Results 1  10
of
32
Learning Efficient Nash Equilibria in Distributed Systems
, 2010
"... Abstract. An individual’s learning rule is completely uncoupled if it does not depend on the actions or payoffs of anyone else. We propose a variant of log linear learning that is completely uncoupled and that selects an efficient pure Nash equilibrium in all generic nperson games that possess at l ..."
Abstract

Cited by 15 (1 self)
 Add to MetaCart
(Show Context)
Abstract. An individual’s learning rule is completely uncoupled if it does not depend on the actions or payoffs of anyone else. We propose a variant of log linear learning that is completely uncoupled and that selects an efficient pure Nash equilibrium in all generic nperson games that possess at least one pure Nash equilibrium. In games that do not have such an equilibrium, there is a simple formula that expresses the longrun probability of the various disequilibrium states in terms of two factors: i) the sum of payoffs over all agents, and ii) the maximum payoff gain that results from a unilateral deviation by some agent. This welfare/stability tradeoff criterion provides a novel framework for analyzing the selection of disequilibrium as well as equilibrium states in nperson games. JEL: C72, C73 1 1. Learning equilibrium in complex interactive systems Game theory has traditionally focussed on situations that involve a small number of players. In these environments it makes sense to assume that players know the structure of the game and can predict the strategic behavior of their opponents. But there are many situations involving huge numbers of players where these assumptions are not particularly persuasive.
1 Opportunistic Spectrum Access in Cognitive Radio Networks: When to Turn off the Spectrum Sensors
"... Abstract — In cognitive radio networks, spectrum sensing is a critical to both protecting the primary users and creating spectrum access opportunities of secondary users. Channel sensing itself, including active probing and passive listening, often incurs cost, in terms of time overhead, energy cons ..."
Abstract

Cited by 14 (2 self)
 Add to MetaCart
(Show Context)
Abstract — In cognitive radio networks, spectrum sensing is a critical to both protecting the primary users and creating spectrum access opportunities of secondary users. Channel sensing itself, including active probing and passive listening, often incurs cost, in terms of time overhead, energy consumption, or intrusion to primary users. It is thus not desirable to sense the channel arbitrarily. In this paper, we are motivated to consider the following problem. A secondary user, equipped with spectrum sensors, dynamically accesses a channel. If it transmits without/with colliding with primary users, a certain reward/penalty is obtained. If it senses the channel, accurate channel information is obtained, but a given channel sensing cost incurs. The third option for the user is to turn off the sensor/transmitter and go to sleep mode, where no cost/gain incurs. So when should the secondary user transmit, sense, or sleep, to maximize the total gain? We derive the optimal transmitting, sensing, and sleeping structure, which is a thresholdbased policy. Our work sheds light on designing sensing and transmitting scheduling protocols for cognitive radio networks, especially the inband sensing mechanism in 802.22 networks. I.
Collaborative sensing in a distributed ptz camera network,” Image Processing
 IEEE Transactions on
, 2012
"... Abstract—The performance of dynamic scene algorithms often suffers because of the inability to effectively acquire features on the targets, particularly when they are distributed over a wide field of view. In this paper, we propose an integrated analysis and control framework for a pan, tilt, zoom ( ..."
Abstract

Cited by 12 (1 self)
 Add to MetaCart
(Show Context)
Abstract—The performance of dynamic scene algorithms often suffers because of the inability to effectively acquire features on the targets, particularly when they are distributed over a wide field of view. In this paper, we propose an integrated analysis and control framework for a pan, tilt, zoom (PTZ) camera network in order to maximize various scene understanding performance criteria (e.g., tracking accuracy, best shot, and image resolution) through dynamic cameratotarget assignment and efficient feature acquisition. Moreover, we consider the situation where processing is distributed across the network since it is often unrealistic to have all the image data at a central location. In such situations, the cameras, although autonomous, must collaborate among themselves because each camera’s PTZ parameter entails constraints on the others. Motivated by recent work in cooperative control of sensor networks, we propose a distributed optimization strategy, which can be modeled as a game involving the cameras and targets. The cameras gain by reducing the error covariance of the tracked targets or through higher resolution feature acquisition, which, however, comes at the risk of losing the dynamic target. Through the optimization of this rewardversusrisk tradeoff, we are able to control the PTZ parameters of the cameras and assign them to targets dynamically. The tracks, upon which the control algorithm is dependent, are obtained through a consensus estimation algorithm whereby cameras can arrive at a consensus on the state of each target through a negotiation strategy. We analyze the performance of this collaborative sensing strategy in active camera networks in a simulation environment, as well as a reallife camera network. Index Terms—Camera networks, cooperative camera control, distributed estimation, game theory, video analysis. I.
Decoupling Coupled Constraints Through Utility Design
"... The central goal in multiagent systems is to engineer a decision making architecture where agents make independent decisions in response to local information while ensuring that the emergent global behavior is desirable with respect to a given system level objective. In many systems this control de ..."
Abstract

Cited by 10 (3 self)
 Add to MetaCart
The central goal in multiagent systems is to engineer a decision making architecture where agents make independent decisions in response to local information while ensuring that the emergent global behavior is desirable with respect to a given system level objective. In many systems this control design is further complicated by coupled constraints on the agents’ behavior. This paper seeks to address the design of such algorithms using the field of game theory. In particular, we derive a systematic methodology for designing local agent utility functions such that (i) all resulting pure Nash equilibria of the designed game optimize the given system level objective and satisfy the given coupled constraint (ii) the resulting game possesses an inherent structure that can be exploited in distributed learning, e.g., potential games. Such developments would greatly simplify the control design by eliminating the need to explicitly consider the constraint. One key to this realization is introducing an estimate of the coupled constraint and incorporating exterior penalty functions and barrier functions into the design of the agents’ utility functions.
Game theoretic methods for the smart grid
 IEEE Signal Processing Magazine
, 2012
"... ar ..."
(Show Context)
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 ..."
Abstract

Cited by 8 (2 self)
 Add to MetaCart
(Show Context)
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
Cooperative topology control with adaptation for improved lifetime in wireless ad hoc networks
 in Proc. IEEE INFOCOM
, 2012
"... Topology control algorithms allow each node in a wireless multihop network to adjust the power at which it makes its transmissions and choose the set of neighbors with which it communicates directly, while preserving global goals such as connectivity or coverage. This allows each node to conserve e ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
(Show Context)
Topology control algorithms allow each node in a wireless multihop network to adjust the power at which it makes its transmissions and choose the set of neighbors with which it communicates directly, while preserving global goals such as connectivity or coverage. This allows each node to conserve energy and contribute to increasing the lifetime of the network. In this paper, in contrast to most previous work, we consider (i) both the energy costs of communication as well as the amount of available energy at each node, (ii) the realistic situation of varying rates of energy consumption at different nodes, and (iii) the fact that cooperation between nodes, where some nodes make a sacrifice by increasing energy consumption to help other nodes reduce their consumption, can be used to extend network lifetime. This paper introduces a new distributed topology control algorithm, called the Cooperative Topology Control with Adaptation (CTCA), based on a gametheoretic approach that maps the problem of maximizing the network’s lifetime into an ordinal potential game. We prove the existence of a Nash equilibrium for the game. Our simulation results indicate that the CTCA algorithm extends the life of a network by more than 50 % compared to the best previouslyknown algorithm. We also study the performance of the distributed CTCA algorithm in comparison to an optimal centralized algorithm as a function of the communication ranges of nodes and node density. ar X iv
Game Couplings: Learning Dynamics and Applications
"... Modern engineering systems (such as the Internet) consist of multiple coupled subsystems. Such subsystems are designed with local (possibly conflicting) goals, with little or no knowledge of the implementation details of other subsystems. Despite the ubiquitous nature of such systems very little is ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
(Show Context)
Modern engineering systems (such as the Internet) consist of multiple coupled subsystems. Such subsystems are designed with local (possibly conflicting) goals, with little or no knowledge of the implementation details of other subsystems. Despite the ubiquitous nature of such systems very little is formally known about their properties and global dynamics. We investigate such distributed systems by introducing a novel gametheoretic construct, that we call gamecoupling. Game coupling intuitively allows us to stitch together the payoff structures of subgames. In order to study efficiency issues, we extend the price of anarchy approach (a major focus of gametheoretical multiagent systems [22]) to this setting, where we now care about the performance of each individual subsystem as well as the global performance. Such concerns give rise to a new notion of equilibrium, as well as a new learning paradigm. We prove matching welfare guarantees for both, both for individual subsystems as well as for the global system, using a generalization of the (λ, µ)smoothness framework [19]. In the second part of the paper, we work on understanding conditions that allow for wellstructured couplings. More generally, we examine when do game couplings preserve or enhance desirable properties of the original games, such as convergence of best response dynamics and low price of anarchy.
Payoffbased Inhomogeneous Partially Irrational Play for Potential Game Theoretic Cooperative Control: Convergence Analysis
"... Abstract — This paper investigates learning algorithm design in potential game theoretic cooperative control, where it is in general required for agents ’ collective action to converge to the most efficient equilibria while standard game theory aims at just computing a Nash equilibrium. In particula ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
(Show Context)
Abstract — This paper investigates learning algorithm design in potential game theoretic cooperative control, where it is in general required for agents ’ collective action to converge to the most efficient equilibria while standard game theory aims at just computing a Nash equilibrium. In particular, the equilibria maximizing the potential function should be selected in case the utility functions are already aligned to a global objective function. In order to meet the requirement, this paper develops a learning algorithm called Payoffbased Inhomogeneous Partially Irrational Play (PIPIP). The main feature of PIPIP is to allow agents to make irrational decisions with a specified probability, i.e. agents can choose an action with a low utility from the past actions stored in the memory. We then prove convergence in probability of the collective action to the potential function maximizers. Finally, the effectiveness of the present algorithm is demonstrated through simulation on a sensor coverage problem. I.
Cooperative learning in multiagent systems from intermittent measurements
"... Abstract — Motivated by the problem of decentralized directiontracking, we consider the general problem of cooperative learning in multiagent systems with timevarying connectivity and intermittent measurements. We propose a distributed learning protocol capable of learning an unknown vector µ fro ..."
Abstract

Cited by 1 (0 self)
 Add to MetaCart
(Show Context)
Abstract — Motivated by the problem of decentralized directiontracking, we consider the general problem of cooperative learning in multiagent systems with timevarying connectivity and intermittent measurements. We propose a distributed learning protocol capable of learning an unknown vector µ from noisy measurements made independently by autonomous nodes. Our protocol is completely distributed and able to cope with the timevarying, unpredictable, and noisy nature of interagent communication, and intermittent noisy measurements of µ. Our main result bounds the learning speed of our protocol in terms of the size and combinatorial features of the (timevarying) network connecting the nodes. I.