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Roukny T.,IRIDIA | Roukny T.,Sbs Em Center rnheim | Bersini H.,IRIDIA | Pirotte H.,Sbs Em Center rnheim | And 2 more authors.
Scientific Reports | Year: 2013

The recent crisis has brought to the fore a crucial question that remains still open: what would be the optimal architecture of financial systems? We investigate the stability of several benchmark topologies in a simple default cascading dynamics in bank networks. We analyze the interplay of several crucial drivers, i.e., network topology, banks' capital ratios, market illiquidity, and random vs targeted shocks. We find that, in general, topology matters only-but substantially-when the market is illiquid. No single topology is always superior to others. In particular, scale-free networks can be both more robust and more fragile than homogeneous architectures. This finding has important policy implications. We also apply our methodology to a comprehensive dataset of an interbank market from 1999 to 2011.

Ducatelle F.,University of Applied Sciences and Arts Southern Switzerland | Di Caro G.A.,University of Applied Sciences and Arts Southern Switzerland | Pinciroli C.,IRIDIA | Mondada F.,Ecole Polytechnique Federale de Lausanne | Gambardella L.,University of Applied Sciences and Arts Southern Switzerland
IEEE International Conference on Intelligent Robots and Systems | Year: 2011

We present a communication based navigation algorithm for robotic swarms. It lets robots guide each other's navigation by exchanging messages containing navigation information through the wireless network formed among the swarm. We study the use of this algorithm in two different scenarios. In the first scenario, the swarm guides a single robot to a target, while in the second, all robots of the swarm navigate back and forth between two targets. In both cases, the algorithm provides efficient navigation, while being robust to failures of robots in the swarm. Moreover, we show that in the latter case, the system lets the swarm self-organize into a robust dynamic structure. This self-organization further improves navigation efficiency, and is able to find shortest paths in cluttered environments. We test our system both in simulation and on real robots. © 2011 IEEE.

Ducatelle F.,University of Applied Sciences and Arts Southern Switzerland | Di Caro G.A.,University of Applied Sciences and Arts Southern Switzerland | Forster A.,University of Applied Sciences and Arts Southern Switzerland | Bonani M.,Ecole Polytechnique Federale de Lausanne | And 8 more authors.
Swarm Intelligence | Year: 2014

We study cooperative navigation for robotic swarms in the context of a general event-servicing scenario. In the scenario, one or more events need to be serviced at specific locations by robots with the required skills. We focus on the question of how the swarm can inform its members about events, and guide robots to event locations. We propose a solution based on delay-tolerant wireless communications: by forwarding navigation information between them, robots cooperatively guide each other towards event locations. Such a collaborative approach leverages on the swarm's intrinsic redundancy, distribution, and mobility. At the same time, the forwarding of navigation messages is the only form of cooperation that is required. This means that the robots are free in terms of their movement and location, and they can be involved in other tasks, unrelated to the navigation of the searching robot. This gives the system a high level of flexibility in terms of application scenarios, and a high degree of robustness with respect to robot failures or unexpected events. We study the algorithm in two different scenarios, both in simulation and on real robots. In the first scenario, a single searching robot needs to find a single target, while all other robots are involved in tasks of their own. In the second scenario, we study collective navigation: all robots of the swarm navigate back and forth between two targets, which is a typical scenario in swarm robotics. We show that in this case, the proposed algorithm gives rise to synergies in robot navigation, and it lets the swarm self-organize into a robust dynamic structure. The emergence of this structure improves navigation efficiency and lets the swarm find shortest paths. © 2013 Springer Science+Business Media New York.

Francesca G.,IRIDIA | Brambilla M.,IRIDIA | Trianni V.,CNR Institute of Cognitive Sciences and Technologies | Dorigo M.,IRIDIA | Birattari M.,IRIDIA
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

Evolutionary robotics can be a powerful tool in studies on the evolutionary origins of self-organising behaviours in biological systems. However, these studies are viable only when the behaviour of the evolved artificial system closely corresponds to the one observed in biology, as described by available models. In this paper, we compare the behaviour evolved in a robotic system with the collegial decision making displayed by cockroaches in selecting a resting shelter. We show that artificial evolution can synthesise a simple self-organising behaviour for a swarm of robots, which presents dynamics that are comparable with the cockroaches behaviour. © 2012 Springer-Verlag.

Devooght R.,IRIDIA | Mantrach A.,Yahoo! | Kivimaki I.,ICTEAM | Bersini H.,IRIDIA | And 2 more authors.
WWW 2014 - Proceedings of the 23rd International Conference on World Wide Web | Year: 2014

Although criticized for some of its limitations, modularity remains a standard measure for analyzing social networks. Quantifying the statistical surprise in the arrangement of the edges of the network has led to simple and powerful algorithms. However, relying solely on the distribution of edges instead of more complex structures such as paths limits the extent of modularity. Indeed, recent studies have shown restrictions of optimizing modularity, for instance its resolution limit. We introduce here a novel, formal and welldefined modularity measure based on random walks. We show how this modularity can be computed from paths induced by the graph instead of the traditionally used edges. We argue that by computing modularity on paths instead of edges, more informative features can be extracted from the network. We verify this hypothesis on a semi-supervised classification procedure of the nodes in the network, where we show that, under the same settings, the features of the random walk modularity help to classify better than the features of the usual modularity. Additionally, the proposed approach outperforms the classical label propagation procedure on two data sets of labeled social networks. Copyright is held by the International World Wide Web Conference Committee (IW3C2).

Devooght R.,IRIDIA | Kourtellis N.,Telefonica | Mantrach A.,Yahoo!
Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining | Year: 2015

Advanced and effective collaborative filtering methods based on explicit feedback assume that unknown ratings do not follow the same model as the observed ones (not missing at random). In this work, we build on this assumption, and introduce a novel dynamic matrix factorization framework that allows to set an explicit prior on unknown values. When new ratings, users, or items enter the system, we can update the factorization in time independent of the size of data (number of users, items and ratings). Hence, we can quickly recommend items even to very recent users. We test our methods on three large datasets, including two very sparse ones, in static and dynamic conditions. In each case, we outrank state-of-the-art matrix factorization methods that do not use a prior on unknown ratings. Copyright 2015 ACM.

Oliveira S.M.,IRIDIA | Hussin M.S.,IRIDIA | Stutzle T.,IRIDIA | Roli A.,University of Bologna | Dorigo M.,IRIDIA
Genetic and Evolutionary Computation Conference, GECCO'11 - Companion Publication | Year: 2011

The population-based ant colony optimization algorithm (P-ACO) uses a very different pheromone update when compared to other ACO algorithms. In this work, we study P-ACO's behavior for solving the traveling salesman problem (TSP) and the quadratic assignment problem (QAP). In particular, we investigate the impact of a local search on P-ACO parameters and performance. The results clearly show that P-ACO is a very competitive tool whose parameters and behavior depend strongly on the problem tackled and on whether a local search is used. © 2011 Authors.

Stutzle T.,IRIDIA | Lopez-Ibaez M.,University of Manchester
GECCO 2016 Companion - Proceedings of the 2016 Genetic and Evolutionary Computation Conference | Year: 2016

Most optimization algorithms, including evolutionary algorithms and metaheuristics, and general-purpose solvers for integer or constraint programming, have often many parameters that need to be properly configured (i.e., tuned) for obtaining the best results on a particular problem. Automatic (offline) algorithm configuration methods help algorithm users to determine the parameter settings that optimize the performance of the algorithm before the algorithm is actually deployed. Moreover, automatic algorithm configuration methods may potentially lead to a paradigm shift in algorithm design and configuration because they enable algorithm designers to explore much larger design spaces than by traditional trial-and-error and experimental design procedures. Thus, algorithm designers can focus on inventing new algorithmic components, combine them in flexible algorithm frameworks, and let final algorithm design decisions be taken by automatic algorithm configuration techniques for specific application contexts. This tutorial will be divided in two parts. The first part will give an overview of the algorithm configuration problem, review recent methods for automatic algorithm configuration, and illustrate the potential of these techniques using recent, notable applications from the presenters' and other researchers work. The second part of the tutorial will focus on a detailed discussion of more complex scenarios, including multi-objective problems, anytime algorithms, heterogeneous problem instances, and the automatic generation of algorithms from algorithm frameworks. The focus of this second part of the tutorial is, hence, on practical but challenging applications of automatic algorithm configuration. The second part of the tutorial will demonstrate how to tackle these configuration tasks using our irace software (, which implements the iterated racing procedure for automatic algorithm configuration. We will provide a practical step-by-step guide on using irace for the typical algorithm configuration scenario. © 2016 Copyright held by the owner/author(s).

Philemotte C.,IRIDIA | Bersini H.,IRIDIA
Natural Computing | Year: 2012

Nowadays, many engineering applications require the minimization of a cost function such as decreasing the delivery time or the used space, reducing the development effort, and so on. Not surprisingly, research in optimization is one of the most active fields of computer science. Metaheuristics are part of the state-of-the-art techniques for combinatorial optimization. But their success comes at the price of considerable efforts in design and development time. Can we go further and automate their preparation? Especially when time is limited, dedicated techniques are unknown or the tackled problem is not well understood? The Gestalt heuristic, a search based on meta-modeling, answers those questions. Our approach, inspired by Gestalt psychology, considers the problem representation as a key factor of the success of the metaheuristic search process. Thanks to the emergence of such representation abstraction, the metaheuristic is being assisted by constraining the search. This abstraction is mainly based on the aggregation of the representation variables. The metaheuristic operators then work with these new aggregates. By learning, the Gestalt heuristic continuously searches for the right level of abstraction. It turns out to be an engineering mechanism very much related with the intrinsic emergence concept. First, the paper introduces the approach in the practical context of combinatorial optimization. It describes one possible implementation with evolutionary algorithms. Then, several experimental studies and results are presented and discussed in order to test the suggested Gestalt heuristic implementation and its main characteristics. Finally, the heuristic is more conceptually discussed in the context of emergence. © Springer Science+Business Media B.V. 2011.

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