ijcai 2011 presentation

COLLUSION RESISTANT REPUTATION

MECHANISM FOR MULTI AGENT

SYSTEMS

Babak Khosravifar, Jamal Bentahar, Maziar Gomrokchi

and Mahsa Alishahi

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


PRELIMINARIES

Agent

Environment

Agent

see action

next state

4


PRELIMINARIES

Agent

Multi agent system

5


PRELIMINARIES

Agent

Multi agent system

Knowledge

Trust and Reputation

Web service agent

Consumer agent

Collusion

6


Manager

Task Announcement

NODE ISSUES TASK ANNOUNCEMENT

7


Manager

Manager

Manager

PotentialContractor

IDLE NODE LISTENING TO TASK

ANNOUNCEMENTS

8


Manager

PotentialContractor

Bid

NODE SUBMITTING A BID

9


Manager

PotentialContractor

PotentialContractor

Bids

MANAGER LISTENING TO BIDS

10


Manager

Contractor

Award

MANAGER MAKING AN AWARD

11


Manager

Contractor

Contract

CONTRACT ESTABLISHED

12


OUTLINE

Preliminaries

The Model

Results

Conclusion

References

13


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 ( )

W

cw

cdf

Pn

14


THE MODEL

Four possible scenarios

Actual collusion is detected

Actual collusion is ignored

Truthful action is penalized

Truthful action is detected

15


OUTLINE

Preliminaries

The Model

Results

Conclusion

References

16


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


RESULTS

Penalizing probability

Expected Payoffs

18


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


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


RESULTS

21


OUTLINE

Preliminaries

The Model

Results

Conclusion

References

22


CONCLUSION

Reputation mechanism

Collusion analysis

Collusion resistant structure

Best response analysis

Three player game

Learning methods

MDP/PO-MDP

23


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.

24


ijcai 2011 presentation

Education