@inproceedings{17425,
author = {Berssenbrügge, Jan and Wiederkehr, Olga and Jähn, Claudius and Fischer, Matthias},
booktitle = {12. Paderborner Workshop Augmented & Virtual Reality in der Produktentstehung},
pages = {65--78},
publisher = {Verlagsschriftenreihe des Heinz Nixdorf Instituts},
title = {{Anbindung des Virtuellen Prototypen an die Partialmodelle intelligenter technischer Systeme}},
volume = {343},
year = {2015},
}
@inproceedings{16460,
abstract = {Consider n nodes connected to a single coordinator. Each node receives an
individual online data stream of numbers and, at any point in time, the
coordinator has to know the k nodes currently observing the largest values, for
a given k between 1 and n. We design and analyze an algorithm that solves this
problem while bounding the amount of messages exchanged between the nodes and
the coordinator. Our algorithm employs the idea of using filters which,
intuitively speaking, leads to few messages to be sent, if the new input is
"similar" to the previous ones. The algorithm uses a number of messages that is
on expectation by a factor of O((log {\Delta} + k) log n) larger than that of
an offline algorithm that sets filters in an optimal way, where {\Delta} is
upper bounded by the largest value observed by any node.},
author = {Mäcker, Alexander and Malatyali, Manuel and Meyer auf der Heide, Friedhelm},
booktitle = {Proceedings of the 29th International Parallel and Distributed Processing Symposium (IPDPS)},
pages = {357--364},
publisher = {IEEE},
title = {{Online Top-k-Position Monitoring of Distributed Data Streams}},
doi = {10.1109/IPDPS.2015.40},
year = {2015},
}
@inproceedings{19988,
author = {Hamann, Heiko and Schmickl, Thomas and Zahadat, Payam},
booktitle = {13th European Conference on Artificial Life (ECAL 2015)},
pages = {174},
publisher = {MIT Press},
title = {{Evolving Collective Behaviors With Diverse But Predictable Sensor States}},
doi = {10.7551/978-0-262-33027-5-ch036},
year = {2015},
}
@inproceedings{19990,
author = {Ding, Hongli and Hamann, Heiko},
booktitle = {First International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM 2015)},
title = {{Dependability in Swarm Robotics: Error Detection and Correction}},
year = {2015},
}
@inproceedings{20005,
author = {Dorigo, Marco and Hamann, Heiko and Valentini, Gabriele},
booktitle = {Proceedings of the 14th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2015)},
title = {{Efficient Decision-Making in a Self-Organizing Robot Swarm: On the Speed Versus Accuracy Trade-Off}},
year = {2015},
}
@article{17658,
abstract = {Abstract We study the problem of bandwidth allocation with multiple interferences. In this problem the input consists of a set of users and a set of base stations. Each user has a list of requests, each consisting of a base station, a frequency demand, and a profit that may be gained by scheduling this request. The goal is to find a maximum profit set of user requests S that satisfies the following conditions: (i) S contains at most one request per user, (ii) the frequency sets allotted to requests in S that correspond to the same base station are pairwise non-intersecting, and (iii) the QoS received by any user at any frequency is reasonable according to an interference model. In this paper we consider two variants of bandwidth allocation with multiple interferences. In the first each request specifies a demand that can be satisfied by any subset of frequencies that is large enough. In the second each request specifies a specific frequency interval. Furthermore, we consider two interference models, multiplicative and additive. We show that these problems are extremely hard to approximate if the interferences depend on both the interfered and the interfering base stations. On the other hand, we provide constant factor approximation algorithms for both variants of bandwidth allocation with multiple interferences for the case where the interferences depend only on the interfering base stations. We also consider a restrictive special case that is closely related to the Knapsack problem. We show that this special case is NP-hard and that it admits an FPTAS. },
author = {Bar-Yehuda, Reuven and Polevoy, Gleb and Rawitz, Dror},
issn = {0166-218X},
journal = {Discrete Applied Mathematics },
keyword = {Local ratio},
pages = {23 -- 36},
publisher = {Elsevier},
title = {{Bandwidth allocation in cellular networks with multiple interferences}},
doi = {http://dx.doi.org/10.1016/j.dam.2015.05.013},
volume = {194},
year = {2015},
}
@inproceedings{453,
abstract = {In this paper we study the potential function in congestion games. We consider both games with non-decreasing cost functions as well as games with non-increasing utility functions. We show that the value of the potential function $\Phi(\sf s)$ of any outcome $\sf s$ of a congestion game approximates the optimum potential value $\Phi(\sf s^*)$ by a factor $\Psi_{\mathcal{F}}$ which only depends on the set of cost/utility functions $\mathcal{F}$, and an additive term which is bounded by the sum of the total possible improvements of the players in the outcome $\sf s$. The significance of this result is twofold. On the one hand it provides \emph{Price-of-Anarchy}-like results with respect to the potential function. On the other hand, we show that these approximations can be used to compute $(1+\varepsilon)\cdot\Psi_{\mathcal{F}}$-approximate pure Nash equilibria for congestion games with non-decreasing cost functions. For the special case of polynomial cost functions, this significantly improves the guarantees from Caragiannis et al. [FOCS 2011]. Moreover, our machinery provides the first guarantees for general latency functions.},
author = {Feldotto, Matthias and Gairing, Martin and Skopalik, Alexander},
booktitle = {Proceedings of the 10th International Conference on Web and Internet Economics (WINE)},
pages = {30--43},
title = {{Bounding the Potential Function in Congestion Games and Approximate Pure Nash Equilibria}},
doi = {10.1007/978-3-319-13129-0_3},
year = {2014},
}
@inproceedings{20121,
abstract = {Collective decision making in self-organized systems is challenging because it relies on local perception and local communication. Globally defined qualities such as consensus time and decision accuracy are both difficult to predict and difficult to guarantee. We present the weighted voter model which implements a self-organized collective decision making process. We provide an ODE model, a master equation model (numerically solved by the Gillespie algorithm), and agent-based simulations of the proposed decision-making strategy. This set of models enables us to investigate the system behavior in the thermodynamic limit and to investigate finite-size effects due to random fluctuations. Based on our results, we give minimum requirements to guarantee consensus on the optimal decision, a minimum swarm size to guarantee a certain accuracy, and we show that the proposed approach scales with system size and is robust to noise.},
author = {Dorigo, Marco and Hamann, Heiko and Valentini, Gabriele and Lomuscio, Alessio and Scerri, Paul and Bazzan, Ana and Huhns, Michael},
booktitle = {Proceedings of the 13th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2014)},
title = {{Self-Organized Collective Decision Making: The Weighted Voter Model}},
year = {2014},
}
@inproceedings{20126,
author = {Hamann, Heiko},
booktitle = {Int. Conf. on Genetic and Evolutionary Computation (GECCO 2014)},
pages = {31--32},
title = {{Evolving Prediction Machines: Collective Behaviors Based on Minimal Surprisal}},
doi = {10.1145/2598394.2598507},
year = {2014},
}
@inproceedings{17661,
author = {King, Thomas C. and Liu, Qingzhi and Polevoy, Gleb and de Weerdt, Mathijs and Dignum, Virginia and van Riemsdijk, M. Birna and Warnier, Martijn},
booktitle = {Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems},
isbn = {978-1-4503-2738-1},
keyword = {crowd-sensing, crowdsourcing, data aggregation, game theory, norms, reciprocation, self interested agents, simulation},
pages = {1651--1652},
publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
title = {{Request Driven Social Sensing}},
year = {2014},
}