Transcript Computational Trust and Reputation Models - LES PUC-Rio

Computational Trust and
Reputation Models
Andrew Diniz da Costa
[email protected]
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
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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
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What we are talking about ...
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What we are talking about ...
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Advantages of trust and reputation mechanisms
• Agents can obtain data from others agents.
• Shared experience.
• Decide on which to trust
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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?
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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]
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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”
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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
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Why using a trust model in a MAS ?
• Trust models allow:
– Identifying and isolating untrustworthy agents
– Evaluating an interaction’s utility
– Deciding whether and with whom to interact
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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
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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.
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eBay model
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eBay model
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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
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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
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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.
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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
Interaction Trust of the Agent B
(price, quality, etc)
Request
Agent A
Provide
Agent B
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Fire Model
• Witness reputation
– reports of witness about an agent’s behaviour
Agent C
Agent D knows Agent B
Request witness
Agent A
Agent D
Agent B
Agent E
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Fire Model
• Certified reputation
– references provided by other agents about its behaviour
Evaluation of D made
by the agent A
Evaluation of A made
by the agent D
Agent D
Evaluation of B made
by the agent A
0,5
Agent A
-0,5
Agent B
Evaluation of A made
by the agent B
Agent C
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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.
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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.
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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
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Domain
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Reputation Transaction Protocol
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Opinion Transaction Protocol
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Simulator
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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
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Preliminary Phase
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Final Phase
1) Electronics & Computer Science, University of Southampton
2) Department of Math & Computer Science, The University of Tulsa
3) Department of Computer Engineering, Bogazici University
4) Agents Research Lab, University of Girona
5) Pontifícia Universidade Católica do Rio de Janeiro
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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.
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References
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Perguntas...
Andrew Diniz da Costa
[email protected]
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