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A formal analysis and taxonomy of task allocation in multirobot systems
 Int’l. J. of Robotics Research
"... Despite more than a decade of experimental work in multirobot systems, important theoretical aspects of multirobot coordination mechanisms have, to date, been largely untreated. To address this issue, we focus on the problem of multirobot task allocation (MRTA). Most work on MRTA has been ad hoc ..."
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Cited by 185 (4 self)
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Despite more than a decade of experimental work in multirobot systems, important theoretical aspects of multirobot coordination mechanisms have, to date, been largely untreated. To address this issue, we focus on the problem of multirobot task allocation (MRTA). Most work on MRTA has been ad hoc and empirical, with many coordination architectures having been proposed and validated in a proofofconcept fashion, but infrequently analyzed. With the goal of bringing objective grounding to this important area of research, we present a formal study of MRTA problems. A domainindependent taxonomy of MRTA problems is given, and it is shown how many such problems can be viewed as instances of other, wellstudied, optimization problems. We demonstrate how relevant theory from operations research and combinatorial optimization can be used for analysis and greater understanding of existing approaches to task allocation, and to show how the same theory can be used in the synthesis of new approaches. KEY WORDS—task allocation, multirobot systems, coordination, utility 1.
Selfish Routing and the Price of Anarchy
, 2005
"... Abstract Selfish routing is a classical mathematical model of how selfinterested users might route traffic through a congested network. The outcome of selfish routing is generally inefficient, in that it fails to optimize natural objective functions. The price of anarchy is a quantitative measure o ..."
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Cited by 171 (11 self)
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Abstract Selfish routing is a classical mathematical model of how selfinterested users might route traffic through a congested network. The outcome of selfish routing is generally inefficient, in that it fails to optimize natural objective functions. The price of anarchy is a quantitative measure of this inefficiency. We survey recent work that analyzes the price of anarchy of selfish routing. We also describe related results on bounding the worstpossible severity of a phenomenon called Braess's Paradox, and on three techniques for reducing the price of anarchy of selfish routing. This survey concentrates on the contributions of the author's PhD thesis, but also discusses several more recent results in the area.
Bounding the Lifetime of Sensor Networks Via Optimal Role Assignments
, 2002
"... A key challenge in adhoc, datagathering wireless sensor networks is achieving a lifetime of several years using nodes that carry merely hundreds of joules of stored energy. In this paper, we explore the fundamental limits of energyefficient collaborative datagathering by deriving upper bounds on ..."
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Cited by 158 (0 self)
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A key challenge in adhoc, datagathering wireless sensor networks is achieving a lifetime of several years using nodes that carry merely hundreds of joules of stored energy. In this paper, we explore the fundamental limits of energyefficient collaborative datagathering by deriving upper bounds on the lifetime of increasingly sophisticated sensor networks.
SelfOrganization and Identification of Web Communities
 IEEE Computer
, 2002
"... Despite the decentralized and unorganized nature of the web, we show that the web selforganizes such that communities of highly related pages can be efficiently identified based purely on connectivity. ..."
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Cited by 149 (0 self)
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Despite the decentralized and unorganized nature of the web, we show that the web selforganizes such that communities of highly related pages can be efficiently identified based purely on connectivity.
Geometric Shortest Paths and Network Optimization
 Handbook of Computational Geometry
, 1998
"... Introduction A natural and wellstudied problem in algorithmic graph theory and network optimization is that of computing a "shortest path" between two nodes, s and t, in a graph whose edges have "weights" associated with them, and we consider the "length" of a path to be the sum of the weights of t ..."
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Cited by 149 (13 self)
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Introduction A natural and wellstudied problem in algorithmic graph theory and network optimization is that of computing a "shortest path" between two nodes, s and t, in a graph whose edges have "weights" associated with them, and we consider the "length" of a path to be the sum of the weights of the edges that comprise it. Efficient algorithms are well known for this problem, as briefly summarized below. The shortest path problem takes on a new dimension when considered in a geometric domain. In contrast to graphs, where the encoding of edges is explicit, a geometric instance of a shortest path problem is usually specified by giving geometric objects that implicitly encode the graph and its edge weights. Our goal in devising efficient geometric algorithms is generally to avoid explicit construction of the entire underlying graph, since the full induced graph may be very large (even exponential in the input size, or infinite). Computing an optimal
Models of Translational Equivalence among Words
 Computational Linguistics
, 2000
"... This article presents methods for biasing statistical translation models to reflect these properties. Evaluation with respect to independent human judgments has confirmed that translation models biased in this fashion are significantly more accurate than a baseline knowledgefree model. This article ..."
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Cited by 143 (2 self)
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This article presents methods for biasing statistical translation models to reflect these properties. Evaluation with respect to independent human judgments has confirmed that translation models biased in this fashion are significantly more accurate than a baseline knowledgefree model. This article also shows how a statistical translation model can take advantage of preexisting knowledge that might be available about particular language pairs. Even the simplest kinds of languagespecific knowledge, such as the distinction between content words and function words, are shown to reliably boost translation model performance on some tasks. Statistical models that reflect knowledge about the model domain combine the best of both the rationalist and empiricist paradigms
A measure of betweenness centrality based on random walks
 Social Networks
, 2005
"... Betweenness is a measure of the centrality of a node in a network, and is normally calculated as the fraction of shortest paths between node pairs that pass through the node of interest. Betweenness is, in some sense, a measure of the influence a node has over the spread of information through the n ..."
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Cited by 138 (0 self)
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Betweenness is a measure of the centrality of a node in a network, and is normally calculated as the fraction of shortest paths between node pairs that pass through the node of interest. Betweenness is, in some sense, a measure of the influence a node has over the spread of information through the network. By counting only shortest paths, however, the conventional definition implicitly assumes that information spreads only along those shortest paths. Here we propose a betweenness measure that relaxes this assumption, including contributions from essentially all paths between nodes, not just the shortest, although it still gives more weight to short paths. The measure is based on random walks, counting how often a node is traversed by a random walk between two other nodes. We show how our measure can be calculated using matrix methods, and give some examples of its application to particular networks. 1
The Web as a Parallel Corpus
 Computational Linguistics
, 2003
"... Parallel corpora have become an essential resource for work in multilingual natural language processing. In this report, we describe our work using the STRAND system for mining parallel text on the World Wide Web, first reviewing the original algorithm and results and then presenting a set of signif ..."
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Cited by 136 (6 self)
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Parallel corpora have become an essential resource for work in multilingual natural language processing. In this report, we describe our work using the STRAND system for mining parallel text on the World Wide Web, first reviewing the original algorithm and results and then presenting a set of significant enhancements. These enhancements include the use of supervised learning based on structural features of documents to improve classification performance, a new contentbased measure of translational equivalence, and adaptation of the system to take advantage of the Internet Archive for mining parallel text from the Web on a large scale.
On Selfish Routing in InternetLike Environments
 in Proceedings of ACM SIGCOMM
, 2003
"... Abstract — A recent trend in routing research is to avoid inefficiencies in networklevel routing by allowing hosts to either choose routes themselves (e.g., source routing) or use overlay routing networks (e.g., Detour or RON). Such approaches result in selfish routing, because routing decisions ar ..."
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Cited by 126 (8 self)
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Abstract — A recent trend in routing research is to avoid inefficiencies in networklevel routing by allowing hosts to either choose routes themselves (e.g., source routing) or use overlay routing networks (e.g., Detour or RON). Such approaches result in selfish routing, because routing decisions are no longer based on systemwide criteria but are instead designed to optimize hostbased or overlaybased metrics. A series of theoretical results showing that selfish routing can result in suboptimal system behavior have cast doubts on this approach. In this paper, we use a gametheoretic approach to investigate the performance of selfish routing in Internetlike environments, using realistic topologies and traffic demands in our simulations. We show that in contrast to theoretical worst cases, selfish routing achieves close to optimal average latency in such environments. However, such performance benefit comes at the expense of significantly increased congestion on certain links. Moreover, the adaptive nature of selfish overlays can significantly reduce the effectiveness of traffic engineering by making network traffic less predictable.