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512
Tussle in cyberspace: Defining tomorrow’s Internet
 In Proc. ACM SIGCOMM
, 2002
"... Abstract—The architecture of the Internet is based on a number of principles, including the selfdescribing datagram packet, the endtoend arguments, diversity in technology and global addressing. As the Internet has moved from a research curiosity to a recognized component of mainstream society, n ..."
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Cited by 228 (8 self)
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Abstract—The architecture of the Internet is based on a number of principles, including the selfdescribing datagram packet, the endtoend arguments, diversity in technology and global addressing. As the Internet has moved from a research curiosity to a recognized component of mainstream society, new requirements have emerged that suggest new design principles, and perhaps suggest that we revisit some old ones. This paper explores one important reality that surrounds the Internet today: different stakeholders that are part of the Internet milieu have interests that may be adverse to each other, and these parties each vie to favor their particular interests. We call this process “the tussle.” Our position is that accommodating this tussle is crucial to the evolution of the network’s technical architecture. We discuss some examples of tussle, and offer some technical design principles that take it into account. Index Terms—Competition, design principles, economics, network architecture, trust, tussle. I.
Rational Learning Leads to Nash Equilibrium
 Econometrica
, 1993
"... Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at ..."
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Cited by 215 (13 self)
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Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at
Coalition Structure Generation with Worst Case Guarantees
, 1999
"... Coalition formation is a key topic in multiagent systems. One may prefer a coalition structure that maximizes the sum of the values of the coalitions, but often the number of coalition structures is too large to allow exhaustive search for the optimal one. Furthermore, finding the optimal coalition ..."
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Cited by 209 (10 self)
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Coalition formation is a key topic in multiagent systems. One may prefer a coalition structure that maximizes the sum of the values of the coalitions, but often the number of coalition structures is too large to allow exhaustive search for the optimal one. Furthermore, finding the optimal coalition structure is NPcomplete. But then, can the coalition structure found via a partial search be guaranteed to be within a bound from optimum? We show that none of the previous coalition structure generation algorithms can establish any bound because they search fewer nodes than a threshold that we show necessary for establishing a bound. We present an algorithm that establishes a tight bound within this minimal amount of search, and show that any other algorithm would have to search strictly more. The fraction of nodes needed to be searched approaches zero as the number of agents grows. If additional time remains, our anytime algorithm searches further, and establishes a progressively lower tight bound. Surprisingly, just searching one more node drops the bound in half. As desired, our algorithm lowers the bound rapidly early on, and exhibits diminishing returns to computation. It also significantly outperforms its obvious contenders. Finally, we show how to distribute the desired
Multiagent Learning Using a Variable Learning Rate
 Artificial Intelligence
, 2002
"... Learning to act in a multiagent environment is a difficult problem since the normal definition of an optimal policy no longer applies. The optimal policy at any moment depends on the policies of the other agents and so creates a situation of learning a moving target. Previous learning algorithms hav ..."
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Cited by 180 (8 self)
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Learning to act in a multiagent environment is a difficult problem since the normal definition of an optimal policy no longer applies. The optimal policy at any moment depends on the policies of the other agents and so creates a situation of learning a moving target. Previous learning algorithms have one of two shortcomings depending on their approach. They either converge to a policy that may not be optimal against the specific opponents' policies, or they may not converge at all. In this article we examine this learning problem in the framework of stochastic games. We look at a number of previous learning algorithms showing how they fail at one of the above criteria. We then contribute a new reinforcement learning technique using a variable learning rate to overcome these shortcomings. Specifically, we introduce the WoLF principle, "Win or Learn Fast", for varying the learning rate. We examine this technique theoretically, proving convergence in selfplay on a restricted class of iterated matrix games. We also present empirical results on a variety of more general stochastic games, in situations of selfplay and otherwise, demonstrating the wide applicability of this method.
Distributed Rational Decision Making
, 1999
"... Introduction Automated negotiation systems with selfinterested agents are becoming increasingly important. One reason for this is the technology push of a growing standardized communication infrastructureInternet, WWW, NII, EDI, KQML, FIPA, Concordia, Voyager, Odyssey, Telescript, Java, etco ..."
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Cited by 167 (0 self)
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Introduction Automated negotiation systems with selfinterested agents are becoming increasingly important. One reason for this is the technology push of a growing standardized communication infrastructureInternet, WWW, NII, EDI, KQML, FIPA, Concordia, Voyager, Odyssey, Telescript, Java, etcover which separately designed agents belonging to different organizations can interact in an open environment in realtime and safely carry out transactions. The second reason is strong application pull for computer support for negotiation at the operative decision making level. For example, we are witnessing the advent of small transaction electronic commerce on the Internet for purchasing goods, information, and communication bandwidth [29]. There is also an industrial trend toward virtual enterprises: dynamic alliances of small, agile enterprises which together can take advantage of economies of scale when available (e.g., respond to mor
Coalitions Among Computationally Bounded Agents
 Artificial Intelligence
, 1997
"... This paper analyzes coalitions among selfinterested agents that need to solve combinatorial optimization problems to operate e ciently in the world. By colluding (coordinating their actions by solving a joint optimization problem) the agents can sometimes save costs compared to operating individua ..."
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Cited by 167 (24 self)
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This paper analyzes coalitions among selfinterested agents that need to solve combinatorial optimization problems to operate e ciently in the world. By colluding (coordinating their actions by solving a joint optimization problem) the agents can sometimes save costs compared to operating individually. A model of bounded rationality is adopted where computation resources are costly. It is not worthwhile solving the problems optimally: solution quality is decisiontheoretically traded o against computation cost. A normative, application and protocolindependent theory of coalitions among boundedrational agents is devised. The optimal coalition structure and its stability are signi cantly a ected by the agents ' algorithms ' performance pro les and the cost of computation. This relationship is rst analyzed theoretically. Then a domain classi cation including rational and boundedrational agents is introduced. Experimental results are presented in vehicle routing with real data from ve dispatch centers. This problem is NPcomplete and the instances are so large thatwith current technologyany agent's rationality is bounded by computational complexity. 1
Multiagent influence diagrams for representing and solving games
 Games and Economic Behavior
, 2001
"... The traditional representations of games using the extensive form or the strategic (normal) form obscure much of the structure that is present in realworld games. In this paper, we propose a new representation language for general multiplayer games — multiagent influence diagrams (MAIDs). This rep ..."
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Cited by 157 (2 self)
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The traditional representations of games using the extensive form or the strategic (normal) form obscure much of the structure that is present in realworld games. In this paper, we propose a new representation language for general multiplayer games — multiagent influence diagrams (MAIDs). This representation extends graphical models for probability distributions to a multiagent decisionmaking context. MAIDs explicitly encode structure involving the dependence relationships among variables. As a consequence, we can define a notion of strategic relevance of one decision variable to another: ¢¡ is strategically relevant to if, to optimize the decision rule at, the decision maker needs to take into consideration the decision rule at ¡. We provide a sound and complete graphical criterion for determining strategic relevance. We then show how strategic relevance can be used to detect structure in games, allowing a large game to be broken up into a set of interacting smaller games, which can be solved in sequence. We show that this decomposition can lead to substantial savings in the computational cost of finding Nash equilibria in these games. 1
The Complexity of Pure Nash Equilibria
, 2004
"... We investigate from the computational viewpoint multiplayer games that are guaranteed to have pure Nash equilibria. We focus on congestion games, and show that a pure Nash equilibrium can be computed in polynomial time in the symmetric network case, while the problem is PLScomplete in general. ..."
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Cited by 145 (6 self)
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We investigate from the computational viewpoint multiplayer games that are guaranteed to have pure Nash equilibria. We focus on congestion games, and show that a pure Nash equilibrium can be computed in polynomial time in the symmetric network case, while the problem is PLScomplete in general. We discuss implications to nonatomic congestion games, and we explore the scope of the potential function method for proving existence of pure Nash equilibria.
Complexity Results about Nash Equilibria
, 2002
"... Noncooperative game theory provides a normative framework for analyzing strategic interactions. ..."
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Cited by 130 (10 self)
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Noncooperative game theory provides a normative framework for analyzing strategic interactions.