T9. Trust and reputation in multi-agent systems

Trust & Reputation
in Multi-Agent Systems

Dr. Jordi Sabater Mir Dr. Javier Carbó
jsabater@iiia.csic.es jcarbo@inf.uc3m.es

EASSS 2012, Valencia, Spain 1

Dr. Jordi Sabater-Mir

IIIA – Artificial Intelligence Research Institute
CSIC – Spanish National Research Council

Outline
• Introduction
• Approaches to control the interaction
• Computational reputation models
– eBay
– ReGreT
• A cognitive perspective to computational reputation
models
– A cognitive view on Reputation
– Repage, a computational cognitive reputation model
– [Properly] Integrating a [cognitive] reputation model into a
[cognitive] agent architecture
– Arguing about reputation concepts

Trust

“A complete absence of trust
would prevent [one] even getting
up in the morning.”
Niklas Luhman - 1979

Trust
A couple of definitions that I like:

“Trust begins where knowledge [certainty] 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)

Trust Epistemic
“The subjective probability by which an individual, A,
expects that another individual, B, performs a given
action on which its welfare depends” [Gambetta]

“An expectation about an uncertain behaviour” [Marsh]

“The decision and the act of relying on, counting on,
depending on [the trustee]” [Castelfranchi & Falcone]

Motivational
6

Reputation

"After death, a tiger leaves behind
his skin, a man his reputation"

Vietnamese proverb

Reputation
“What a social entity says about a target regarding his/her behavior”

It is always associated to a specific
behaviour/property

• The social evaluation linked to the reputation is
not necessarily a belief of the issuer.
• Reputation cannot exist without communication.

Set of individuals plus a set of social relations among
these individuals or properties that identify them as a
group in front of its own members and the society at
large.

What is reputation good for?

• Reputation is one of the elements that allows
us to build trust.
• Reputation has also a social dimension. It is
not only useful for the individual but also for
the society as a mechanism for social order.

But... why we need computational
models of those concepts?

What we are talking about...

Mr. Yellow

Two years ago... Trust based on... Direct experiences

Mr. Yellow

Trust based on... Third party information

Mr. Pink

Mr. Yellow

Trust based on... Third party information

Mr. Green Mr. Pink

Mr. Yellow

Trust based on... Reputation

Mr. Yellow


?

Characteristics of computational 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. Therefore,
exclusion must be a punishment for the outsider.

Characteristics of computational trust and
reputation mechanisms

• Bootstrap problem.

• Not all kind of environments are suitable to apply
these mechanisms. It is necessary a social
environment.

Approaches to control the
interaction

Different approaches to control the
interaction

Security approach

interaction

• Security approach
Agent identity validation.
Integrity, authenticity of messages.
...

interaction

Institutional approach

Security approach

interaction

• Institutional approach

interaction

Trust and reputation
Social approach mechanisms are at this
level.


Security approach

They are complementary
and cover different aspects
of interaction.

Computational reputation
models

Classification dimensions

• Paradigm type • Model’s granularity
• Mathematical approach • Single context
• Cognitive approach • Multi context

• Information sources • Agent behaviour assumptions
• Cheating is not considered
• Direct experiences
• Agents can hide or bias the
• Witness information
information but they never lie
• Sociological information
• Type of exchanged information
• Prejudice
• Visibility types
• Subjective
• Global

Subjective vs Global
• Global
• The reputation is maintained as a centralized resource.
• All the agents in that society have access to the same reputation values.

Advantages:
• Reputation information is available even if you are a newcomer and do not
depend on how well connected or good informants you have.
• Agents can be simpler because they don’t need to calculate reputation
values, just use them.

Disadvantages:
• Particular mental states of the agent or its singular situation are not taken
into account when reputation is calculated. Therefore, a global view it is only
possible when we can assume that all the agents think and behave similar.
• Not always is desireable for an agent to make public information about the
direct experiences or submit that information to an external authority.
• Therefore, a high trust on the central institution managing reputation is
essential.

Subjective vs Global
• Subjective
• The reputation is maintained by each agent and is calculated according to its
own direct experiences, information from its contacts, its social relations...

Advantages:
• Reputation values can be calculated taking into account the current state of
the agent and its individual particularities.

Disadvantages:
• The models are more complex, usually because they can use extra sources of
information.
• Each agent has to worry about getting the information to build reputation
values.
• Less information is available so the models have to be more accurate to
avoid noise.

A global reputation model: eBay

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.

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.

A subjective reputation model: 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.

The ReGreT
ODB IDB SDB system

Credibility
Neigh-
Witness
bourhood
reputation
reputation

Direct Reputation
Trust model

System
reputation
Trust

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 Contract
Quantity =c 300
Outcome
Prize =f 2000
Quality =f C Fulfillment
Quantity =f 295


Outcome
Prize =c 2000
offers_good_prices
Quality =c A
Quantity =c 300
maintains_agreed_quantities
Prize =f 2000
Quality =f C
Quantity =f 295

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

Imp(o, 1 )
Outcome
Prize =c 2000
Quality =c A
Quantity =c 300 Imp(o,  2 )
Prize =f 2000
Quality =f C
Quantity =f 295
Imp(o,  3 )

The ReGreT
ODB IDB SDB system

Credibility
Neigh-
Witness
bourhood
reputation
reputation
Reliability of the value based on:
Direct Reputation
Trust • Number of outcomes model

• Deviation: The greater the variability in
the rating values the more volatile will be
System
the other agent in the fulfillment of its
reputation
agreements.
Trust

Direct Trust
Trust relationship calculated directly from an agent’s
outcomes database.

DTa b ( )    (t , t )  Imp(o ,  )
i i
oi ODB gr,b )
a
(

f (ti , t )
 (t , ti ) 
o IDBa ,b f (t j , t )
gr (  ) ti
f (ti , t ) 
j

t

Direct Trust
DT reliability
a ,b a ,b
DTRLab ( )  No ( ODBgr (  ) )  (1  Dv ( ODBgr (  ) )

Number of Deviation
outcomes (Dv)
(No)
The greater the variability in
the rating values the more
volatile will be the other
agent in the fulfillment of its
agreements.
a ,b
No ( ODB gr (  ) ), itm  10

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.

B o C o
A o # u7 + D +
+ a1 # ^
o^
c1
o+
b2 c1
b1 u6
a2 o # o+ c2 #^ d1
u3
+ d1 +
a1 u2 u8 + d2
o u4
^
u1 u5
c2 u2
u9
#^ b2 u9 u1
# u3 u8

u6 u5
u4 u7
d2
^
o a2 b1 o
+ o + #
#
+ trade ^

B o C o
A o # + D +
+ # ^
^

b2 c1
b1 u1 # c2
o+ u4 #^
a2 o d1
+ u9 +
a1 d2
o u4
u3 ^
u1 u2 u5
u5
u2
u9 u3 u8
u6
u7 u8
u6
u7
cooperation
o o
Big exchange of sincere infor-
# + #
mation and some kind of predispo-
+
sition to help if it is possible. ^

B o C o
A o # + D +
+ # ^
^

b2 c1
b1 # o+ c2u3
u9
a2 o #^ d1
+ u1 +
a1 d2
o u4
u2 ^
u1 u5
u2
u9 u7 u8
u3 u8
u5
u6 u4
u6
u7
competition
o o
Agents tend to use all the available
# + #
mechanisms to take some advantage
+
from their competitors. ^

Witness u7

reputation # a1
o
c1
o+
Step 1: Identifying u6
the witnesses u3
d1
• Initial set of witnesses: u2 u8 +
?
Agents that have had
a trade Relation with
the target agent c2
#^ b2 u9 u1
#

u5
u4
d2
b1 ^
a2
+ o
trade

Witness u7
Grouping agents with frequent interactions
reputation them and considering each one of these
among
groups as a single source of reputation values:
• Minimizes u3 correlated evidence problem.
the witnesses the
• Initial set of witnesses: u2 u8
• Reduces the number of queries to agents that
probably will give us more or less the same
the target agent information. b2
#
To group agents ReGreT relies on sociograms.
u5
u4

trade

Witness reputation u7

Heuristic to identify groups and Central-point
the best agents to represent
them: u6
u3
1. Identify the components of
u2 u8
the graph.
2. For each component, find the
set of cut-points. b2
#
3. For each component that
does not have any cut-point, u5
u4
select a central point (node
with larger degree). Cut-point

cooperation

Witness u7

reputation

the witnesses u3
• Initial set of witnesses: u2 u8
the target agent b2
• Grouping and selecting
#
the most representative
witnesses u5
u4

trade

Witness
reputation

Step 1: Identifying
the witnesses u3
• Initial set of witnesses: u2
the target agent b2
• Grouping and selecting #
the most representative
witnesses u5

trade

Witness
reputation  Trustu 2b 2 ( ), TrustRLu 2b 2 ( ) 
u2

the witnesses u5
Step 2: Who can I
 Trustu 5b 2 ( ), TrustRLu 5b 2 ( ) 
trust?

Credibility model
Two methods are used to evaluate the credibility of
witnesses:

Credibility
(witnessCr)

Social relations Past history
(socialCr) (infoCr)

Credibility model
• socialCr(a,w,b): credibility that agent a assigns to agent w when
w is giving information about b and considering the social structure
among w, b and himself.
a a a
w w w
b b b

a a a
w w w
b b b

a a a
w w w
b b b

w - witness
competitive relation b - target agent
cooperative relation a - source agent

Credibility model
Regret uses fuzzy rules to calculate how the structure of
social relations influences the credibility on the information.

IF coop(w,b) is h
THEN socialCr(a,w,b) is vl

1 1

0 0
0 1 0 1
low moderate high very_low low moderate high very_high
(l) (m) (h) (vl) (l) (m) (h) (vh)

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 DTan (offers_good_quality ) is X AND coop(b,ni)  low
i

THEN Rab (offers_good_quality) is X
n i

IF DTRLan (offers_good_quality) is X’ AND coop(b,ni) is Y’
i

THEN RLab (offers_good_quality) is T(X’,Y’)
n i

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.

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.

Neigh-
Witness
bourhood
reputation
reputation

Direct Reputation
Trust model

System
reputation
Trust

A cognitive perspective to computational
reputation models

• A cognitive view on Reputation

• Repage, a computational cognitive reputation model

• [Properly] Integrating a [cognitive] reputation model into a

• Arguing about reputation concepts

Social evaluation
• A social evaluation, as the name suggests, is the evaluation by a social
entity of a property related to a social aspect.

• Social evaluations may concern physical, mental, and social properties of
targets.

• A social evaluation includes at least three sets of agents:
 a set E of agents who share the evaluation (evaluators)
 a set T of evaluation targets
 a set B of beneficiaries

We can find examples where the different sets intersect totally, partially,
etc...

e (e in E) may evaluate t (t in T) with regard to a state of the world that is in
b’s (b in B) interest, but of which b not necessarily is aware.
Example: quality of TV programs during children’s timeshare

Image and 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 in artificial societies
[Rosaria Conte, Mario Paolucci]

Image
“An evaluative belief; it tells whether the target is good or bad with respect
to a given behaviour” [Conte & Paolucci]

Is the result of an internal reasoning on
different sources of information that leads the
agent to create a belief about the behaviour
of another agent.

Beliefs The agent has accepted φ as something true
and its decisions from now on will take this
B into account.

Social evaluation 

Reputation
• A voice is something that “it is said”, a piece of information that is being
transmitted.

• Reputation: a voice about a social evaluation that is recognised by the
members of a group to be circulating among them.

Beliefs • The agent believes that the social
B(S(f)) evaluation f is communicated.

• This does not imply that the agent
believes that f is true.

Reputation
Implications:

• The agent that spreads a reputation, because it is not implicit that it
believes the associated social evaluation, takes no responsibility about
that social evaluation (another thing is the responsibility associated to
the action of spreading that reputation).

• This fact allows reputation to circulate more easily than image
(less/no fear of retaliation).

• Notice that if an agent believes “what people say”, image and
reputation colapse.

• This distinction has important advantages from a technical point of
view.

Gossip
• In order for reputation to exist, it has to be transmitted. We cannot have
reputation without communication.

• Gossip currently has the meaning of an idle talk or rumour, especially
about the personal or private affairs of others. Usually has a bad
connotation. But in fact is an essential element in human nature.

• The antecedents of gossip is grooming.

• Studies from evolutionary psicology have found gossip to be very
important as a mechanism to spread reputation [Sommerfeld et al. 07, Dunbar 04]

• Gossip and reputation complement social norms: Reputation evolves
along with implicit norms to encourage socially desirable conducts, such as
benevolence or altruism and discourage socially unacceptable ones, like
cheating.

Outline

• A cognitive view on Reputation

• Repage, a computational cognitive reputation model

• [Properly] Integrating a [cognitive] reputation model into a

• Arguing about reputation concepts

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.

RepAge memory

Value:
Rep Img P

P P Strength: 0.6

P P P

P P P P P

What do you mean by “properly”?

Current models

Planner Trust & Reputation
system
?

Inputs
Decision
mechanism
Comm

Black box Agent
Reactive


Current models

system
Value

Inputs
Decision
mechanism
Comm

Black box Agent
Reactive


The next generation?

system

Inputs
Decision
mechanism
Comm

Agent


The next generation?

Planner

Inputs
Decision
mechanism
Comm

Agent
Not only reactive...
... proactive

BDI model
• Very popular model in the multiagent community.

• Has the origins in the theory of human practical reasoning
[Bratman] and the notion of intentional systems [Dennett].

• The main idea is that we can talk about computer programs as if
they have a “mental state”.

• Specifically, the BDI model is based on three mental attitudes:
Beliefs - what the agent thinks it is true about the world.

Desires - world states the agent would like to achieve.

Intentions - world states the agent is putting efforts to achieve.

BDI model
• The agent is described in terms of these mental attitudes.

• The decision-making model underlying the BDI model is known as
practical reasoning.

• In short, practical reasoning is what allows the agent to go from
beliefs, desires and intentions to actions.

Multicontext systems
• Declarative languages, each with a set of
Logics axioms amd a number of rules of inference.

• Structural entities representing the main
architecture components. Each unit has a
UNITS single logic associated with it.

• Rules of inference wich relate formulae
Bridge Rules in different units.

• Sets of formulae written in the logic
Theories associated with a unit

U1 U2

d

U1:b , U2:d

U3:a

U3

U1 U2

b d

U1:b , U2:d

U3:a

U3

U1 U2

b d

U1:b , U2:d

U3:a

U3

a

Repage integration in a BDI architecture

Grounding Image and Reputation to BC-Logic

Arguing about Reputation Concepts
Goal: Allow agents to participate in argumentation-based dialogs regarding
reputation elements in order to:

- Decide on the acceptance of a communicated social evaluation based
on its reliability.
“Is the argument associated to a communicated social evaluation (and according to
my knowledge) strong enough to consider its inclusion in the knwoledge base of my
reputation model?”
- Help in the process of trust alignment.

What we need:
• A language that allows the exchange of reputation-related
information.
• An argumentation framework that fits the requirements imposed by
the particular nature of reputation.
• A dialog protocol to allow agents establish information seeking
dialogs.

The language: LRep

LREP : First-order sorted languange with
special predicates representing the
typology of social evaluations we use:
Img, Rep, ShV, ShE, DE, Comm. Ex 2: Linguistic Labels

•SF: Set of constant formulas

Allows LREP formulas to be nested in communications

• SV: Set of evaluative values
f:
{ 0 , 1, 2 , 3 , 4 }

The reputation argumentation framework

• Given the nature of social evaluations (the values of a social evaluation
are graded) we need an argumentation framework that allows to weight
the attacks.
Example: We have to be able to differentiate between Img(j,seller,VG)
being attacked by Img(j,seller,G) or being attacked by Img(j,seller,VB).

• Specifically we instantiate the Weighted Abstract Argumentation
Framework defined in

P.E. Dunne, A. Hunter, P. McBurney, S. Parsons, and M. Wooldridge,
‘Inconsistency tolerance in weighted argument systems’, in
AAMAS’09, pp. 851–858, (2009).

• Basically, this framework introduces the notions of strength and
inconsistency budgets (defined as the amount of “inconsistency” that the
system can tolerate regarding attacks) in a classical Dung’s framework.

Building Argumentative Theories

Argumentative theory
(Build from the Simple shared consequence relation
reputation theory)

Argumentation level
? ?
Reputation-related information

Consequence relation
Reputation theory: set of ground
(Reputation model)
elements (expressed in LREP) gathered
Specific to each agent
by j through interactions and
communications.

Attack and Strength

f:
{ 0 , 1, 2 , 3 , 4 }

Strength of the attack

Example of argumentative dialog

Role: seller Role: Inf
informant

Role: sell(q) Role: sell(dt)
• Agent i: proponent quality delivery time
• Agent j: opponent

j i
• Each agent is equipped with a Reputation Weighted Argument System


j i


j i

Strength of the attack

Using Inconsistency Budgets

j i

Outline
+ PART II:
 Trust Computing Approaches
Security
Institutional
Social
 Evaluation of Trust and Reputation Models

EASSS 2010, Saint-Etienne, France 111

Dr. Javier Carbó

GIAA – Group of Applied Artificial Intelligence
Univ. Carlos III de Madrid

Trust in Information Security
Same Word, Different World

Security approach tackles “hard” problems of trust.
They view trust as an objective, universal and
verifiable property of agents.
Their trust problems have solutions:
• False identity
• Reading/modification of messages by third parties
• Repudiation of messages
• Certificates of accomplishing tasks/services
according to standards

An example,
Public Key Infrastructure
LDAP directory
Certificate authority 4. Publication of
certificate

3. Public key
5. Certificate
sent
sent

2. Private key sent

1. Client identity
Registration authority


Trust in I&S, limitations
Their trust relies on central entities:
– Authorities, Trust Third Parties
– Partially solved using hierarchies of TTPs.
They ignore part of the problem:
- Top authority should be trusted by any other way
Their scope is far away from Real Life Trust issues:
– lies, defection, collusions, social norm violations, …


Institutions have proved to successfully regulate human
societies for a long time:
- created to achieve particular goals while complying norms.
- responsible for defining the rules of the game (norms), to
enforce them and assess penalties in case of violation.
Examples: auction houses, parliaments, stock exchange
markets,.…
Institutional approach is focused on the existence of
organizations:
• Providing an execution infrastructure
• Controlling the resources access
• Sanctionning/rewarding agents’ behaviors

An example: e-institutions


Institutional approach, limitations
They view trust as an partially objective, local and verifiable
property of agents.
Intrusive control on the agents (modification on the
execution resources, process killing, …)
They require a shared agreeement to define of what is
expected (norm compliance, case laws…)
They require a central entity and global supervision
– Repositories, access control entities should be
centralised
– Low scalability if every agent is observed by the
institution
Assumes that the institution itself is trusted


Social approach
Social approach consists in the idea of an auto-organized society
(Adam Smith’s invisible hand)
Each agent has its own evaluation criteria of what is expected:
no social norms, just individual norms
Each agent is in charge of rewards and punishments (often in
terms of more/less future cooperative interactions)
No central entity at all, it consists of a completely distributed
social control of malicious agents.
Trust as an emergent property
Avoids Privacy issues caused by centralized approaches


Social approach, limitations
Unlimited, but undefined and unexpected trust scope:
We view trust as a subjective, local and unverifiable
property of agents.
Exclusion/Isolation is the typical punishment for the
malicious agents  Difficult to enforce it in open and
dynamical societies of agents
Malicious behaviors may occur, they are supposed to be
prevented due to the lack of incentives and punishments.
Difficult to define which domain and society is appropriate
to test this social approach.


Ways to evaluate any system

 Integration on real applications
 Using real data from public datasets
 Using realistic data generated artificially
 Using ad-hoc simulated data with no
justification/motivation
 None of above

Ways to evaluate T&R in agent systems

 Integration of T&R on real agent applications
 Using real T&R data from public datasets
 Using realistic T&R data generated artificially
 Using ad-hoc simulated data with no
justification/motivation
 None of above

Real Applications using T&R in an agent
system
• What real application are we looking for?
• Trust and reputation:
– System that uses (for something) and exchanges
subjective opinions about other participants 
Recommender Systems
• Agent System:
– Distributed view, no central entity collects, aggregates
and publishes a final valuation  ???

Real Applications using T&R in an agent
system
• Desiderata of application domains:
(To be filled by students)

Real data & public datasets
• Assuming real agent applications exists, would data
be publicly available?
– Privacy concerns
– Lack of incentives to save data along time
– Distribution of data.Heisenberg uncertainty
principle: If users knew their subjective opinions
would be collected by a central entity, they would
not be as if their opinions had just a private
(supposed-to-be friendly) reader.
• No agents, no distribution  public dataset from
recomender systems

A view on privacy concerns

• Anonymity: use of arbitrary/secure pseudonysms
• Using concordance: similarity between users within a
single context. Mean of differences rating a set of items.
Users tend to agree. (Private Collaborative Filtering using
estimated concordance measures, N. Lathia, S. Hailes, L.
Capra, 2007)
• Secure Pair-wise comparison of fuzzy ratings
(Introducing newcomers into a fuzzy reputation agent
system, J. Carbo, J.M. Molina, J. Davila, 2002)

Real Data & Public Datasets

• MovieLens, www.grouplens.org: Two datasets:
– 100,000 ratings for 1682 movies by 943 users.
– 1 million ratings for 3900 movies by 6040 users.
• These are the “standard” datasets that many
recommendation system papers use in their evaluation

My paper with MovieLens

• I selected users among those who had rated 70 or more
movies, and we also selected the movies that were
evaluated more than 35 times in order to avoid the
sparsity problem.
• Finally we had 53 users and 28 movies.
• The average votes per user is approximately 18. So the
sparsity of the selected set of users and movies is under
35%
“Agent-based collaborative filtering based on fuzzy
recommendations” J. Carbó, J.M. Molina, IJWET v1 n4,
2004


BookCrossing (BX) dataset:
• www.informatik.uni-freiburg.de/~cziegler/BX
• collected by Cai-Nicolas Ziegler in a 4-week crawl (August
/ September 2004) from the Book-Crossing community.
• It contains 278,858 users providing 1,149,780 ratings
(explicit / implicit) about 271,379 books.


Last.fm Dataset
• top artists played by all users:
– contains <user, artist-mbid, artist-name, total-plays>
– tuples for ~360,000 users about 186,642 artists.
• full listening history of 1000 users:
– Tuples of <user-id, timestamp, artist-mbid, artist-
name, song-mbid, song-title>
• Collected by Oscar Celma, Univ. Pompeu Fabra
• www.dtic.upf.edu/~ocelma/MusicRecommendationDatas
et


Jester Joke Data Set:
• Ken Goldberg from UC Berkeley released a dataset from
Jester Joke Recommender System.
• 4.1 million continuous ratings (-10.00 to +10.00) of 100
jokes from 73,496 users.
• www.ieor.berkeley.edu/~goldberg/jester-data/
• It differentiates itself from other datasets by having a
much smaller number of rateable items.


Epinions dataset, collected by P. Massa:
• in a 5-week crawl (November/December 2003) from the
Epinions.com
• Not just ratings about items, also trust statements:
– 49,290 users who rated a total of
– 139,738 different items at least once, writing 664,824
reviews.
– 487,181 issued trust statements.
• only positive trust statements and not negative ones


Advogato: www.trustlet.org
• a weighted dataset. Opinions aggregated (centrally) on a
3 levels base, Apprentice, Journeyer, and Master
• Tuples of: minami -> polo [level="Journeyer"];
• Used to test trust propagation in social networks
(asuming trust transitivity).
• Trust metric (by P. Massa) uses this information in order
to assign to every user a final certification level
aggregating weighted opinions.


MoviePilot dataset: www.moviepilot.com
• this dataset contains information related to concepts
from the world of cinema, e.g. single movies, movie
universes (such as the world of Harry Potter movies),
upcoming details (trailers, teasers, news, etc
• RecSysChallenge: live evaluation session will take place
where algorithms trained on offline data will be
evaluated online, on real users.
Mendeley dataset: www.mendeley.com
• recommendations to users about scientific papers that
they might be interested in.


• No agents, no distribution  public dataset from
recomender systems
• Authors have to distribute opinions to participants in
some way.
• Ratings about items, not trust statements.
• Relationship between # of ratings / # of items too low
• Relationship between # of ratings / # of users too low
• No time-stamps
• Papers intend to be based on real data, but required
transformation from centralized to distributed
aggregation distort reality of these data.

Realistic Data

• We need to generate realistic data to test trust and
reputation in agent systems.
• Several technical/design problems arise:
– Which # of users, ratings and items we need?
– How much dynamic would be the society of agents?
• But the hardest part is the pshichological/sociological
one:
– How individuals take trust decisions? Which types of
individuals?
– How real society of humans trust? How many of each
individual type belong to real human society?

Realistic Data

• Large-scale simulation with Netlogo
(http://ccl.northwestern.edu/netlogo/)
• Others: MASON (https://mason.dev.java.net/), RePast
(http://repast.sourceforge.net/)
• But there are mainly adhoc simulations which are
difficult to repeat by third parties.
• Many of them are unrealistic agents with binary
behaviour altruist/egoist based on game theory views.

Examples of AdHoc Simulations

• Convergence of reputation image to real behaviour of
agents. Static behaviours, no recomendations, just
consume/provide services. Worst case.
• Maximum Influence of cooperation. Free and honest
recomendations from every agent based on consumed
services. Best case.
• Inclusion of dynamic behaviours, different % of malicious
agents in society, collusions between recommenders and
providers, etc. Compare results with the previous ones.
“Avoiding malicious agents using fuzzy recommendations” J.
Carbo, J. M. Molina, J. Dávila. Journal of Organizational
Computing & Electronic Commerce, vol. 17, num. 1

Technical/Design Problems to generate
simulated data
• Lessons learned from the ART testbed experience.
• http://megatron.iiia.csic.es/art-testbed/
• A testbed would help to compute fair comparisons:
“Researchers can perform easily-repeatable experiments
in a common environment against accepted
benchmarks”
• Relative Success:
– 3 international competitions jointly with AAMAS 06-
08.
– Over 15 participants in each competition.
– Several journal and conference publications use it.

ART Interface

The agent system is displayed as a topology in the left, while
in the left two panels show the details of particular agent
statistics and of global system statistics.

The ART testbed

• The simulation creates opinions according to an error
distribution of zero mean and a standard deviation s:
s = (s∗ + α / cg) t
• where s∗, unique for each era, is assigned to an appraiser
from a uniform distribution.
• t is the true value of the painting to be appraised
• α is a hidden value fixed for all appraisers that balances
opinion-generation cost and final accuracy.
• cg, the cost an appraiser decides to pay to generate an
opinion. Therefore, the minimum achievable error
distribution standard deviation is s∗ · t

The ART testbed

• Each appraiser a’s actual client share ra takes into
account the appraiser’s client share from the previous
timestep:
ra = q · ra’ + (1 − q) · ˜ra
• where ra’ is appraiser a’s client share in the previous
timestep.
• q is a value that reflects the influence of previous client
share size on next client share size (thus the volatility in
client share magnitudes due to frequent accuracy
oscillations may be reduced)

2006 ART Competition

2006 Competition setup:
• Clients per agent: 20, Painting eras: 10, games with 5
agents
• Costs 100/10/1, Sensing-Cost-Accuracy=0.5, Winner iam
from Southampton Univ.
Post competition discussion notes:
• Larger number of agents required, Definition of dummy
agents, Relate # of eras with # of agents, More fair
distribution of expertise (just uniform), More abrupt
change in # of clients (greater q), Improving expertise
over time?

2006 ART Winner conclusions
“The ART of IAM: The Winning Strategy for the 2006
Competition”, Luke Teacy et al, Trust WS, AAMAS 07.
• It is generally more economical for an agent to purchase
opinions from a number of third parties than it is to
invest heavily in its own opinion
• There is little apparent advantage to reputation sharing.
reputation is most valuable in cases where direct
experience is relatively more difficult to acquire
• The final lesson is that although trust can be viewed as a
sociological concept, and inspiration for computational
models of trust can be drawn from multiple disciplines,
the problem of combining estimates of unknown
variables (such as trustee behaviour) is fundamentally a
statistical one.

2007 Competition Setup:
• Costs 100/10/0.1, All agents have equal sum of expertise
values, Painting eras: static but unknown, Expertise
assignments may change during the course of the game,
Include dummy agents, games with 25 agents
2007 Competition Discussion Notes:
• it need sto facilitate reputation exchange
• It doesn’t have to produce all changes at the same time,
Gradual changes
• Studying barriers to entry; how a new agent joins an
existing MAS: Cold start vs. Hot start (exploration vs
explotation)
• More competitive dummy agents
• relationship between opinion generation cost and accuracy


2008 Competition Setup:
• limited in the number of certainty and opinion requests
that he can send.
• Certainty request has cost.
• deny the use of self opinions
• Wider range of expertise values
• Every time step, select randomly a number of eras to
change, and add a given amount of positive change
(increase value). For every positive change, apply also a
negative change of the same amount, so that the average
expertise of the agent is not modified

Evaluation criteria

• Lack of criteria on which and how the very different trust
decisions should be considered
Conte and Paolucci 02:
• epistemic decisions: those about about updating and
generating trust opinions from received reputations
• pragmatic-strategic decisions are decisions of how to
behave with partners using these reputation-based trust
• memetic decisions stand for the decisions of how and
when to share reputation with others.

Main Evaluation Criteria of The ART
testbed
• The winning agent is selected as the appraiser with the
highest bank account balance in the direct confrontation
of appraiser agents repeated X times.
• In other words, the appraiser who is able to:
– estimate the value of its paintings most accurately
– purchase information most prudently.
• Where an ART iteration involves 19 steps (11 decisions, 8
interactions) to be taken by an agent.

Trust decisions in ART testbed
1. How our agent should aggregate reputation information
about others?
2. How our agent should trust weights of providers and
recommenders are updated afterwards?
3. How many agents our agent should ask for reputation
information about other agents?
4. How many reputations and opinions requests from other
agents should our agent answer?
5. How many agents our agent should ask for opinions about our
assigned paintings?
6. How much time (economic value) our agent should spend
building requested opinions about the paintings of the other
agents?
7. How much time (economic value) our agent should spend
building the appraisals of the own paintings?
(AUTOPROVIDER!)
…

Limitations of Main Evaluation Criteria
of ART testbed
From my point of view:
• Evaluates all trust decisions jointly: should participants
play provider and consumer roles jointly of just the role
of opinion consumers?
• Is the direct confrontation of competitor agents the right
scenario to compare them?

Providers vs. Consumers

• Playing games with two participants of 2007 competition
(iam2 and afras) and other 8 dummy agents.
• Dummy agents implemented ad hoc to be the solely
opinion providers, they do not ask for any service to 2007
participants.
• None of both 2007 participants will ever provide
opinions/reputations, they are just consumers.
•  Differences between both agents were much less than
the official competition stated (absolutely and relatively).
“An extension of a fuzzy reputation agent trust model in the
ART testbed” Soft Computing v14, issue 8, 2010

Trust Strategies in
Evolutive Agent Societies
• An evolutionarily stable strategy (ESS) is a strategy which,
if adopted by a population of players, cannot be invaded
by any alternative strategy
• An evolutionarily stable trust strategy is a strategy which,
if becomes dominant (adopted by a majority of agents)
can not be defeated by any alternative trust strategy.
• Justification: The goal of trust strategies is to establish
some kind of social control over malicious/distrustful
agents
• Assumption: agents may change of trust strategy. Agents
with a failing trust strategy would get rid of it and they
would adopt a successful trust strategy in the future.

An evolutive view of ART games

• We consider a failing trust strategy the one who lost
(earning less money than the others) the last ART game.
• We consider the successful trust strategy to the one who
won the last ART game (earning more money than the
others).
• By this way replacing in consecutive games the
participant who lost the game by the one who won it.
• We have applied it to the 16 participant agents of 2007
ART competition

and so on…

16 participants Winner
in 2007 competition Winner

ART gam
ART game
ART game
Loser
Loser

Game Winner Earnings Loser Earnings
1 iam2 17377 xerxes -8610
2 iam2 14321 lesmes -13700
3 iam2 10360 reneil -14757
4 iam2 10447 blizzard -7093
5 agentevicente 8975 Rex -5495
6 iam2 8512 alatriste -999
7 artgente 8994 agentevicente 2011
8 artgente 10611 agentevicente 1322
9 artgente 8932 novel 424
10 iam2 9017 IMM 1392
11 artgente 7715 marmota 1445
12 artgente 8722 spartan 2083
13 artgente 8966 zecariocales 1324
14 artgente 8372 iam2 2599
16 artgente 8384 UNO 2719
18 iam2 6279 JAM 3486
19 iam2 14674 artgente 2811

Results of repeated games
2007 winner is not a Evolutionarily Stable Strategy.
• Although the strategy of the winner of the 2007 spreads
in the society of agents (until 6 iam2 agents out of 16), it
never becomes dominant (no majority of iam2 agents).
• iam2 strategy is defeated by artgente strategy, which
becomes dominant (11 artgente agents out of 16).
Therefore its superiority as winner of 2007 competition
is, at least, relative.
• The right equilibrium of trust strategies that form an
evolutionarily stable society is composed by 10-11
Artgente agents and 6-5 iam2 agents.

CompetitionRank EvolutionRank Agent ExcludedInGame
6 1 artgente -
1 2 iam2 -
2 3 JAM 18
7 4 UNO 16
4 5 zecariocales 13
5 6 spartan 12
9 7 marmota 11
13 8 IMM 10
10 9 novel 9
15 10 agentevicente 8
11 11 alatriste 6
12 12 rex 5
3 13 Blizzard 4
8 14 reneil 3
14 15 lesmes 2
16 16 xerxes 1

Other Evaluation Criteria of the ART
testbed
• The testbed also provides functionality to compute:
– the average accuracy of the appraiser’s final appraisals
(final appraisal error mean)
– the consistency of that accuracy (final appraisal error
standard deviation)
– the quantities of each type of message passed
between appraisers are recorded.
• We could take into account other relevant evaluation
criteria?

Evaluation criteria from the agent-based
view
Characterization and Evaluation of Multi-agent System, P.
Davidsson, S. Johanson, M. Svahnberg In Software
Engineering for Multi-Agent Systems IV, LNCS 3914, 2006.
9 Quality atributes:
1. Reactivity: How fast are opinions re-evaluated when
there are changes in expertise?
2. Load balancing: How evenly is the load balanced
between the appraisals?
3. Fairness: Are all the providers treated equally?
4. Utilization of resources: Are the available
abilities/information utilized as much as is possible?

view
5. Responsiveness: How long does it take for the
appraisals to get response to an individual request?
6. Communication overhead: How much extra
communication is needed for the appraisals?
7. Robustness: How vulnerable is the agent to the absence
of responses?
8. Modifiability: How easy is it to change the behaviour of
the agent in very different conditions?
9. Scalability: How good is the system at handling large
numbers of providers and consumers)?

view
Evaluation of Multi-Agent Systems: The case of Interaction,
H. Joumaa, Y. Demazeau, J.M. Vincent, 3rd Int. Conf. on
Information & Communication Technologies: from
Theory to Applications. IEEE Computer Society, Los
Alamitos (2008)

• An evaluation at the interaction level, based on the
weight of the information brought by a message.
• A function Φ is defined in order to calculate the weight of
pertinent messages.

view
• The relation between the received message m and the
effects on the agent is studied in order to calculate the
Φ(m) value. According to the model, two kinds of
functions are considered:
– A function that associates weight to the message
according to its type.
– A function that associates weight to the message
according to the change provoked on the internal
state and the actions triggered by its reception.

Consciousness Scale

• Too much quantification (AI is not just statistics…)
• Compare agents qualitatively  Measure their level of
consciusness
• A scale of 13 conscious levels according to the cognitive
skills of an agent, the “Cognitive Power” of an agent.
• The higher the level obtained, the more the behavior of
the agent resembles humans
• www.consscale.com

Bio-inspired order of Cognitive Skills

• From the point of view of emotions (Damasio, 1999):

“Emotion”

“Feeling”

“Feeling of a Feeling”

“Fake Emotions”


• From the point of view of perception and action (Perner,
1999):

“Perception”

“Adaptation”

“Attention”

“Set Shifting”

“Planning”
“Imagination”


• From the point of view of Theory of Mind (Lewis 2003):

“I Know”

“I Know I Know”

“I Know You Know”

“I Know You Know I Know”

Consciousness Levels
Super-Conscious

Human-like

Social

Empathic

Self-Conscious

Emotional

Executive

Attentional

Adaptive

Reactive

Evaluating agents with ConsScale

Thank you !


T9. Trust and reputation in multi-agent systems

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