III. LNCS, vol. 4489, pp. 90–97. Springer, Heidelberg (2007)Harmati, I., Skrzypczyk, K.: Robot team coordination for target tracking using fuzzy logiccontroller in game theoretic framework. Robotics and Autonomous Systems 57(1) (2009)Jones, C., Mataric, M.: Adaptive Division of Labor in Large-Scale Minimalist Multi-Robot Systems. In: Proc. of IEEE/RSJ International Conference on Intelligent Robots and Systems, Las Vegas, pp. 1969–1974 (2003)Kaminka, G.A., Erusalimchik, D., Kraus, S.: Adaptive Multi-Robot Coordination: A Game-Theoretic Perspective. In: Proc. of IEEE International Conference on Robotics and Automation, Anchorage Convention District, Anchorage, Alaska, USA (2002)Kok, J.R., Spaan, M.T.J., Vlassis, N.: Non-communicative multi-robot coordination in dynamic environments. Robotics and Autonomous Systems 50(2-3), 99–114 (2005)Klusch, M., Gerber, A.: Dynamic coalition formation among rational agents. IEEE Intelligent Systems 17(3), 42–47 (2002)Kraus, S., Winkfeld, J., Zlotkin, G.: Multiagent negotiation under time constraints. Artificial Intelligence 75, 297–345 (1995)Kraus, S.: Negotiation and cooperation in multiagent environments. Artificial Intelligence 94(1-2), 79–98 (1997)Mataric, M., Sukhatme, G., Ostergaard, E.: Multi-Robot Task Allocation in Uncertain Environments. Autonomous Robots 14, 255–263 (2003)Schneider-Fontan, M., Mataric, M.J.: Territorial Multi-Robot Task Division. IEEE Transactionson Robotics and Automation 14(5), 815–822 (1998)Winkfeld, K.J., Zlotkin, G.: Multiagent negotiation under time constraints. Artificial Intelligence (75), 297–345 (1995)Wooldridge, M.: An Introduction to Multiagent Systems. Johnn Wiley and Sons Ltd., UK (2009) ISBN:978-0-470-51946-2Vail, D., Veloso, M.: Dynamic Multi-Robot Coordination. In: Schultz, A., et al. (eds.) Multi Robot Systems: From Swarms to Intelligent Automata, vol. II, pp. 87–98. Kluwer Academic Publishers, The Netherlands (2003)Gałuszka, A., Pacholczyk, M., Bereska, D., Skrzypczyk, K.: Planning as Artifficial Intelligence Problem-short introduction and overview. In: Nawrat, A., Simek, K., Świerniak, A. (eds.) Advanced Technologies for Intelligent Systems of National Border Security. SCI, vol. 440, pp. 95–104. Springer, Heidelberg (2013)Jędrasiak, K., Bereska, D., Nawrat, A.: The Prototype of Gyro-Stabilized UAV Gimbal for Day-Night Surveillance. In: Nawrat, A., Simek, K., Świerniak, A. (eds.) Advanced Technologies for Intelligent Systems of National Border Security. SCI, vol. 440, pp. 107–116. Springer, Heidelberg (2013)Galuszka, A., Bereska, D., Simek, K., Skrzypczyk, K., Daniec, K.: Application of graphs theory methods to criminal analysis system. Przeglad Elektrotechniczny 86(9), 278–283 (2010