AAMAS08Trust.ppt

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AAMAS’08 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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MAIA – Intelligent Autonomous MachineINRIA – LORIA – Laboratory of IT Research and its Applications

Dr. Guillaume Muller

Dr. Laurent Vercouter

G2I – Division for Industrial Engineering and Computer SciencesEMSE – Ecole des Mines of St-Etienne

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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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Mr. Yellow

Direct experiencesTwo years ago... Trust based on...

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Mr. Yellow

Third party informationTrust based on...

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Mr. Yellow

Third party informationTrust based on...

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Mr. Yellow

ReputationTrust based on...

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Mr. Yellow

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?

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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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• Security approach


Agent identity validation.Integrity, authenticity of messages....

I’m Alice

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Security approach

Institutional approach

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• Institutional approach


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Security approach


Social approach

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Example: P2P systems

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Security approach


Social approachTrust and reputation mechanisms are at this level.

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


• 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


• 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 person’s humanity and possibilities or to encourage the person’s trustworthiness.” [Russel Hardin, 2002]

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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

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

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

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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 let’s 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, ) ΛBelieves(i, OccCap(j, )) ΛBelieves(i, OccPower(j, , )) ΛBelieves(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, ) ΛBelieves(i, CondCap(j, )) ΛBelieves(i, CondPower(j, , )) ΛBelieves(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 j’s 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 target’s 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]

Target

…

Participants

Observer

observationsEvaluator

Beneficiary

evaluations

Beneficiary

reputations

Propagator

Propagator

Propagator

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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 ?

• Trust models allow:– Identifying and isolating

untrustworthy agents

Bob

I don’t trust Bob

I don’t trust Bob

I don’t trust Bob

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untrustworthy agents– Evaluating an interaction’s

utility Bob

I can sell you the information you

require

I don’t trust Bob

No, thank you !

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untrustworthy agents– Evaluating an interaction’s

utility– Deciding whether and with

whom to interact

BobI can sell you the information you

require

I can sell you the information you

require

I trust Bob more than Charles

Ok, Bob. It’s a deal

No, thank you !

Charles

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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

(message)key

certifies

Authority

Bob Alice

trusts

Certification:

signs

Bob’s ID

Bob’s pubkey

Authority

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• Context: replace the centralized Trusted Authorities in Public Key management

message

certifies

Authority

Bob Alice

trusts

message

‘Web ofTrust’

certifies

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• 2 kinds of trusts:

• Tc Trust in the certificate: {undefined,marginal,complete}

• Ti Trust as an introducer: {untrustworthy,marginal,full,don’tknow}

• OpenPGP computes reputations based on transitivity along all existing pathes

• >X complete OR >Y marginal c

• >0 marginal (or >) 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.

‘Web ofTrust’

TiTc

Tc

Ti

Ti

message

certifies

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Marsh’s 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) × Ťx(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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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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• 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.

eBay model

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+ Advantages:

+ Used everyday

+ In a real life application

+ Very simple

– Limits: [Dellarocas, 00&01] [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

eBay model

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• 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

OnSale model

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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 oncemore than one interaction => most recent rating considered

.5. Higher reputations are updated more moderatetly

Currentrating

Memory ofThe system

Reputation ofthe rater

Normalizedprev. reputation

Dumpingfactor

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Histos• Aim: compute a global ‘personalized reputation’ (PRp) value for each member

• ‘personalized reputation’ is computed by transitivity

1. Find all directed paths from A to AL

with length N2. Keep only the most recent ones3. Start a backward recursion

1. If path length = 1,PRp = rating

2. If path length > 1,PRp = f(Raters’PRp,rating)

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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 Prisonner’s Dilemma (IPD)

Coop. Defeat

Coop. 1

1

0

5Defeat 5

0

3

3• 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 isT(A,B) = number of honest rounds / number of total rounds

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• 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.

1.0

0.7

0.25

0.8

0.2

Trust Net [Schillo & Funk, 99]

– Binary evaluation

– Annouced behaviour

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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 A×utility)/(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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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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LIAR: Social control of agent communications

Social Control

Interactions

Definition ofAcceptability

(Social norms)

(Reputations)

Representation(Social commitments)

Sanction

Trust intentions

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LIAR: Social commitments and norms

Social Commitment example:

Observer

Debtor(sender) Content

State

Utterance time

Creditor(receiver)

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LIAR: Social commitments and norms

Social Norm example:

Prohibition PunishersTargets Evaluators

Content

Condition

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The LIAR agent architecture

Interactions

Reputations

Socialcommitments

Observation

Socialpolicies

Socialnorms

Evaluation

Punishment

Initialisation

Trustintention

Context

Reasoning

Mentalstates

Decision

Sanction

Recommendationfiltering

Recommendations

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LIAR: partial observation

inform(p)Agent A Agent B

Agent C Agent D

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sc(A,B,8pm,[8pm-9pm],active,p)

A CSAB

D CSAB

C CSAB

B CSAB

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cancel(p)

A CSAB

D CSAB

C CSAB

B CSAB

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Detection of violations

Evaluator Propagatorobservations(ob)

socialcommitment

updatesocialpolicy

generationsocialpolicy

evaluation

JustificationProtocol[p

roof

rec

eive

d]it

erat

ion

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Reputation types in LIAR

Rptarget (facet,dimension,time) ∈[-1,+1] {unknown}Ս

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

beneficiary

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Reputation computation

Direct Interaction based ReputationSeparate the social policies according to their state

associate a penalty to each set

reputation = weighted average of the penalties

Reputation Recommendation based Reputationbased on trusted recommendation

reputation = weighted average of received values

weighted by the reputation of the punisher

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LIAR decision process

ObsRcbRp EvRcbRp RpRcbRpObsRcbRpDIbRp GDtT

(*) -> (unknown) or not relevant or not discriminant

(*) (*) (*) (*) (*)

Trust_int = trust

Trust_int = distrust

>θ trustDIbRp

>θ trustIIbRp

>θ trustObsRcbRp

>θ trustEvRcbRp

>θ trustRpRcbRp

<θ distrustDIbRp

<θ distrustIIbRp

<θ distrustObsRcbRp

<θ distrustEvRcbRp

<θ distrustRpRcbRp

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LIAR: conclusion

• LIAR is adapted to P2P infrastructures• Partial observations/incomplete information

• Scalable

• Applied in a GNUtella–like 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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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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Reputationmodel

Witnessreputation

Systemreputation

Neigh-bourhoodreputation

ODB

DirectTrust

Credibility

IDB SDB

Trust

The ReGreTsystem

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Reputationmodel

Witnessreputation

Systemreputation


ODB

DirectTrust

Credibility

IDB SDB

Trust

The ReGreTsystem

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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.

Outcomes and Impressions

Prize =c 2000Quality =c AQuantity =c 300

Prize =f 2000Quality =f CQuantity =f 295

Example:

Outcome

Contract

Fulfillment

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Outcome

offers_good_prices

maintains_agreed_quantities

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Impression:

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




Outcome),(Imp 1o

),(Imp 2o

),(Imp 3o

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Reputationmodel

Witnessreputation

Systemreputation


ODB

DirectTrust

Credibility

IDB SDB

Trust

The ReGreTsystem

Reliability of the value based on:

• Number of outcomes

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

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Reputationmodel

Witnessreputation

Systemreputation


ODB

DirectTrust

Credibility

IDB SDB

Trust

The ReGreTsystem

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Witness reputationReputation 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:

1. Find Witnesses

• Direct relation with target

• Use of sociograms (cut-points and central points)

2. Weight each recommendation with the credibility

Advantages:

+ Minimizes the correlated evidence problem.

+ Reduces the number of queries

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Credibility model

Two methods are used to evaluate the credibility of witnesses:

Credibility(witnessCr)

Social relations(socialCr)

Past history(infoCr)

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Reputationmodel

Witnessreputation

Systemreputation


ODB

DirectTrust

Credibility

IDB SDB

Trust

The ReGreTsystem

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ReGreT uses fuzzy rules to model this reputation.

IF is X AND coop(b, ) lowTHEN is X

)( d_qualityoffers_gooDTina

)( d_qualityoffers_gooR bain

in

IF is X’ AND coop(b, ) is Y’ THEN is T(X’,Y’)

)( d_qualityoffers_gooDTRLina

)( d_qualityoffers_gooRL bain

in

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.

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Reputationmodel

Witnessreputation

Systemreputation


ODB

DirectTrust

Credibility

IDB SDB

Trust

The ReGreTsystem

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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.

System reputation

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Trust decision

Reputationmodel

Witnessreputation

Systemreputation


DirectTrust

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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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.

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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.

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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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The Agent Reputation and Trust Testbed

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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

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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

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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

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Appraiser Agent

Appraiser Agent

Client

Client

Client

Client Share

Opinions and Reputations

Appraiser Agent

Appraiser Agent

Appraiser Agent

Testbed Game RulesAgents function as art appraisers with varying expertise in

different artistic eras.

For a fixed price, clients ask appraisers to provide

appraisals of paintings from various eras.

If an appraiser is not very knowledgeable

about a painting, it can purchase "opinions"

from other appraisers.

Appraisers can also buy and sell reputation information about other

appraisers.

Appraisers whose appraisals are more

accurate receive larger shares of the client base

in the future. 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)

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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

appraiser’s internal trust model– Serves as standardized format

for inter-agent communication

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Step 2: Reputation Transactions

ProviderRequester

Request

Accept

Payment

Reputation

Requester sends request message to a potential reputation provider, identifying

appraiser whose reputation is

requested

Potential reputation provider sends

“accept” message

Requester sends fixed payment to the

provider

Provider sends reputation

information, which may not be truthful

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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 provider’s 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

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Step 3: Certainty & Opinion Transactions

ProviderRequester

Request

Certainty

Request

Opinion

Requester sends certainty request

message to potential providers, identifying

an era

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 opinion request

messages to potential providers, identifying

a painting

Provider sends opinion, which may not be truthful and

receive a fixed payment

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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

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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

1. Building an agent– creating a new agent class– strategic methods

2. Running a game– designing a game– running the game

3. Viewing the game– Running a game monitor interface

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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:

<agentConfig>

<classFile>

c:\ARTAgent\testbed\participants\MyAgent.class

</classFile>

</agentConfig>

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 > 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

Reputation values are assigned to every agent

The agent class is extended

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 createdFor 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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