AAMAS08 Tutorial 2: Computational Trust and Reputation Models Dr' Guillaume Muller Dr' Laurent Verco

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AAMAS08 Tutorial 2 Computational Trust and
Reputation Models Dr. Guillaume Muller
Dr. Laurent Vercouter
7th International Conference on Autonomous Agents
Multi-Agent Systems
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Dr. Laurent Vercouter
G2I Division for Industrial Engineering and
Computer Sciences EMSE Ecole des Mines of
St-Etienne
Dr. Guillaume Muller
MAIA Intelligent Autonomous Machine INRIA
LORIA Laboratory of IT Research and its
Applications
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Presentation outline

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

• Computational trust and reputation models
• OpenPGP
• Marsh
• eBay/OnSale
• Sporas Histos
• TrustNet
• Fuzzy Models
• LIAR
• ReGret

• ART
• The testbed
• An example

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Motivation
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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
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 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 There are
no obvious units in which trust can be measured,
Dasgupta, 2000
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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. And
therefore, many definitions of Trust. Conceptual
morass Barber, 83 Confusing pot-pourri
Shapiro, 87 Just leave this to philosophers,
psycologists 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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Computational trust

• Castelfranchi Falcone make a clear distinction
  between
• Trust as an evaluative belief
• A truster agent believes that the trustee is
  trustful
• e.g. I believe that my doctor is a good surgeon
• Trust as a mental attitude
• A truster agent relies on a trustee for a given
  behaviour
• e.g. I accept that my doctor makes a surgical
  operation on me

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Trust as a belief

• A truster i trusts a trustee j to do an action ?
  in order to achieve a goal ? Castelfranchi
  Falcone
• Agent i has the goal ?
• Internal attribution of trust
• i believes that j intends to do ?
• External attribution of trust
• i believes that j is capable to do ?
• i believes that j has the power to achieve ? by
  doing ?
• The goal component can be generalized to consider
  norm-obedience. Demolombe Lorini

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Occurrent trust

• Occurent trust happens when a truster believes
  that the trustee is going to act here and now
  Herzig et al, 08.

OccTrust(i, j, ?, ?) Goal(i, ?) ? Believes(i,
OccCap(j, ?)) ? Believes(i, OccPower(j, ? , ?))
? Believes(i, OccIntends(j, ?))
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Dispositional trust

• Dispositional trust happens when a truster
  believes that the trustee is going to act
  whenever some conditions are satisfied Herzig et
  al, 08.

DispTrust(i, j, ?, ?) PotGoal(i, ?)
? Believes(i, CondCap(j, ?)) ? Believes(i,
CondPower(j, ? , ?)) ? Believes(i,
CondIntends(j, ?))
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Trust and delegation

• Trust (as a belief) can lead to delegation, when
  the truster i relies on the trustee j.
• Weak delegation
• j is not aware of the fact that i is exploiting
  his action
• Strong delegation
• i elicits or induces js expected behaviour to
  exploit it
• There can be trust without delegation
  (insufficient trust, prohibitions)
• There can be delegations without trust (no
  information, obligations)

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Computational reputation

• Reputation adds a collective dimension to the
  truster.
• Reputation is an objective social property that
  emerges from a propagating cognitive
  representation Conte Paolucci. This
  definition includes both
• a process of transmitting a targets image
• a cognitive representation resulting from this
  propagation

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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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Reputation processes

• Reputation transmission / reception
• An agent sends/receive a reputation information
  to/from another one
• Reputation evaluation
• Production of a reputation measurement that can
  contain several valued attributes (content
  evaluation) or an unexplained estimation (esteem
  level). Values can be quantitative or
  qualitative.
• Reputation maintenance
• The reputation alterations over time that can
  take into account the incremental impact of
  agents behavior (aggregation) or the history of
  behaviors (historical process)

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Agent roles Conte Paolucci, 02
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Reputation types Mui, 02

• 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 outline

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

• Computational trust and reputation models
• OpenPGP
• Marsh
• eBay/OnSale
• Sporas Histos
• TrustNet
• Fuzzy Models
• LIAR
• ReGret

• ART
• The testbed
• Example

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Computational trust and reputation models

• OpenPGP
• Marsh
• eBay/OnSale
• Sporas Histos
• TrustNet
• Fuzzy Models
• LIAR
• ReGret

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OpenPGP model Adbul-Rahman, 97

• Context replace the centralized Trusted
  Authorities in Public Key management

Authority
Certification
trusts
certifies
Authority
Bobs ID
signs
(message)key
Bob
Alice
Bobs pubkey
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OpenPGP model Adbul-Rahman, 97

• Context replace the centralized Trusted
  Authorities in Public Key management

Authority
Web of Trust
trusts
certifies
certifies
message
message
Bob
Alice
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OpenPGP model Adbul-Rahman, 97

• 2 kinds of trusts
• Tc Trust in the certificate undefined,marginal,
  complete
• Ti Trust as an introducer untrustworthy,margina
  l,full,dontknow
• OpenPGP computes reputations based on
  transitivity along all existing pathes
• gtX complete OR gtY marginal ? c
• ? gt0 marginal (or gt) ? m
• Humans set all Ti some Tc and take decisions
• Parameters are
• X min. number of complete
• Y min. number of marginal
• length of the trust pathes.

Tc
Tc
Ti
Web of Trust
certifies
Ti
message
Ti
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Computational trust and reputation models

• OpenPGP
• Marsh
• eBay/OnSale
• Sporas Histos
• TrustNet
• Fuzzy Models
• LIAR
• ReGret

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Marshs model Marsh, 94

• Context collaborative work
• Addresses only direct interactions, does not
  consider gossips
• Two kinds of trust
• General Trust Tx(y), Trust of x in y in general
• Situational Trust Tx(y, ?x), contextualized
  trust
• Trust is modelled as a probability,in fact a
  value in 0,1)
• Computation
• Tx(y) average of the Tx(y,?x), in all possible
  contexts
• Tx(y,?x) Tx(y, ?x) Ux(?x) Ix(?x) Tx(y)
• Decision to trust
• Tx(y,?x) ? CooperThresholdx(?x) ?
  WillCooper(x,y,?x)
• CooperThreshold depends on the risks, perceived
  competence, importance

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Computational trust and reputation models

• OpenPGP
• Marsh
• eBay/OnSale
• Sporas Histos
• TrustNet
• Fuzzy Models
• LIAR
• ReGret

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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

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eBay model

• Buyers and sellers evaluate each other after
  transactions
• The evaluation is not mandatory and will never
  be removed
• evaluation a comment a rating
• comment a line of text
• rating numeric evaluation in -1,0,1
• 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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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
• 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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eBay model

• Advantages
• Used everyday
• In a real life application
• Very simple
• Limits Dellarocas, 0001 Steiner, 03
• Fear of reciprocity
• What is the semantic of a high reputation?
• Problem of electronic commerce change of
  identity
• The textual comment makes the efficiency
• Few public papers, evolves frequently

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OnSale model

• OnSale specialized on computer-related stuff
• Newcomers
• OnSale no reputation
• eBay zero feedback points (lowest reputation)
• Bidders
• OnSale not rated at all, register with credit
  card
• eBay are rated, used internally, bought PayPal

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Computational trust and reputation models

• OpenPGP
• Marsh
• eBay/OnSale
• Sporas Histos
• TrustNet
• Fuzzy Models
• LIAR
• ReGret

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SPORAS HISTOS Zacharias et al., 99

• Context e-commerce, similar to eBay
• Reputations are faceted an individual may enjoy
  a very high reputation in one domain, while she
  has a low reputation in another.
• Two models are proposed
• Sporas works even with few ratings
• Histos assumes abundance of ratings
• Deterrent for agents to change their IDs
• Reputations can decrease, but it will never fall
  below a newcomer's value
• A low-reputed agent can improve its status at
  the same rate as a beginner
• Ratings given by users with a high reputation
  are weighted more
• Measure against end-of-game strategies
• Reputation values are not allowed to increase at
  infinitum

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SPORAS
1. Reputations are in 0, 3000. Newcommers 0.
Ratings are in 0.1, 1 2. Reputations never get
below 0, even in the case of very bad
behaviours 3. After each rating the reputation
is updated 4. Two users may rate each other only
once more than one interaction gt most recent
rating considered . 5. Higher reputations are
updated more moderatetly
Memory of The system
Reputation of the rater
Normalized prev. reputation
Dumping factor
Current rating
previous reputation
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Histos

• Aim compute a global personalized reputation
  (PRp) value for each member
• personalized reputation is computed by
  transitivity

• Find all directed paths from A to ALwith length
  ? N
• Keep only the ? most recent ones
• Start a backward recursion
• If path length 1,PRp rating
• If path length gt 1,PRp f(RatersPRp,rating)

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Computational trust and reputation models

• OpenPGP
• Marsh
• eBay/OnSale
• Sporas Histos
• TrustNet
• Fuzzy Models
• LIAR
• ReGret

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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 announces 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
  aggregates direct experiences and testimonies
  with a probabilistic function on the lying
  behaviour of witnesses, which reduces the
  correlated evidence problem.

.65
1.0
0.25
0.8
0.7
0.2

• Binary evaluation
• Annouced behaviour

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Computational trust and reputation models

• OpenPGP
• Marsh
• eBay/OnSale
• Sporas Histos
• TrustNet
• Fuzzy Models
• LIAR
• ReGret

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Fuzzy models Rehák, 05

• Trust modelled as a type-2 fuzzy set
• Iterative building of the fuzzy set
• Estimate the subjective utility of the
  cooperation
• Compute the rating of 1 agent based on this
  utility
• Flat
• Proportional distribution (trust of
  Autility)/(trust of avg agent)
• Fuzzy set membership function on sets of
  ratings

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Fuzzy models Rehák, 05
Trust Decision
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Computational trust and reputation models

• OpenPGP
• Marsh
• eBay/OnSale
• Sporas Histos
• TrustNet
• Fuzzy Models
• LIAR
• ReGret

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The LIAR model Muller Vercouter, 08

• 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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The LIAR model Muller Vercouter, 07
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The LIAR model Muller Vercouter, 07
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The LIAR model Muller Vercouter, 07
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The LIAR model Muller Vercouter, 07
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The LIAR model Muller Vercouter, 07
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The LIAR model Muller Vercouter, 07
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The LIAR model Muller Vercouter, 07
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LIAR Social control of agent communications
Social Control
Definition of Acceptability (Social norms)
Trust intentions
(Reputations)
Sanction
Representation (Social commitments)
Interactions
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LIAR Social commitments and norms
Social Commitment example
Debtor (sender)
Content
Utterance time
Observer
Creditor (receiver)
State
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LIAR Social commitments and norms
Social Norm example
Prohibition
Punishers
Targets
Evaluators
Condition
Content
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The LIAR agent architecture
Reputations
Interactions
85
LIAR partial observation
Agent D
Agent C
inform(p)
Agent A
Agent B
86
LIAR partial observation
D CSAB
C CSAB
sc(A,B,8pm,8pm-9pm,active,p)
A CSAB
B CSAB
87
LIAR partial observation
D CSAB
C CSAB
cancel(p)
A CSAB
B CSAB
88
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

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

beneficiary

• 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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LIAR conclusion

• LIAR is adapted to P2P infrastructures
• Partial observations/incomplete information
• Scalable
• Applied in a GNUtellalike network ? malicious
  nodes are excluded
• LIAR is fine-grained
• Different types of reputation maintained
  separately
• multi-facet and multi-dimension
• LIAR covers the whole loop of social control
• evaluation of a single behaviour ? decision to
  act in trust.

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Computational trust and reputation models

• OpenPGP
• Marsh
• eBay/OnSale
• Sporas Histos
• TrustNet
• Fuzzy Models
• LIAR
• ReGret

94
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
97
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
98
Outcomes and Impressions
99
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
100
The ReGreT system
ODB
IDB
SDB
Credibility
Witness reputation
Neigh- bourhood reputation
Reputation model
Direct Trust
System reputation
Trust
101
The ReGreT system
ODB
IDB
SDB
Credibility
Witness reputation
Neigh- bourhood reputation
Reputation model
Direct Trust
System reputation
Trust
102
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.
• Correlated evidence problem Pearl, 88.
• Functionning
• Find Witnesses
• Direct relation with target
• Use of sociograms (cut-points and central points)
• Weight each recommendation with the credibility
• Advantages
• Minimizes the correlated evidence problem.
• Reduces the number of queries

103
Credibility model

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

Credibility (witnessCr)
104
The ReGreT system
ODB
IDB
SDB
Credibility
Witness reputation
Neigh- bourhood reputation
Reputation model
Direct Trust
System reputation
Trust
105
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
107
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.

108
Trust decision

• 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.

109
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.

110
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.

111
Presentation outline

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

• Computational trust and reputation models
• OpenPGP
• Marsh
• eBay/OnSale
• SPORAS HISTOS
• TrustNet
• Fuzzy Models
• LIAR
• ReGret

• ART
• The testbed
• Example

112
The Agent Reputation and Trust Testbed
113
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

114
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

115
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

116
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)

118
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

119
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
120
Step 3 Certainty 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

121
Step 3 Certainty Opinion Transactions
Potential provider sends a certainty assessment
about the opinion it can provide for this era -
Real number (0 1) - Not required to truthfully
report certainty assessment
Requester sends certainty request message to
potential providers, identifying an era
Provider
Requester
Requester sends opinion request messages to
potential providers, identifying a painting
Provider sends opinion, which may not be truthful
and receive a fixed payment
122
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

123
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

124
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

125
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

126
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

127
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

128
Strategic methods of the Agent class (2)

• For certainty transactions
• prepareCertaintyRequests()
• To ask certainty about eras to other agents
• prepareCertaintyReplies()
• To announce its own certainty about eras to
  requesters
• For opinion transactions
• prepareOpinionRequests()
• To ask opinion to other agents
• prepareOpinionCreationOrders()
• To produce evaluations of paintings
• prepareOpinionReplies()
• To reply to confirmed requests
• prepareOpinionProviderWeights()
• To weight the opinion of other agents

129
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

130
Initialization
The agent class is extended
Reputation values are assigned to every agent
131
Opinion requests
Opinion requests are only sent to agents with a
reputation over 0.5
132
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
133
Updating reputations
According to the difference between opinions and
real painting values, Reputations are increased
or decreased
134
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

135
How did my agent behaved ?
136
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