Computational Trust and Reputation Models Dr' Jordi Sabater Mir Dr' Laurent Vercouter

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Computational Trust and Reputation Models Dr.
Jordi Sabater Mir Dr. Laurent
Vercouter
9th European Agent Systems Summer School
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Dr. Laurent Vercouter
G2I Division for Industrial Engineering and
Computer Sciences EMSE Ecole des Mines of
St-Etienne
Dr. Jordi Sabater-Mir
IIIA Artificial Intelligence Research
Institute CSIC Spanish National Research Council
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Presentation index

• Motivation
• Approaches to control de interaction
• Some definitions
• The computational perspective

• Computational trust and reputation models
• eBay
• TrustNet
• LIAR
• ReGret
• Repage
• Prospective view

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• ART
• The testbed
• Example

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Motivation
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A complete absence of trust would prevent one
even getting up in the morning. Luhmann, 1979.
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What we are talking about...
Mr. Yellow
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What we are talking about...
Two years ago...
Trust based on...
Direct experiences
Mr. Yellow
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What we are talking about...
Trust based on...
Third party information
Mr. Yellow
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What we are talking about...
Trust based on...
Third party information
Mr. Yellow
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What we are talking about...
Trust based on...
Reputation
Mr. Yellow
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What we are talking about...
Mr. Yellow
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What we are talking about...
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Advantages of trust and reputation mechanisms

• Each agent is a norm enforcer and is also under
  surveillance by the others. No central authority
  needed.
• Their nature allows to arrive where laws and
  central authorities cannot.
• Punishment is based usually in ostracism.

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Problems of trust and reputation mechanisms

• Bootstrap problem.
• Exclusion must be a punishment for the outsider.
• Not all kind of environments are suitable to
  apply these mechanisms.

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Approaches to control the interaction
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Different approaches to control the interaction
Security approach
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Different approaches to control the interaction

• Security approach

Agent identity validation. Integrity,
authenticity of messages. ...
Im Alice
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Different approaches to control the interaction
Institutional approach
Security approach
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Different approaches to control the interaction

• Institutional approach

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Different approaches to control the interaction
Social approach
Institutional approach
Security approach
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Example P2P systems
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Example P2P systems
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Example P2P systems
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Different approaches to control the interaction
Social approach
Trust and reputation mechanisms are at this level.
Institutional approach
Security approach
They are complementary and cover different
aspects of interaction.
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Definitions
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Trust
A couple of definitions that I like Trust
begins where knowledge ends trust provides a
basis dealing with uncertain,complex,and
threatening images of the future.
(Luhmann,1979) Trust is the outcome of
observations leading to the belief that the
actions of another may be relied upon, without
explicit guarantee, to achieve a goal in a risky
situation. (Elofson, 2001)
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Trust

• There are many ways of considering Trust.
• Trust as Encapsulated Interest
• Russell Hardin, 2002

I trust you because I think it is in your
interest to take my interests in the relevant
matter seriously. And this is because you value
the continuation of our relationship. You
encapsulate my interests in your own interests.
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Trust

• There are many ways of considering Trust.
• Instant trust

Trust is only a matter of the characteristics of
the trusted, characteristics that are not
grounded in the relationship between the truster
and the trusted. Example
Rug merchant in a bazaar
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Trust

• There are many ways of considering Trust.
• Trust as Moral

Trust is expected, and distrust or lack of trust
is seen as a moral fault. One migh argue that
to act as though I do trust someone who is not
evidently (or not yet) trustworthy is to
acknowledge the persons humanity and
possibilities or to encourage the persons
trustworthiness. Russel Hardin, 2002
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Trust

• There are many ways of considering Trust.
• Trust as Noncognitive

Trust based on affects, emotions... To say that
we trust on other in a non cognitive way is to
say that we are disposed to be trustful of them
independently of our beliefs or expetations about
their trustworthiness Becker 1996

• Trust as Ungrounded Faith

Notice here there is a power relation between the
truster and the trusted.

• Example
• infant towards her parents
• follower towards his leader

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Trust
There are many ways of considering Trust. Just
leave this to philosophers, psicologists and
sociologists... ...but lets have an eye on it.
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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 expectation of future opportunities arising
  from cooperation Axelrod, Parkhe
• The opinion others have of us

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Computational perspective
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Dimensions of trust McKnight Chervany, 02
Disposition to trust
Trusting intention
Trust related behaviour
Trusting beliefs
Institution- based trust
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The Functional Ontology of Reputation Casare
Sichman, 05

• The Functional Ontology of Reputation (FORe) aims
  at defining standard concepts related to
  reputation
• FORe includes
• Reputation processes
• Reputation types and natures
• Agent roles
• Common knowledge (information sources, entities,
  time)
• Facilitate the interoperability of heterogeneous
  reputation models

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Processes needed for trust computation

• Initialisation
• first default value
• Evaluation
• judgement of a behaviour
• Punishment/Sanction
• calculation of reputation values
• Reasoning
• inference of trust intentions
• Decision
• decision to trust
• Propagation
• communication about reputation/trust information

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Agent roles
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Reputation types Casare Sichman, 05

• Primary reputation
• Direct reputation
• Observed reputation
• Secondary reputation
• Collective reputation
• Propagated reputation
• Stereotyped reputation

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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
• Quickly converging
• quickly compute accurate values
• Multi-dimensional
• Consider different agent characteristics
• Efficient
• Compute in reasonable time and cost

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Why using a trust model in aMAS ?
Bob

• Trust models allow
• Identifying and isolating untrustworthy agents

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Why using a trust model in aMAS ?

• Trust models allow
• Identifying and isolating untrustworthy agents
• Evaluating an interactions utility

I can sell you the information you require
Bob
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Why using a trust model in aMAS ?

• Trust models allow
• Identifying and isolating untrustworthy agents
• Evaluating an interactions utility
• Deciding whether and with whom to interact

I can sell you the information you require
Charles
I can sell you the information you require
Bob
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Presentation index

• Motivation
• Approaches to control de interaction
• Some definitions
• The computational perspective

• Computational trust and reputation models
• eBay
• TrustNet
• LIAR
• ReGret
• Repage
• Prospective view

break

• ART
• The testbed
• Example

break
44
Computational trust and reputation models

• eBay
• TrustNet
• LIAR
• ReGret
• Repage

45
eBay model

• Model oriented to support trust between buyer and
  seller.
• Completely centralized.
• Buyers and sellers may leave comments about each
  other after transactions.
• Comment a line of text numeric evaluation
  (-1,0,1)
• Each eBay member has a 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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eBay model

• Specifically oriented to scenarios with the
  following characteristics
• A lot of users (we are talking about milions)
• Few chances of repeating interaction with the
  same partner
• Easy to change identity
• Human oriented
• Considers reputation as a global property and
  uses a single value that is not dependent on the
  context.
• A great number of opinions that dilute false
  or biased information is the only way to increase
  the reliability of the reputation value.

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Computational trust and reputation models

• eBay
• TrustNet
• LIAR
• ReGret
• Repage

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Trust Net Schillo Funk, 99

• Model designed to evaluate the agents honesty
• Completely decentralized
• Applied in a game theory context the Iterated
  Prisonners Dilemma (IPD)
• Each agent announce its strategy and choose an
  opponent according to its announced strategy
• If an agent does not follow the strategy it
  announced, its opponent decreases its reputation
• The trust value of agent A towards agent B is
• T(A,B) number of honest rounds / number of
  total rounds

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Trust Net Schillo Funk, 99

• Agents can communicate their trust values to
  fasten the convergence of trust models
• An agent can build a Trust Net of trust values
  transmitted by witnesses
• The final trust value of an agent towards another
  aggregate direct experiences and testimonies with
  a probabilistic function on the lying behaviour
  of witnesses

1.0
0.25
0.8
0.7
0.2
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Computational trust and reputation models

• eBay
• TrustNet
• LIAR
• ReGret
• Repage

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The LIAR model Muller Vercouter, 07

• Model designed for the control of communications
  in a P2P network
• Completely decentralized
• Applied to a peer-to-peer protocol for query
  routings
• The global functionning of a p2p network relies
  on an expected behaviour of several nodes (or
  agents)
• Agents behaviour must be regulated by a social
  control Castelfranchi, 00

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LIAR Social control of agent communications
Social Control
Definition of acceptability
Trust intentions
Reputations
Social commitments
Sanction
Representation
Interactions
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The LIAR agent architecture
Reputations
Interactions
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Detection of violations
Evaluator
Propagator
observations(ob)
social commitment update
social policy generation
social policy evaluation
proof receivediteration
Justification Protocol
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Reputation types in LIAR

• Rptargetbeneficary(facet,dimension,time) ?
  -1,1 ? unknown

• 7 different roles
• target
• participant
• observator
• evaluator
• punisher
• beneficiary
• propagator

• 5 reputation types based on
• direct interaction
• indirect interaction
• recommendation about observation
• recommendation about evaluation
• recommendation about reputation

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Reputation computation

• Direct Interaction based Reputation
• Separate the social policies according to their
  state
• associate a penalty to each set
• reputation weighted average of the penalties
• Reputation Recommendation based Reputation
• based on trusted recommendation
• reputation weighted average of received values
• weighted by the reputation of the punisher

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LIAR decision process
Trust_int trust
ObsRcbRp
EvRcbRp
RpRcbRp
ObsRcbRp
DIbRp
GDtT
()
()
()
()
()
Trust_int distrust
() -gt (unknown) or not relevant or not
discriminant
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Computational trust and reputation models

• eBay
• TrustNet
• LIAR
• ReGret
• Repage

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ReGreT
What is the ReGreT system? It is a modular trust
and reputation system oriented to complex
e-commerce environments where social relations
among individuals play an important role.
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The ReGreT system
ODB
IDB
SDB
Credibility
Witness reputation
Neigh- bourhood reputation
Reputation model
Direct Trust
System reputation
Trust
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The ReGreT system
ODB
IDB
SDB
Credibility
Witness reputation
Neigh- bourhood reputation
Reputation model
Direct Trust
System reputation
Trust
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Outcomes and Impressions

• Outcome
• The initial contract
• to take a particular course of actions
• to establish the terms and conditions of a
  transaction.
• AND
• The actual result of the contract.

Example
Prize c 2000 Quality c A Quantity c 300
Contract
Outcome
Prize f 2000 Quality f C Quantity f 295
Fulfillment
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Outcomes and Impressions
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Outcomes and Impressions

• Impression
• The subjective evaluation of an outcome from a
  specific point of view.

Outcome
Prize c 2000 Quality c A Quantity c 300
Prize f 2000 Quality f C Quantity f 295
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The ReGreT system
ODB
IDB
SDB
Credibility
Witness reputation
Neigh- bourhood reputation
Reputation model
Direct Trust
System reputation
Trust
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The ReGreT system
ODB
IDB
SDB
Credibility
Witness reputation
Neigh- bourhood reputation
Reputation model
Direct Trust
System reputation
Trust
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Witness reputation

• Reputation that an agent builds on another agent
  based on the beliefs gathered from society
  members (witnesses).

• Problems of witness information
• Can be false.
• Can be incomplete.
• It may suffer from the correlated evidence
  problem.

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Big exchange of sincere infor-mation and some
kind of predispo-sition to help if it is possible.
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Agents tend to use all the available mechanisms
to take some advantage from their competitors.
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Witness reputation
Step 1 Identifying the witnesses
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Witness reputation
Step 1 Identifying the witnesses
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Witness reputation
u7
Heuristic to identify groups and the best agents
to represent them
u6
u3

• Identify the components of
• the graph.

u8
u2

• For each component, find the
• set of cut-points.

b2

3. For each component that does not have any
cut-point, select a central point (node with
larger degree).
u5
u4
cooperation
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Witness reputation
Step 1 Identifying the witnesses

• Grouping and selecting
• the most representative
• witnesses

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Witness reputation
Step 1 Identifying the witnesses
u3
u2
u2

• Grouping and selecting
• the most representative
• witnesses

u5
u5
trade
trade
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Witness reputation
u2
u3
Step 1 Identifying the witnesses
u5
Step 2 Who can I trust?
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The ReGreT system
ODB
IDB
SDB
Credibility
Witness reputation
Neigh- bourhood reputation
Reputation model
Direct Trust
System reputation
Trust
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Credibility model

• Two methods are used to evaluate the
  credibility of
• witnesses

Credibility (witnessCr)
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The ReGreT system
ODB
IDB
SDB
Credibility
Witness reputation
Neigh- bourhood reputation
Reputation model
Direct Trust
System reputation
Trust
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Neighbourhood reputation

• The trust on the agents that are in the
  neighbourhood of the target agent and their
  relation with it are the elements used to
  calculate what we call the Neighbourhood
  reputation.

ReGreT uses fuzzy rules to model this reputation.
IF is X AND coop(b, ) low THEN
is X
IF is X AND coop(b, ) is Y
THEN is T(X,Y)
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The ReGreT system
ODB
IDB
SDB
Credibility
Witness reputation
Neigh- bourhood reputation
Reputation model
Direct Trust
System reputation
Trust
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System reputation

• The idea behind the System reputation is to use
  the common knowledge about social groups and the
  role that the agent is playing in the society as
  a mechanism to assign reputation values to other
  agents.
• The knowledge necessary to calculate a system
  reputation is usually inherited from the group or
  groups to which the agent belongs to.

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Trust

• If the agent has a reliable direct trust value,
  it will use that as a measure of trust. If that
  value is not so reliable then it will use
  reputation.

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Computational trust and reputation models

• eBay
• TrustNet
• LIAR
• ReGret
• Repage

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RepAge
What is the RepAge model? It is a reputation
model evolved from a cognitive theory by Conte
and Paolucci. The model is designed with an
special attention to the internal representation
of the elements used to build images and
reputations as well as the inter-relations of
these elements.
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Image vs Reputation

• Both are social evaluations.
• They concern other agents' (targets) attitudes
  toward socially desirable behaviour but...
• whereas image consists of a set of evaluative
  beliefs about the characteristics of a target,
  reputation concerns the voice that is circulating
  on the same target.
• Reputation is a belief about the existence of a
  communicated evaluation. It is a meta-belief.
• This has important consequences
• To accept a meta-belief does not imply to accept
  the contained belief.

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The Repage system
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RepAge memory
P
P
P
P
P
P
P
P
P
P
P
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Image(w,informant)
CandImage(a,seller)
Image(a,seller)
ValComm(w, image(a,seller))
Comm(w, image(a,seller))
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The analyzer

• The interplay between image and reputation might
  be a cause of uncertainty and inconsistency.
• Inconsistencies do not necessarily lead to a
  state of cognitive dissonance, nor do they always
  urge the system to find a solution.
• For example, an inconsistency between own image
  of a given target and its reputation creates no
  problem to the system.
• However, a contradiction between own evaluations
  is sometimes possible
• my direct experience may be confirmed in further
  interaction, but at the same time it may be
  challenged by the image I believe others, whom I
  trust a lot, have formed about the same target
• What will I do in such a condition? Will I go
  ahead and sign a contract, may be a low-cost one,
  just to acquire a new piece of direct evidence,
  or will I check the reliability of my informants?
  
• The picture is rather complex, and the number of
  possibilities is bound to increase at any step,
  making the application of rule-based reasoning
  computationally heavy.

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The Analyzer

• The main task of the analyzer is to propose
  actions that
• can improve the accuracy of the predicates in the
  Repage memory and
• can solve cognitive dissonances trying to produce
  a situation of certainty.
• For each possible action
• two copies of the current memory will be
  istantiated,
• to which the hypotetical new information
  (good/bad) will be added.
• effects will be evaluated about change in the
  image of the target in the specified role
• the wrapper agent architecture will try to
  combine informational value with other costs and
  benefits
• a situated ordering,

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Current work with the RepAge architecture

• Agents that are able to justify the values of
  Images and reputations (the LRep language).
• Formalization that allows an agent to reason
  about the elements that conform an image and/or a
  reputation.
• Dynamic ontology mapping.

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A prospective view
Is that enough?
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The lieutenant and the merchant Trifonov
(From The Brothers Karamazov Dostoyevsky)
Lieutenant
Trifonov

• It was a secret exchange. No contract.

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The lieutenant and the merchant Trifonov
(From The Brothers Karamazov Dostoyevsky)
Replacement notification
Ive never received any money from you... and
couldnt possibly have received any.
Lieutenant
Trifonov
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The lieutenant and the merchant Trifonov

• If instead of the lieutenant it was a virtual
  agent using a current trust and reputation model,
  it will be also in a trouble.
• Previous interactions were perfect so Trifonov
  trustworthiness would be very high.
• All current models would suggest to go ahead with
  the interaction.
• To be successful in this situation implies to
  ask about the reasons for thinking the relevant
  party to be trustworthy.

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A prospective view
Current models
Planner
Trust Reputation system
Decision mechanism
Inputs
Comm
Black box
Agent
Reactive
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A prospective view
Current models
Planner
Trust Reputation system
Value
Decision mechanism
Inputs
Comm
Black box
Agent
Reactive
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A prospective view
The next generation?
Planner
Trust Reputation system
Decision mechanism
Inputs
Comm
Agent
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A prospective view
The next generation?
Planner
Decision mechanism
Inputs
Comm
Agent
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Conclusions

• Computational trust and reputation models are an
  essential part of autonomous social agents. It is
  not possible to talk about social agents without
  considering trust and reputation.
• Current trust and reputation models are still
  far from covering the necessities of an
  autonomous social agent.
• We have to change the way the trust and
  reputation system is considered in the agent
  architecture.

111
Conclusions

• Tight integration with the rest of the modules
  of the agent and proactivity are necessary to
  transform the trust and reputation system in a
  useful tool that be able to solve the kind of
  situations a real social agent will face in
  virtual societies.
• To achieve that, more collaboration with other
  artificial intelligence areas is needed.

112
Comparison among models

• Slide 11, Guillaumes thesis
• Some slides to show that there are a lot of
  models and they are quite different.

Jordi
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Presentation index

• Motivation
• Approaches to control de interaction
• Some definitions
• The computational perspective

• Computational trust and reputation models
• eBay
• TrustNet
• LIAR
• ReGret
• Repage
• Prospective view

break

• ART
• The testbed
• Example

break
114
The Agent Reputation and Trust Testbed
115
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

116
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

117
The ART Testbed

• A tool for
• Experimentation Researchers can perform
  easily-repeatable experiments in a common
  environment against accepted benchmarks
• Competitions Trust technologies compete against
  each other the most promising technologies are
  identified

118
Testbed Game Rules
If an appraiser is not very knowledgeable about a
painting, it can purchase "opinions" from other
appraisers.
For a fixed price, clients ask appraisers to
provide appraisals of paintings from various eras.
Agents function as art appraisers with varying
expertise in different artistic eras.
Opinions and Reputations
Client Share
Appraisers whose appraisals are more accurate
receive larger shares of the client base in the
future.
Appraisers can also buy and sell reputation
information about other appraisers.
Appraisers compete to achieve the highest
earnings by the end of the game.
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Step 1 Client and Expertise Assignments

• Appraisers receive clients who pay a fixed price
  to request appraisals
• Client paintings are randomly distributed across
  eras
• As game progresses, more accurate appraisers
  receive more clients (thus more profit)

120
Step 2 Reputation Transactions

• Appraisers know their own level of expertise for
  each era
• Appraisers are not informed (by the simulation)
  of the expertise levels of other appraisers
• Appraisers may purchase reputations, for a fixed
  fee, from other appraisers
• Reputations are values between zero and one
• Might not correspond to appraisers internal
  trust model
• Serves as standardized format for inter-agent
  communication

121
Step 2 Reputation Transactions
Requester sends request message to a potential
reputation provider, identifying appraiser whose
reputation is requested
Provider
Requester

• Potential reputation provider sends accept
  message

Requester sends fixed payment to the provider
Provider sends reputation information, which may
not be truthful
122
Step 3 Opinion Transactions

• For a single painting, an appraiser may request
  opinions (each at a fixed price) from as many
  other appraisers as desired
• The simulation generates opinions about
  paintings for opinion-providing appraisers
• Accuracy of opinion is proportional to opinion
  providers expertise for the era and cost it is
  willing to pay to generate opinion
• Appraisers are not required to truthfully reveal
  opinions to requesting appraisers

123
Step 3 Opinion Transactions
Potential provider sends a certainty assessment
about the opinion it can provide - Real number (0
1) - Not required to truthfully report
certainty assessment
Requester sends request message to a potential
opinion provider, identifying painting
Provider
Requester
Requester sends fixed payment to the provider
Provider sends opinion, which may not be truthful
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Step 4 Appraisal Calculation

• Upon paying providers and before receiving
  opinions, requesting appraiser submits to
  simulation a weight (self-assessed reputation)
  for each other appraiser
• Simulation collects opinions sent to appraiser
  (appraisers may not alter weights or received
  opinions)
• Simulation calculates final appraisal as
  weighted average of received opinions
• True value of painting and calculated final
  appraisal are revealed to appraiser
• Appraiser may use revealed information to revise
  trust models of other appraisers

125
Analysis Metrics

• Agent-Based Metrics
• Money in bank
• Average appraisal accuracy
• Consistency of appraisal accuracy
• Number of each type of message passed
• System-Based Metrics
• System aggregate bank totals
• Distribution of money among appraisers
• Number of messages passed, by type
• Number of transactions conducted
• Evenness of transaction distribution across
  appraisers

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Conclusions

• The ART Testbed provides a tool for both
  experimentation and competition
• Promotes solutions to prominent trust research
  problems
• Features desirable characteristics that
  facilitate experimentation

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An example of using ART

• Building an agent
• creating a new agent class
• strategic methods
• Running a game
• designing a game
• running the game
• Viewing the game
• Running a game monitor interface

128
Building an agent for ART

• An agent is described by 2 files
• a Java class (MyAgent.java)
• must be in the testbed.participant package
• must extend the testbed.agent.Agent class
• an XML file (MyAgent.xml)
• only specifying the agent Java class in the
  following way
• ltagentConfiggt
• ltclassFilegt
• c\ARTAgent\testbed\participants\MyAgent.class
• lt/classFilegt
• lt/agentConfiggt

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Strategic methods of the Agent class (1)

• For the beginning of the game
• initializeAgent()
• To prepare the agent for a game
• For reputation transactions
• prepareReputationRequests()
• To ask reputation information (gossips) to other
  agents
• prepareReputationAcceptsAndDeclines()
• To accept or refuse requests
• prepareReputationReplies()
• To reply to confirmed requests

130
Strategic methods of the Agent class (2)

• For opinion transactions
• prepareOpinionRequests()
• To ask opinion to other agents
• prepareOpinionCertainties()
• To announce its own expertise to a requester
• prepareOpinionRequestConfirmations()
• To confirm/cancel requests to providers
• prepareOpinionCreationOrders()
• To produce evaluations of paintings
• prepareOpinionProviderWeights()
• To weight the opinion of other agents
• prepareOpinionReplies()
• To reply to confirmed requests

131
The strategy of this example of agent

• We will implement an agent with a very simple
  reputation model
• It associates a reputation value to each other
  agent (initialized at 1.0)
• It only sends opinion requests to agents with
  reputation gt 0.5
• No reputation requests are sent
• If an appraisal of another agent is different
  from the real value by less than 50, reputation
  is increased by 0.03
• Otherwise it is decreased by 0.03
• If our agent receives a reputation request from
  another agent with a reputation less than 0.5, it
  provides a bad appraisal (cheaper)
• Otherwise its appraisal is honest

132
Initialization
The agent class is extended
Reputation values are assigned to every agent
133
Opinion requests
Opinion requests are only sent to agents with a
reputation over 0.5
134
Opinion Creation Order
If a requester has a bad reputation value, a
cheap and bad opinion is created For it.
Otherwise It is an expensive and accurate one
135
Updating reputations
According to the difference between opinions and
real painting values, Reputations are increased
or decreased
136
Running a game with MyAgent

• Parameters of the game
• 3 agents MyAgent, HonestAgent, CheaterAgent
• 50 time steps
• 4 painting eras
• average client share 5 / agent

137
How did my agent behaved ?
Wow ! I should think about participating to the
next competition !
138
References

• Casare Sichman, 05 S. J. Casare and J. S.
  Sichman, Towards a functional ontology of
  reputation, Proceedings of AAMAS05, 2005
• Castelfranchi, 00 C. Castelfranchi, Engineering
  Social Order, Proceedings of ESAW00, 2000
• 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
  AAMAS05, 2005
• 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
• 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
• 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
• 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