ucs presentation 2011

COLLUSION RESISTANT REPUTATION MECHANISM FOR MULTI AGENT SYSTEMS

Babak Khosravifar

Concordia University, Montreal, Canada

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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

OUTLINE


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PRELIMINARIES

¢ Agent ¢ Multi agent system ¢ Knowledge ¢ Trust and Reputation ¢ Multi agent trading environment

�  Web service agent �  Consumer agent

¢ Collusion

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PRELIMINARIES

¢ Reputation mechanism �  Feedback pool �  Feedback aggregation method �  Feedback posting incentives �  Feedback accuracy checking �  Consistent reputation update �  Sound reputation management

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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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OUTLINE


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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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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 ( )

βλ ΨW

cw

cdf

Pn

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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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OUTLINE


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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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RESULTS

¢ Penalizing probability

¢ Expected Payoffs

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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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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.

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RESULTS

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OUTLINE


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CONCLUSION

¢ Reputation mechanism ¢ Collusion analysis ¢ Collusion resistant structure ¢ Best response analysis

¢ Three player game ¢ Learning methods ¢ MDP/PO-MDP

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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. of 9’th Int. Conf. on Autonomous Agent and Multi Agent Systems (AAMAS), pages 225–232, 2010.

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ucs presentation 2011

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