TY - CONF
AU - Berssenbrügge, Jan
AU - Wiederkehr, Olga
AU - Jähn, Claudius
AU - Fischer, Matthias
ID - 17425
T2 - 12. Paderborner Workshop Augmented & Virtual Reality in der Produktentstehung
TI - Anbindung des Virtuellen Prototypen an die Partialmodelle intelligenter technischer Systeme
VL - 343
ER -
TY - CONF
AB - 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.
AU - Mäcker, Alexander
AU - Malatyali, Manuel
AU - Meyer auf der Heide, Friedhelm
ID - 16460
T2 - Proceedings of the 29th International Parallel and Distributed Processing Symposium (IPDPS)
TI - Online Top-k-Position Monitoring of Distributed Data Streams
ER -
TY - CONF
AU - Hamann, Heiko
AU - Schmickl, Thomas
AU - Zahadat, Payam
ID - 19988
T2 - 13th European Conference on Artificial Life (ECAL 2015)
TI - Evolving Collective Behaviors With Diverse But Predictable Sensor States
ER -
TY - CONF
AU - Ding, Hongli
AU - Hamann, Heiko
ID - 19990
T2 - First International Symposium on Swarm Behavior and Bio-Inspired Robotics (SWARM 2015)
TI - Dependability in Swarm Robotics: Error Detection and Correction
ER -
TY - CONF
AU - Dorigo, Marco
AU - Hamann, Heiko
AU - Valentini, Gabriele
ID - 20005
T2 - Proceedings of the 14th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2015)
TI - Efficient Decision-Making in a Self-Organizing Robot Swarm: On the Speed Versus Accuracy Trade-Off
ER -
TY - JOUR
AB - 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.
AU - Bar-Yehuda, Reuven
AU - Polevoy, Gleb
AU - Rawitz, Dror
ID - 17658
JF - Discrete Applied Mathematics
KW - Local ratio
SN - 0166-218X
TI - Bandwidth allocation in cellular networks with multiple interferences
VL - 194
ER -
TY - CONF
AB - 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.
AU - Feldotto, Matthias
AU - Gairing, Martin
AU - Skopalik, Alexander
ID - 453
T2 - Proceedings of the 10th International Conference on Web and Internet Economics (WINE)
TI - Bounding the Potential Function in Congestion Games and Approximate Pure Nash Equilibria
ER -
TY - CONF
AB - 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.
AU - Dorigo, Marco
AU - Hamann, Heiko
AU - Valentini, Gabriele
AU - Lomuscio, Alessio
AU - Scerri, Paul
AU - Bazzan, Ana
AU - Huhns, Michael
ID - 20121
T2 - Proceedings of the 13th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2014)
TI - Self-Organized Collective Decision Making: The Weighted Voter Model
ER -
TY - CONF
AU - Hamann, Heiko
ID - 20126
T2 - Int. Conf. on Genetic and Evolutionary Computation (GECCO 2014)
TI - Evolving Prediction Machines: Collective Behaviors Based on Minimal Surprisal
ER -
TY - CONF
AU - King, Thomas C.
AU - Liu, Qingzhi
AU - Polevoy, Gleb
AU - de Weerdt, Mathijs
AU - Dignum, Virginia
AU - van Riemsdijk, M. Birna
AU - Warnier, Martijn
ID - 17661
KW - crowd-sensing
KW - crowdsourcing
KW - data aggregation
KW - game theory
KW - norms
KW - reciprocation
KW - self interested agents
KW - simulation
SN - 978-1-4503-2738-1
T2 - Proceedings of the 2014 International Conference on Autonomous Agents and Multi-agent Systems
TI - Request Driven Social Sensing
ER -