1. Collusion Resistant Reputation Mechanism for Multi Agent Systems BabakKhosravifar, Jamal Bentahar, MaziarGomrokchiand MahsaAlishahi Concordia University, Montreal, Canada 1

2. Outline Preliminaries The Model Results Conclusion References 2 Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi

3. Outline Preliminaries The Model Results Conclusion References 3 Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi

4. 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

5. Preliminaries Agent Multi agent system 5 Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi

6. 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

7. Task Announcement Manager Node Issues Task Announcement 7 Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi

8. 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

9. 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

10. 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

11. Award Manager Contractor Manager Making an Award 11 Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi

12. Contract Manager Contractor Contract Established 12 Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi

13. Outline Preliminaries The Model Results Conclusion References 13 Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi

14. 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

15. 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

16. Outline Preliminaries The Model Results Conclusion References 16 Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi

17. 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

18. Results Penalizing probability Expected Payoffs 18 Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi

19. 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

20. 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

21. Results 21 Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi

22. Outline Preliminaries The Model Results Conclusion References 22 Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi

23. 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

24. 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