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The price of selfishness in network coding
 IEEE Transactions on Information Theory
, 2012
"... Abstract—A gametheoretic framework is introduced for studying selfish user behavior in shared wireless networks. The investigation treats anunicast problem in a wireless network that employs a restricted form of network coding called reverse carpooling. Unicast sessions independently choose routes ..."
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Abstract—A gametheoretic framework is introduced for studying selfish user behavior in shared wireless networks. The investigation treats anunicast problem in a wireless network that employs a restricted form of network coding called reverse carpooling. Unicast sessions independently choose routes through the network. The cost of a set of unicast routes is the number of wireless transmissions required to establish those connections using those routes. Game theory is employed as a tool for analyzing the impact of cost sharing mechanisms on the global system performance when each unicast independently and selfishly chooses its route to minimize its individual cost. The investigation focuses on the performance of stable solutions, where a stable solution is one in which no single unicast can improve its individual cost by changing its route. The results include bounds on the best and worstcase stable solutions compared to the best performance that could be found and implemented using a centralized controller. The optimal cost sharing protocol is derived and the worstcase solution is bounded. That worstcase stable performance cannot be improved using costsharing protocols that are independent of the network structure. Index Terms—Distributed control, game theory, network coding, price of anarchy (PoA), reverse carpooling.
DISTRIBUTED COVERAGE GAMES FOR ENERGYAWARE MOBILE SENSOR NETWORKS
"... Abstract. Inspired by current challenges in dataintensive and energylimited sensor networks, we formulate a coverage optimization problem for mobile sensors as a (constrained) repeated multiplayer game. Each sensor tries to optimize its own coverage while minimizing the processing/energy cost. The ..."
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Abstract. Inspired by current challenges in dataintensive and energylimited sensor networks, we formulate a coverage optimization problem for mobile sensors as a (constrained) repeated multiplayer game. Each sensor tries to optimize its own coverage while minimizing the processing/energy cost. The sensors are subject to the informational restriction that the environmental distribution function is unknown a priori. We present two distributed learning algorithms where each sensor only remembers its own utility values and actions played during the last plays. These algorithms are proven to be convergent in probability to the set of (constrained) Nash equilibria and global optima of certain coverage performance metric, respectively. Numerical examples are provided to verify the performance of our proposed algorithms. 1. Introduction. There
Distributed Seeking of Nash Equilibria in Mobile Sensor Networks
"... Abstract — In this paper we consider the problem of distributed convergence to a Nash equilibrium based on minimal information about the underlying noncooperative game. We assume that the players/agents generate their actions based only on measurements of local cost functions, which are corrupted wi ..."
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Abstract — In this paper we consider the problem of distributed convergence to a Nash equilibrium based on minimal information about the underlying noncooperative game. We assume that the players/agents generate their actions based only on measurements of local cost functions, which are corrupted with additive noise. Structural parameters of their own and other players ’ costs, as well as the actions of the other players are unknown. Furthermore, we assume that the agents may have dynamics: their actions can not be changed instantaneously. We propose a method based on a stochastic extremum seeking algorithm with sinusoidal perturbations and we prove its convergence, with probability one, to a Nash equilibrium. We discuss how the proposed algorithm can be adopted for solving coordination problems in mobile sensor networks, taking into account specific motion dynamics of the sensors. The local cost functions can be designed such that some specific overall goal is achieved. We give an example in which each agent/sensor needs to fulfill a locally defined goal, while maintaining connectivity with neighboring agents. The proposed algorithms are illustrated through simulations. I.
Overcoming the Limitations of Utility Design for Multiagent Systems
, 2011
"... Cooperative control focuses on deriving desirable collective behavior in multiagent systems through the design of local control algorithms. Game theory is beginning to emerge as a valuable set of tools for achieving this objective. A central component of this game theoretic approach is the assignmen ..."
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Cooperative control focuses on deriving desirable collective behavior in multiagent systems through the design of local control algorithms. Game theory is beginning to emerge as a valuable set of tools for achieving this objective. A central component of this game theoretic approach is the assignment of utility functions to the individual agents. Here, the goal is to assign utility functions within an “admissible” design space such that the resulting game possesses desirable properties. Our first set of results illustrates the complexity associated with such a task. In particular, we prove that if we restrict the class of utility functions to be local, scalable, and budgetbalanced then (i) ensuring that the resulting game possesses a pure Nash equilibrium requires computing a Shapley value, which can be computationally prohibitive for largescale systems, and (ii) ensuring that the allocation which optimizes the system level objective is a pure Nash equilibrium is impossible. The last part of this paper demonstrates that both limitations can be overcome by introducing an underlying state space into the potential game structure.
Game Theory and Distributed Control
, 2012
"... Game theory has been employed traditionally as a modeling tool for describing and influencing behavior in societal systems. Recently, game theory has emerged as a valuable tool for controlling or prescribing behavior in distributed engineered systems. The rationale for this new perspective stems fro ..."
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Game theory has been employed traditionally as a modeling tool for describing and influencing behavior in societal systems. Recently, game theory has emerged as a valuable tool for controlling or prescribing behavior in distributed engineered systems. The rationale for this new perspective stems from the parallels between the underlying decision making architectures in both societal systems and distributed engineered systems. In particular, both settings involve an interconnection of decision making elements whose collective behavior depends on a compilation of local decisions that are based on partial information about each other and the state of the world. Accordingly, there is extensive work in game theory that is relevant to the engineering agenda. Similarities notwithstanding, there remain important differences between the constraints and objectives in societal and engineered systems that require looking at game theoretic methods from a new perspective. This chapter provides an overview of selected recent developments of game theoretic methods in this role as a framework for distributed control in engineered systems.
DISTRIBUTED CLASSIFIER CHAIN OPTIMIZATION FOR REALTIME MULTIMEDIA STREAM MINING SYSTEMS
"... We consider the problem of optimally configuring classifier chains for realtime multimedia stream mining systems. Jointly maximizing the performance over several classifiers under minimal endtoend processing delay is a difficult task due to the distributed nature of analytics (e.g. utilized model ..."
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We consider the problem of optimally configuring classifier chains for realtime multimedia stream mining systems. Jointly maximizing the performance over several classifiers under minimal endtoend processing delay is a difficult task due to the distributed nature of analytics (e.g. utilized models or stored data sets), where changing the filtering process at a single classifier can have an unpredictable effect on both the feature values of data arriving at classifiers further downstream, as well as the endtoend processing delay. While the utility function can not be accurately modeled, in this paper we propose a randomized distributed algorithm that guarantees almost sure convergence to the optimal solution. We also provide results using speech data showing that the algorithm can perform well under highly dynamic environments.
Learning in Games
"... Abstract—In a Nash equilibrium, each player selects a strategy that is optimal with respect to the strategies of other players. This definition does not mention the process by which players reach a Nash equilibrium. The topic of learning in games seeks to address this issue in that it explores how s ..."
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Abstract—In a Nash equilibrium, each player selects a strategy that is optimal with respect to the strategies of other players. This definition does not mention the process by which players reach a Nash equilibrium. The topic of learning in games seeks to address this issue in that it explores how simplistic learning/adaptation rules can lead to Nash equilibrium. This article presents a selective sampling of learning rules and their longrun convergence properties, i.e., conditions under which player strategies converge or not to Nash equilibrium.
1Selecting Efficient Correlated Equilibria Through Distributed Learning
"... A learning rule is completely uncoupled if each player’s behavior is conditioned only on his own realized payoffs, and does not need to know the actions or payoffs of anyone else. We demonstrate a simple, completely uncoupled learning rule such that, in any finite normal form game with generic payof ..."
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A learning rule is completely uncoupled if each player’s behavior is conditioned only on his own realized payoffs, and does not need to know the actions or payoffs of anyone else. We demonstrate a simple, completely uncoupled learning rule such that, in any finite normal form game with generic payoffs, the players ’ realized strategies implements a Pareto optimal coarse correlated (Hannan) equilibrium a very high proportion of the time. A variant of the rule implements correlated equilibrium a very high proportion of the time. I.
Reaching the set of Nash equilibria
"... coverage games for mobile visual sensors (I): ..."
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