1. COLLUSION RESISTANT
REPUTATION MECHANISM FOR
MULTI AGENT SYSTEMS
1 Babak Khosravifar
Concordia University, Montreal, Canada

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

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

4. PRELIMINARIES
¢ Agent
¢ Multi agent system
¢ Knowledge
¢ Trust and Reputation
¢ Multi agent trading environment
— Web service agent
— Consumer agent
¢ Collusion
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Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi

5. PRELIMINARIES
¢ Reputation mechanism
— Feedback pool
— Feedback aggregation method
— Feedback posting incentives
— Feedback accuracy checking
— Consistent reputation update
— Sound reputation management
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Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi

6. PRELIMINARIES
¢ Agents’ goals
— Acceptable service quality for service consumers
— Maximum (long-term) income for service providers
— Maximum (long-term) performance in reputation
mechanism
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Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi

7. OUTLINE
¢ Preliminaries
¢ The Model
¢ Results
¢ Conclusion
¢ References
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Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi

8. THE MODEL
¢ Consumer agent looks for service provider
¢ Provider agent provides the requested service
¢ Corresponding satisfaction feedback is posted
¢ Reputation mechanism updates the reputation
values
¢ Provider’s income parameters
— Mean periodic request λ
— Service fee β
— Request boost parameter Ψ
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Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi

9. THE MODEL
¢ Consumer/Provider strategy profile
¢ Collusion Benefits
— Consumer agent ( ε )
— Web service agent ( λW Ψβ )
¢ Controller agent’s investigation parameters
— Analyzing feedback window (wc )
— Detecting fake feedback ( df c )
— Penalty (Pn)
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Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi

10. THE MODEL
¢ Four possible scenarios
— Actual collusion is detected
— Actual collusion is ignored
— Truthful action is penalized
— Truthful action is detected
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Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi

11. OUTLINE
¢ Preliminaries
¢ The Model
¢ Results
¢ Conclusion
¢ References
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Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi

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

13. RESULTS
¢ Penalizing probability
¢ Expected Payoffs
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Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi

14. 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.
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Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi

15. RESULTS
¢ Ifthe 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.
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Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi

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

17. OUTLINE
¢ Preliminaries
¢ The Model
¢ Results
¢ Conclusion
¢ References
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Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi

18. CONCLUSION
¢ Reputation mechanism
¢ Collusion analysis
¢ Collusion resistant structure
¢ Best response analysis
¢ Three player game
¢ Learning methods
¢ MDP/PO-MDP
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Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi

19. 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.
¢ Radu Jurca and Boi Faltings. Collusion-resistant, incentive-compatible feedback payments. In Proc. of the
ACM Conf. on E-Commerce, pages 200–209, 2007.
¢ Radu Jurca, Boi Faltings, 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.
¢ Babak Khosravifar, Jamal Bentahar, Philippe Thiran, Ahmad Moazin, and Addrien Guiot. 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.
¢ Babak Khosravifar, 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. 19
of 9’th Int. Conf. on Autonomous Agent and Multi Agent Systems (AAMAS), pages 225–232, 2010.
Collusion Resistant Reputation Mechanism for Multi Agent Systems B. Khosravifar, J. Bentahar, M. Gomrokchi, M. Alishahi