computational trust and reputation models andrew diniz da costa [email protected]

Computational Trust andReputation Models

Andrew Diniz da [email protected]

2Andrew Diniz da Costa © LES/PUC-Rio

Presentation Outline

• Part 1: Introduction

– Motivation

– Some definitions

• Part 2: Computational trust and reputation models

– eBay/OnSale

– SPORAS & HISTOS

– Fire Model

– Governance Framework

• Part 3: ART-Testbed

– Overview




– Motivation



– eBay/OnSale

– SPORAS & HISTOS

– Fire Model



– Overview


What we are talking about ...


Advantages of trust and reputation mechanisms

• Agents can obtain data from others agents.

• Shared experience.

• Decide on which to trust


Problems of trust and reputation mechanisms

• Not all kind of environments are suitable to apply these mechanisms.

• Exclusion must be a punishment

• What is trust?

• What is reputation?


Trust

• Some statements we like:

• “Trust begins where knowledge ends: trust provides a basis dealing with uncertain,complex,and threatening images of the future.” [Luhmann,1979]

• “There are no obvious units in which trust can be measured,” [Dasgupta, 2000]


Reputation

• Some definitions:

• “The estimation of the consistency over time of an attribute or entity” [Herbig et al.]

• “Information that individuals receive about the behaviour of their partners from third parties and that they use to decide how to behave themselves” [Buskens, Coleman...]

• “The opinion others have of us”


What is a good trust model?

• A good trust model should be [Fullam et al, 05]:

• Accurate

– provide good previsions

• Adaptive

– evolve according to behaviour of others

• Multi-dimensional

– Consider different agent characteristics

• Efficient

– Compute in reasonable time and cost


Why using a trust model in a MAS ?

• Trust models allow:

– Identifying and isolating untrustworthy agents





– Evaluating an interaction’s utility





– Evaluating an interaction’s utility

– Deciding whether and with whom to interact




– Motivation



– eBay/OnSale

– SPORAS & HISTOS

– Fire model



– Overview


eBay model

• Context: e-commerce

– Model oriented to support trust between buyer and seller

– Buyer has no physical access to the product of interest

– Seller or buyer may decide not to commit the transaction

– Centralized: all information remains on eBay Servers

• Buyers and sellers evaluate each other after transactions

• The evaluation is not mandatory and will never be removed

• Each eBay member has a “reputation” (feedback score) that is the summation of the numerical evaluations.


eBay model


SPORAS & HISTOS

• Context: e-commerce, similar to eBay

• An individual may have a very high reputation in one domain, while she has a low reputation in another.

• Two models are proposed:– Sporas: works even with few evaluations (ratings)

– Histos: assumes abundance of evaluations

• Ratings given by users with a high reputation are weighted more

• Reputation values are not allowed to increase at infinitum


SPORAS & HISTOS

• SPORAS

– Reputations are in [0, 3000]. Newcommers = 0. Ratings are in [0.1, 1]

– Reputations never get below 0, even in the case of very bad behaviours

– After each rating the reputation is updated

• HISTOS

– Aim: compute a global ‘personalized reputation’ value for each member


Fire Model

• Three types of reputation

– Interaction trust

– Witness reputation

– Certified reputation

* Huynh, T. D., Jennings, N. R. and Shadbolt, N. (2004) FIRE: an integrated trust and reputation model for open multi-agent systems. In: 16th European Conference on Artificial Intelligence, 2004, Valencia, Spain.


Fire Model

• Interaction trust

– resulting from past experiences from direct interactions

– Between [-1, +1]

– -1 means absolutely negative

– +1 means absolutely positive

– 0 means neutral or uncertain

Agent A Agent B

Interaction Trust of the Agent B(price, quality, etc)

Request

Provide


Fire Model

• Witness reputation

– reports of witness about an agent’s behaviour

Agent A

Agent C

Agent D

Agent E

Agent B

Request witness

Request witness

Request witness

Agent C knows Agent B

Agent D knows Agent B

Agent E knows Agent B


Fire Model

• Certified reputation

– references provided by other agents about its behaviour

Agent A

Agent D

Agent B

Agent C

What is your reputation

Evaluation of A made by the agent D

Evaluation of A made by the agent B

Evaluation of D made by the agent A

Evaluation of B made by the agent A0,5

-0,5

0,5


Governance Framework

- GUEDES, José ; SILVA, V. T. ; LUCENA, Carlos José Pereira de . A Reputation Model Based on Testimonies. In: Kolp, M, Garcia, A, Ghoze, C, Bresciani, P, Henderson-Sellers, B, Mouratidis, M.. (Org.). Agent-Oriented Information Systems.:

Springer-Verlag, 2008, v. LNAI, p. 37-52.

- DURAN, Feranda ; SILVA, V. T. ; LUCENA, Carlos José Pereira de . Using Testimonies to Enforce the behavior of Agents. In: Sichman, J., Noriega, P., Padget, J. and Ossowski, S.. (Org.). Coordination, Organizations, Institutions and Norms in Agent Systems III. : Springer-Verlag, 2008, v. LNAI, p. 218-231.


Governance Framework – Reputation System

• Three different kinds of reputations were defined:

– role reputation, norm reputation and global reputation.

• Role reputations only consider norms that were violated while playing a specified role or lies that were told while playing the role.

• Norm reputations focus on the violation of a norm and on the lies told while considering a norm.

• The global reputation of an agent considers all violated norms and all told lies.




– Motivation



– eBay/OnSale

– SPORAS & HISTOS

– Fire Model



– Overview


Motivation

• Trust in MAS is a young field of research, experiencing breadth-wise growth

– Many trust-modeling technologies

– Many metrics for empirical validation

• Lack of unified research direction

– No unified objective for trust technologies

– No unified performance metrics and benchmarks


An Experimental and Competition Testbed…

• Presents a common challenge to the research community

– Facilitates solving of prominent research problems

• Provides a versatile, universal site for experimentation

– Employs well-defined metrics

– Identifies successful technologies

• Matures the field of trust research

– Utilizes an exciting domain to attract attention of other researchers and the public


Domain


Reputation Transaction Protocol


Opinion Transaction Protocol


Simulator


Competition

• 17 agents (1 didn’t execute) of 13 different institutions

• Two phases– Preliminary– Final

• Preliminary phase (May 10-11)– 8 agents of the different institutions– 15 agents offered by competition (5 “bad”, 5 “neutral”, 5 “bad”

dummies )– 100 rounds

• Final phase (May 16-17)– 5 best agents of the preliminary phase– 15 agents offered by competition (5 “bad”, 5 “neutral”, 5 “bad”

dummies )– 200 rounds


Preliminary Phase


Final Phase

5) Pontifícia Universidade Católica do Rio de Janeiro

4) Agents Research Lab, University of Girona

3) Department of Computer Engineering, Bogazici University

2) Department of Math & Computer Science, The University of Tulsa

1) Electronics & Computer Science, University of Southampton


Conclusion

• ART-Testbed is being useful, however:

– What is reputation?

– Unreal Domain

• Researches have worked in domains of the industry to apply trust and reputation.

• Area is growing

• Famous researches are working in this area.


References

• [AbdulRahman, 97] A. Abdul-Rahman. The PGP trust model. EDI-Forum: the Journal of Electronic Commerce, 10(3):27–31, 1997.

• [Barber, 83] B. Barber, The Logic and Limits of Trust, The meanings of trust: Technical competence and fiduciary responsibility, Rutgers University Press, Rutgers, NJ, United States of America, 1983, p. 7-25.

• [Carbo et al., 03] J. Carbo and J. M. Molina and J. {Dávila Muro, Trust Management Through Fuzzy Reputation, International Journal of Cooperative Information Systems, 2003, vol. 12:1, p. 135-155.

• [Casare & Sichman, 05] S. J. Casare and J. S. Sichman, Towards a functional ontology of reputation, Proceedings of AAMAS’05, 2005.

• [Castelfranchi, 00] C. Castelfranchi, Engineering Social Order, Proceedings of ESAW’00, 2000.• [Castelfranchi & Falcone, 98] C. Castelfranchi and R. Falcone, Principles of trust for MAS:

Cognitive anatomy, social importance and quantification. Proc of ICMAS’98, pages 72-79, 1998.• [Conte & Paolucci, 02] R. Conte and M. Paolucci, Reputation in Artificial Societies. Social Beliefs for

Social Order, Kluwer Academic Publishers, G. Weiss (eds), Dordrecht, The Netherlands, 2002.• [Dellarocas, 00] C. Dellarocas, Immunizing online reputation reporting systems against unfair

ratings and discriminatory behavior, p. 150-157, Proceedings of the ACM Conference on "Electronic Commerce“ (EC'00), October, ACM Press, New York, NY, United States of America, 2000.

• [Dellarocas, 01] C. Dellarocas, Analyzing the economic efficiency of {eBay-like} online reputation reporting mechanisms, p. 171-179, Proceedings of the ACM Conference on "Electronic Commerce" (EC'01), October, ACM Press, New York, NY, United States of America, 2001.

• [Demolombe & Lorini, 08] R. Demolombe and E. Lorini, Trust and norms in the context of computer security: a logical formalization. Proc of DEON’08, LNAI, 1998.


References

• [Fullam et al, 05] K. Fullam, T. Klos, G. Muller, J. Sabater-Mir, A. Schlosser, Z. Topol, S. Barber, J. Rosenschein, L. Vercouter and M. Voss, A Specification of the Agent Reputation and Trust (ART) Testbed: Experimentation and Competition for Trust in Agent Societies, Proceedings of AAMAS’05, 2005.

• [Herzig et al, 08] A. Herzig, E. Lorini, J. F. Hubner, J. Ben-Naim, C. Castelfranchi, R. Demolombe, D. Longin and L. Vercouyter. Prolegomena for a logic of trust and reputation, submitted to Normas 08.

• [Luhmann, 79] N. Luhmann, Trust and Power, John Wiley \& Sons, 1979. [McKnight & Chervany, 02] D. H. McKnight and N. L. Chervany, What trust means in e-commerce customer relationship: an interdisciplinary conceptual typology, International Journal of Electronic Commerce, 2002.

• [Mui et al., 02] L. Mui and M. Mohtashemi and A. Halberstadt, Notions of Reputation in Multi-agent Systems: A Review, Proceedings of Autonomous Agents and Multi-Agent Systems (AAMAS'02), p. 280-287, 2002, C. Castelfranchi and W.L. Johnson (eds), Bologna, Italy, July, ACM Press, New York, NY, United States of America.

• [Muller & Vercouter, 05] G. Muller and L. Vercouter, Decentralized Monitoring of Agent Communication with a Reputation Model, Trusting Agents for trusting Electronic Societies, LNCS 3577, 2005.

• [Pearl, 88] Pearl, J. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, Morgan Kaufmann, San Francisco, 1988.

• [Rehák et al., 05] M. Rehák and M. Pěchouček and P. Benda and L. Foltn, Trust in Coalition Environment: Fuzzy Number Approach, Proceedings of the Workshop on "Trust in Agent Societies" at Autonomous Agents and Multi-Agent Systems (AAMAS'05), p. 132-144, 2005, C. Castelfranchi and S. Barber and J. Sabater and M. P. Singh (eds) Utrecht, The Netherlands, July.

• [Sabater, 04] Evaluating the ReGreT system Applied Artificial Intelligence, 18 (9-10) :797-813.• [Sabater & Sierra, 05] Review on computational trust and reputation models Artificial Intelligence

Review ,24 (1) :33-60.


References

• [Sabater-Mir & Paolucci, 06] Repage: REPutation and imAGE among limited autonomous partners, JASSS - Journal of Artificial Societies and Social Simulation ,9 (2), 2006.

• [Schillo & Funk, 99] M. Schillo and P. Funk, Learning from and about other agents in terms of social metaphors, Agents Learning About From and With Other Agents, 1999.

• [Sen & Sajja, 02] S. Sen and N. Sajja, Robustness of reputation-based trust: Boolean case, Proceedings of Autonomous Agents and Multi-Agent Systems (AAMAS'02), p. 288-293, 2002, Bologna, Italy, M. Gini and T. Ishida and C. Castelfranchi and W. L. Johnson (eds), ACM Press, New York, NY, United States of America, vol.1.

• [Shapiro, 87] S. P. Shapiro, The social control of impersonal trust, American Journal of Sociology, 1987, vol. 93, p. 623-658.

• [Steiner, 03] D. Steiner, Survey: How do Users Feel About eBay's Feedback System? January, 2003, http://www.auctionbytes.com/cab/abu/y203/m01/abu0087/s02 .

• [Zacharia et al., 99] G. Zacharia and A. Moukas and P. Maes, Collaborative Reputation Mechanisms in Electronic Marketplaces, Proceedings of the Hawaii International Conference on System Sciences (HICSS-32), vol. 08, 1999, p. 8026, IEEE Computer Society, Washington, DC, United States of America.


Perguntas...

Andrew Diniz da Costa

[email protected]

computational trust and reputation models andrew diniz da costa [email protected]

Documents

costa lespucrio reputation

trust models

reputation models andrew

computational trust

good trust model

cost slide

interactions utility

arttestbed overview