4 research outputs found

    On the Combination of Game-Theoretic Learning and Multi Model Adaptive Filters

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    This paper casts coordination of a team of robots within the framework of game theoretic learning algorithms. In particular a novel variant of fictitious play is proposed, by considering multi-model adaptive filters as a method to estimate other players’ strategies. The proposed algorithm can be used as a coordination mechanism between players when they should take decisions under uncertainty. Each player chooses an action after taking into account the actions of the other players and also the uncertainty. Uncertainty can occur either in terms of noisy observations or various types of other players. In addition, in contrast to other game-theoretic and heuristic algorithms for distributed optimisation, it is not necessary to find the optimal parameters a priori. Various parameter values can be used initially as inputs to different models. Therefore, the resulting decisions will be aggregate results of all the parameter values. Simulations are used to test the performance of the proposed methodology against other game-theoretic learning algorithms.</p

    Mathematical Models for Improving Flexibility within the Smart Grid domain

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    One of the most important topics of the last decades has been finding energy sources who can replace fossil fuels. Renewable energy is a good candidate, being virtually inexhaustible and more environment-friendly. In order to allow for this transition, electric energy grids have evolved and have become new objects, called \emph{smart grids}. However, the complexity of this new type of grid brings new issues and challenges, which are currently object of study for many researchers. The purpose of this thesis is to showcase some of these problems, and to build mathematical models and algorithms in order to solve them, by leveraging a new property relative to energy loads: flexibility. Since smart devices are becoming more common and they can be remotely controlled, manipulation of energy profiles is possible, and this is a powerful tool for the management of smart grids. Going more into detail, a framework for managing demand response, peak shaving and energy trading has been designed by the means of a combinatorial approach, and it has been enhanced by exploitation of parallel computing. Moreover, an incentive mechanism for usage of renewable energy has been analyzed and improved, by changing some functions which define its behavior. This mechanism has also been examined from a game-theoretic point of view, and it has been further improved in order to always guarantee an agreement between users for flexibility usage. Finally, a decentralized, multi-agent system approach has been used to solve the problems of cost optimization and congestion management. Most of the content of this thesis derives from research works published in journals and conferences

    Proceedings of The Multi-Agent Logics, Languages, and Organisations Federated Workshops (MALLOW 2010)

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    http://ceur-ws.org/Vol-627/allproceedings.pdfInternational audienceMALLOW-2010 is a third edition of a series initiated in 2007 in Durham, and pursued in 2009 in Turin. The objective, as initially stated, is to "provide a venue where: the cost of participation was minimum; participants were able to attend various workshops, so fostering collaboration and cross-fertilization; there was a friendly atmosphere and plenty of time for networking, by maximizing the time participants spent together"

    Proceedings of the IJCAI-09 Workshop on Nonmonotonic Reasoning, Action and Change

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    Copyright in each article is held by the authors. Please contact the authors directly for permission to reprint or use this material in any form for any purpose.The biennial workshop on Nonmonotonic Reasoning, Action and Change (NRAC) has an active and loyal community. Since its inception in 1995, the workshop has been held seven times in conjunction with IJCAI, and has experienced growing success. We hope to build on this success again this eighth year with an interesting and fruitful day of discussion. The areas of reasoning about action, non-monotonic reasoning and belief revision are among the most active research areas in Knowledge Representation, with rich inter-connections and practical applications including robotics, agentsystems, commonsense reasoning and the semantic web. This workshop provides a unique opportunity for researchers from all three fields to be brought together at a single forum with the prime objectives of communicating important recent advances in each field and the exchange of ideas. As these fundamental areas mature it is vital that researchers maintain a dialog through which they can cooperatively explore common links. The goal of this workshop is to work against the natural tendency of such rapidly advancing fields to drift apart into isolated islands of specialization. This year, we have accepted ten papers authored by a diverse international community. Each paper has been subject to careful peer review on the basis of innovation, significance and relevance to NRAC. The high quality selection of work could not have been achieved without the invaluable help of the international Program Committee. A highlight of the workshop will be our invited speaker Professor Hector Geffner from ICREA and UPF in Barcelona, Spain, discussing representation and inference in modern planning. Hector Geffner is a world leader in planning, reasoning, and knowledge representation; in addition to his many important publications, he is a Fellow of the AAAI, an associate editor of the Journal of Artificial Intelligence Research and won an ACM Distinguished Dissertation Award in 1990
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