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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