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Daniel Pareja - One of the best experts on this subject based on the ideXlab platform.

  • multi round master worker computing a Repeated Game approach
    Symposium on Reliable Distributed Systems, 2016
    Co-Authors: Antonio Fernandez Anta, Chryssis Georgiou, Miguel A Mosteiro, Daniel Pareja
    Abstract:

    We consider a computing system where a master processor assigns tasks for execution to worker processors through the Internet. We model the workers' decision of whether to comply (compute the task) or not (return a bogus result to save the computation cost) as a mixed extension of a strategic Game among workers. That is, we assume that workers are rational in a Game-theoretic sense, and that they randomize their strategic choice. Workers are assigned multiple tasks in subsequent rounds. We model the system as an infinitely Repeated Game of the mixed extension of the strategic Game. In each round, the master decides stochastically whether to accept the answer of the majority or verify the answers received, at some cost. Incentives and/or penalties are applied to workers accordingly. Under the above framework, we study the conditions in which the master can reliably obtain tasks results, exploiting that the Repeated Game model captures the effect of long-term interaction. That is, workers take into account that their behavior in one computation will have an effect on the behavior of other workers in the future. Indeed, should a worker be found to deviate from some agreed strategic choice, the remaining workers would change their own strategy to penalize the deviator. Hence, being rational, workers do not deviate. We identify analytically the parameter conditions to induce a desired worker behavior, and we evaluate experimentally the mechanisms derived from such conditions. We also compare the performance of our mechanisms with a previously known multi-round mechanism based on reinforcement learning.

  • SRDS - Multi-round Master-Worker Computing: A Repeated Game Approach
    2016 IEEE 35th Symposium on Reliable Distributed Systems (SRDS), 2016
    Co-Authors: Antonio Fernandez Anta, Chryssis Georgiou, Miguel A Mosteiro, Daniel Pareja
    Abstract:

    We consider a computing system where a master processor assigns tasks for execution to worker processors through the Internet. We model the workers' decision of whether to comply (compute the task) or not (return a bogus result to save the computation cost) as a mixed extension of a strategic Game among workers. That is, we assume that workers are rational in a Game-theoretic sense, and that they randomize their strategic choice. Workers are assigned multiple tasks in subsequent rounds. We model the system as an infinitely Repeated Game of the mixed extension of the strategic Game. In each round, the master decides stochastically whether to accept the answer of the majority or verify the answers received, at some cost. Incentives and/or penalties are applied to workers accordingly. Under the above framework, we study the conditions in which the master can reliably obtain tasks results, exploiting that the Repeated Game model captures the effect of long-term interaction. That is, workers take into account that their behavior in one computation will have an effect on the behavior of other workers in the future. Indeed, should a worker be found to deviate from some agreed strategic choice, the remaining workers would change their own strategy to penalize the deviator. Hence, being rational, workers do not deviate. We identify analytically the parameter conditions to induce a desired worker behavior, and we evaluate experimentally the mechanisms derived from such conditions. We also compare the performance of our mechanisms with a previously known multi-round mechanism based on reinforcement learning.

Antonio Fernandez Anta - One of the best experts on this subject based on the ideXlab platform.

  • multi round master worker computing a Repeated Game approach
    Symposium on Reliable Distributed Systems, 2016
    Co-Authors: Antonio Fernandez Anta, Chryssis Georgiou, Miguel A Mosteiro, Daniel Pareja
    Abstract:

    We consider a computing system where a master processor assigns tasks for execution to worker processors through the Internet. We model the workers' decision of whether to comply (compute the task) or not (return a bogus result to save the computation cost) as a mixed extension of a strategic Game among workers. That is, we assume that workers are rational in a Game-theoretic sense, and that they randomize their strategic choice. Workers are assigned multiple tasks in subsequent rounds. We model the system as an infinitely Repeated Game of the mixed extension of the strategic Game. In each round, the master decides stochastically whether to accept the answer of the majority or verify the answers received, at some cost. Incentives and/or penalties are applied to workers accordingly. Under the above framework, we study the conditions in which the master can reliably obtain tasks results, exploiting that the Repeated Game model captures the effect of long-term interaction. That is, workers take into account that their behavior in one computation will have an effect on the behavior of other workers in the future. Indeed, should a worker be found to deviate from some agreed strategic choice, the remaining workers would change their own strategy to penalize the deviator. Hence, being rational, workers do not deviate. We identify analytically the parameter conditions to induce a desired worker behavior, and we evaluate experimentally the mechanisms derived from such conditions. We also compare the performance of our mechanisms with a previously known multi-round mechanism based on reinforcement learning.

  • SRDS - Multi-round Master-Worker Computing: A Repeated Game Approach
    2016 IEEE 35th Symposium on Reliable Distributed Systems (SRDS), 2016
    Co-Authors: Antonio Fernandez Anta, Chryssis Georgiou, Miguel A Mosteiro, Daniel Pareja
    Abstract:

    We consider a computing system where a master processor assigns tasks for execution to worker processors through the Internet. We model the workers' decision of whether to comply (compute the task) or not (return a bogus result to save the computation cost) as a mixed extension of a strategic Game among workers. That is, we assume that workers are rational in a Game-theoretic sense, and that they randomize their strategic choice. Workers are assigned multiple tasks in subsequent rounds. We model the system as an infinitely Repeated Game of the mixed extension of the strategic Game. In each round, the master decides stochastically whether to accept the answer of the majority or verify the answers received, at some cost. Incentives and/or penalties are applied to workers accordingly. Under the above framework, we study the conditions in which the master can reliably obtain tasks results, exploiting that the Repeated Game model captures the effect of long-term interaction. That is, workers take into account that their behavior in one computation will have an effect on the behavior of other workers in the future. Indeed, should a worker be found to deviate from some agreed strategic choice, the remaining workers would change their own strategy to penalize the deviator. Hence, being rational, workers do not deviate. We identify analytically the parameter conditions to induce a desired worker behavior, and we evaluate experimentally the mechanisms derived from such conditions. We also compare the performance of our mechanisms with a previously known multi-round mechanism based on reinforcement learning.

Miguel A Mosteiro - One of the best experts on this subject based on the ideXlab platform.

  • multi round master worker computing a Repeated Game approach
    Symposium on Reliable Distributed Systems, 2016
    Co-Authors: Antonio Fernandez Anta, Chryssis Georgiou, Miguel A Mosteiro, Daniel Pareja
    Abstract:

    We consider a computing system where a master processor assigns tasks for execution to worker processors through the Internet. We model the workers' decision of whether to comply (compute the task) or not (return a bogus result to save the computation cost) as a mixed extension of a strategic Game among workers. That is, we assume that workers are rational in a Game-theoretic sense, and that they randomize their strategic choice. Workers are assigned multiple tasks in subsequent rounds. We model the system as an infinitely Repeated Game of the mixed extension of the strategic Game. In each round, the master decides stochastically whether to accept the answer of the majority or verify the answers received, at some cost. Incentives and/or penalties are applied to workers accordingly. Under the above framework, we study the conditions in which the master can reliably obtain tasks results, exploiting that the Repeated Game model captures the effect of long-term interaction. That is, workers take into account that their behavior in one computation will have an effect on the behavior of other workers in the future. Indeed, should a worker be found to deviate from some agreed strategic choice, the remaining workers would change their own strategy to penalize the deviator. Hence, being rational, workers do not deviate. We identify analytically the parameter conditions to induce a desired worker behavior, and we evaluate experimentally the mechanisms derived from such conditions. We also compare the performance of our mechanisms with a previously known multi-round mechanism based on reinforcement learning.

  • SRDS - Multi-round Master-Worker Computing: A Repeated Game Approach
    2016 IEEE 35th Symposium on Reliable Distributed Systems (SRDS), 2016
    Co-Authors: Antonio Fernandez Anta, Chryssis Georgiou, Miguel A Mosteiro, Daniel Pareja
    Abstract:

    We consider a computing system where a master processor assigns tasks for execution to worker processors through the Internet. We model the workers' decision of whether to comply (compute the task) or not (return a bogus result to save the computation cost) as a mixed extension of a strategic Game among workers. That is, we assume that workers are rational in a Game-theoretic sense, and that they randomize their strategic choice. Workers are assigned multiple tasks in subsequent rounds. We model the system as an infinitely Repeated Game of the mixed extension of the strategic Game. In each round, the master decides stochastically whether to accept the answer of the majority or verify the answers received, at some cost. Incentives and/or penalties are applied to workers accordingly. Under the above framework, we study the conditions in which the master can reliably obtain tasks results, exploiting that the Repeated Game model captures the effect of long-term interaction. That is, workers take into account that their behavior in one computation will have an effect on the behavior of other workers in the future. Indeed, should a worker be found to deviate from some agreed strategic choice, the remaining workers would change their own strategy to penalize the deviator. Hence, being rational, workers do not deviate. We identify analytically the parameter conditions to induce a desired worker behavior, and we evaluate experimentally the mechanisms derived from such conditions. We also compare the performance of our mechanisms with a previously known multi-round mechanism based on reinforcement learning.

Chryssis Georgiou - One of the best experts on this subject based on the ideXlab platform.

  • multi round master worker computing a Repeated Game approach
    Symposium on Reliable Distributed Systems, 2016
    Co-Authors: Antonio Fernandez Anta, Chryssis Georgiou, Miguel A Mosteiro, Daniel Pareja
    Abstract:

    We consider a computing system where a master processor assigns tasks for execution to worker processors through the Internet. We model the workers' decision of whether to comply (compute the task) or not (return a bogus result to save the computation cost) as a mixed extension of a strategic Game among workers. That is, we assume that workers are rational in a Game-theoretic sense, and that they randomize their strategic choice. Workers are assigned multiple tasks in subsequent rounds. We model the system as an infinitely Repeated Game of the mixed extension of the strategic Game. In each round, the master decides stochastically whether to accept the answer of the majority or verify the answers received, at some cost. Incentives and/or penalties are applied to workers accordingly. Under the above framework, we study the conditions in which the master can reliably obtain tasks results, exploiting that the Repeated Game model captures the effect of long-term interaction. That is, workers take into account that their behavior in one computation will have an effect on the behavior of other workers in the future. Indeed, should a worker be found to deviate from some agreed strategic choice, the remaining workers would change their own strategy to penalize the deviator. Hence, being rational, workers do not deviate. We identify analytically the parameter conditions to induce a desired worker behavior, and we evaluate experimentally the mechanisms derived from such conditions. We also compare the performance of our mechanisms with a previously known multi-round mechanism based on reinforcement learning.

  • SRDS - Multi-round Master-Worker Computing: A Repeated Game Approach
    2016 IEEE 35th Symposium on Reliable Distributed Systems (SRDS), 2016
    Co-Authors: Antonio Fernandez Anta, Chryssis Georgiou, Miguel A Mosteiro, Daniel Pareja
    Abstract:

    We consider a computing system where a master processor assigns tasks for execution to worker processors through the Internet. We model the workers' decision of whether to comply (compute the task) or not (return a bogus result to save the computation cost) as a mixed extension of a strategic Game among workers. That is, we assume that workers are rational in a Game-theoretic sense, and that they randomize their strategic choice. Workers are assigned multiple tasks in subsequent rounds. We model the system as an infinitely Repeated Game of the mixed extension of the strategic Game. In each round, the master decides stochastically whether to accept the answer of the majority or verify the answers received, at some cost. Incentives and/or penalties are applied to workers accordingly. Under the above framework, we study the conditions in which the master can reliably obtain tasks results, exploiting that the Repeated Game model captures the effect of long-term interaction. That is, workers take into account that their behavior in one computation will have an effect on the behavior of other workers in the future. Indeed, should a worker be found to deviate from some agreed strategic choice, the remaining workers would change their own strategy to penalize the deviator. Hence, being rational, workers do not deviate. We identify analytically the parameter conditions to induce a desired worker behavior, and we evaluate experimentally the mechanisms derived from such conditions. We also compare the performance of our mechanisms with a previously known multi-round mechanism based on reinforcement learning.

Shuva Paul - One of the best experts on this subject based on the ideXlab platform.

  • A Learning-Based Solution for an Adversarial Repeated Game in Cyber-Physical Power Systems.
    IEEE transactions on neural networks and learning systems, 2020
    Co-Authors: Shuva Paul
    Abstract:

    Due to the rapidly expanding complexity of the cyber-physical power systems, the probability of a system malfunctioning and failing is increasing. Most of the existing works combining smart grid (SG) security and Game theory fail to replicate the adversarial events in the simulated environment close to the real-life events. In this article, a Repeated Game is formulated to mimic the real-life interactions between the adversaries of the modern electric power system. The optimal action strategies for different environment settings are analyzed. The advantage of the Repeated Game is that the players can generate actions independent of the previous actions' history. The solution of the Game is designed based on the reinforcement learning algorithm, which ensures the desired outcome in favor of the players. The outcome in favor of a player means achieving higher mixed strategy payoff compared to the other player. Different from the existing Game-theoretic approaches, both the attacker and the defender participate actively in the Game and learn the sequence of actions applying to the power transmission lines. In this Game, we consider several factors (e.g., attack and defense costs, allocated budgets, and the players' strengths) that could affect the outcome of the Game. These considerations make the Game close to real-life events. To evaluate the Game outcome, both players' utilities are compared, and they reflect how much power is lost due to the attacks and how much power is saved due to the defenses. The players' favorable outcome is achieved for different attack and defense strengths (probabilities). The IEEE 39 bus system is used here as the test benchmark. Learned attack and defense strategies are applied in a simulated power system environment (PowerWorld) to illustrate the postattack effects on the system.

  • ISGT - A Strategic Analysis of Attacker-Defender Repeated Game in Smart Grid Security
    2019 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2019
    Co-Authors: Shuva Paul, Zhen Ni
    Abstract:

    Traditional power grid security schemes are being replaced by highly advanced and efficient smart security schemes due to the advancement in grid structure and inclusion of cyber control and monitoring tools. Smart attackers create physical, cyber, or cyber-physical attacks to gain the access of the power system and manipulate/override system status, measurements and commands. In this paper, we formulate the environment for the attacker-defender interaction in the smart power grid. We provide a strategic analysis of the attacker-defender strategic interaction using a Game theoretic approach. We apply Repeated Game to formulate the problem, implement it in the power system, and investigate for optimal strategic behavior in terms of mixed strategies of the players. In order to define the utility or cost function for the Game payoffs calculation, generation power is used. Attack-defense budget is also incorporated with the attacker-defender Repeated Game to reflect a more realistic scenario. The proposed Game model is validated using IEEE 39 bus benchmark system. A comparison between the proposed Game model and the all monitoring model is provided to validate the observations.