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IJCAI 2011 Presentation IJCAI 2011 Presentation Presentation Transcript

  • Collusion Resistant Reputation Mechanism for Multi Agent Systems
    BabakKhosravifar, Jamal Bentahar, MaziarGomrokchiand MahsaAlishahi
    Concordia University, Montreal, Canada
    1
  • Outline
    Preliminaries
    The Model
    Results
    Conclusion
    References
    2
    Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi
  • Outline
    Preliminaries
    The Model
    Results
    Conclusion
    References
    3
    Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi
  • Preliminaries
    Agent
    Agent
    see
    action
    state
    next
    Environment
    4
    Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi
  • Preliminaries
    Agent
    Multi agent system
    5
    Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi
  • Preliminaries
    Agent
    Multi agent system
    Knowledge
    Trust and Reputation
    Web service agent
    Consumer agent
    Collusion
    6
    Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi
  • Task Announcement
    Manager
    Node Issues Task Announcement
    7
    Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi
  • Manager
    Potential
    Contractor
    Manager
    Manager
    Idle Node Listening to Task Announcements
    8
    Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi
  • Bid
    Manager
    Potential
    Contractor
    Node Submitting a Bid
    9
    Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi
  • Bids
    Potential
    Contractor
    Manager
    Potential
    Contractor
    Manager listening to bids
    10
    Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi
  • Award
    Manager
    Contractor
    Manager Making an Award
    11
    Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi
  • Contract
    Manager
    Contractor
    Contract Established
    12
    Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi
  • Outline
    Preliminaries
    The Model
    Results
    Conclusion
    References
    13
    Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi
  • The Model
    Consumer/Provider strategy profile
    Collusion Benefits
    Consumer agent ( ε)
    Web service agent ( )
    Controller agent’s investigation parameters
    Analyzing feedback window ( )
    Detecting fake feedback ( )
    Penalty ( )
    14
    Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi
  • The model
    Four possible scenarios
    Actual collusion is detected
    Actual collusion is ignored
    Truthful action is penalized
    Truthful action is detected
    15
    Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi
  • Outline
    Preliminaries
    The Model
    Results
    Conclusion
    References
    16
    Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi
  • Results
    In repeated game with decision making process, if the falsely detected feedback is more that correctly detected ones, web service and consumer agents choose collusion as dominant strategy.
    Penalizing the collusion is Pure Strategy Nash Equilibrium.
    17
    Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi
  • Results
    Penalizing probability
    Expected Payoffs
    18
    Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi
  • Results
    Estimated penalizing probability
    In mixed strategy repeated games, there is a threshold μ such that if qw > μ acting truthful would be the dominant strategy.
    19
    Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi
  • Results
    If the estimated probability of penalizing exceeds the obtained threshold, acting truthful and not being penalized would be the Mixed Strategy Nash Equilibrium.
    A collusion resistant reputation mechanism is achieved when the controller agent maximizes the following value.
    20
    Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi
  • Results
    21
    Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi
  • Outline
    Preliminaries
    The Model
    Results
    Conclusion
    References
    22
    Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi
  • Conclusion
    Reputation mechanism
    Collusion analysis
    Collusion resistant structure
    Best response analysis
    Three player game
    Learning methods
    MDP/PO-MDP
    23
    Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi
  • References
    Archie Chapman, Alex Rogers, Nicholas Jennings, and David Leslie. A unifying framework for iterative approximate best response algorithms for distributed constraint optimization problems. Knowledge Engineering Review (in press), 2011.
    RaduJurca and BoiFaltings. Collusion-resistant, incentive-compatible feedback payments. In Proc. of the ACM Conf. on E-Commerce, pages 200–209, 2007.
    RaduJurca, BoiFaltings, andWalter Binder. Reliable QoS monitoring based on client feedback. In Proc. of the 16’th Int. World Wide Web Conf., pages 1003–1011, 2007.
    Georgia Kastidou, Kate Larson, and Robin Cohen. Exchanging reputation information between communities: A payment-function approach. In Proc. of the 21st Int. Joint Conf. on Artificial Intelligence (IJCAI), pages 195–200, 2009.
    BabakKhosravifar, Jamal Bentahar, Philippe Thiran, Ahmad Moazin, and AddrienGuiot. An approach to incentive-based reputation for communities of web services. In Proc. of IEEE 7’th Int. Con. on Web Services (ICWS), pages 303–310, 2009.
    BabakKhosravifar, Jamal Bentahar, Ahmed Moazin, and Philippe Thiran. On the reputation of agent-based web services. In Proc. of the 24’th Conf. on Artificial Intelligence (AAAI), pages 1352–1357, 2010.
    E. Michael Maximilien and Munindar P. Singh. Conceptual model of web service reputation. SIGMOD Record, ACM Special Interest Group on Management of Data, 31(4):36– 41, 2002.
    George Vogiatzis, Ian MacGillivray, and Maria Chli. A probabilistic model for trust and reputation. In Proc. of 9’th Int. Conf. on Autonomous Agent and Multi Agent Systems (AAMAS), pages 225–232, 2010.
    24
    Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi