Trust Analysis through Relationship Identification

Ronald Ashri1, Sarvapali D. Ramchurn1, Jordi Sabater2, Michael Luck1 and Nick Jennings1

1. Intelligence, Agents, Multimedia, University of Southampton

2. Institute of Cognitive Science and Technology, CNR, Roma

Talk Outline

Motivation Relationship Identification Relationship Characterisation Relationship Interpretation

Motivation (0) Trust

Expectation on the efficiency or effectiveness of an opponent (when it has some opportunity to defect)

Highly context dependent and application specific – hard (or impossible) to design one model for all.

The more information components the better (e.g. Debenham,Sierra,2005, Sabater,Sierra,2002, Huynh et al.,2004, Ramchurn et al, 2004, Patel et al, 2005)

Motivation Most mechanisms for evaluating trust depend

on using: history of interactions to form Confidence:

recommendations from other agents to get Reputation

1

)),('(),(

n

ConnCon

nw

ConwConwrep

),(),()( 21

Motivation (2) These face some challenges

Obtaining a history of interactions May take time to build sufficient history to

deduce correctly (may suffer some loss) Which agents to choose first?

Obtaining the recommendations of other agents Assume the recommendations are truthful AND

accurate Which recommendations to give more importance

to?

Motivation (3) In both of these cases the relationships

between agents are rarely taken into account in manipulating and using the information received

This work provides the foundation for improving trust evaluation by taking into account relationships between agents

Why take into account relationships?

Relationships can provide more information about the context of interaction

They can reveal whether two agents are in competition, cooperation or inclined to collude

This in turn helps in refining trust evaluations since it provide clues as to how agents may behave

Approach Relationship Identification

Generic Relationship Identification Model Relationship Characterisation

Application Domain Model Identify of all the possible relationships

which are the most relevant Relationship Interpretation

Use identified relationships and additional context information to derive trust valuations

Relationship Identification

What are relationships?

Relationship IdentificationFoundational Concepts (Luck and d’Inverno – SMART)

Attributes are describable features of the environment

An environment is a set of attributes Actions can change environments by

adding or removing attributes A goal is a set of attributes describing

desirable environmental states

Relationship IdentificationAgents

An agent is described by

Attributes – budget,organisation,products

Actions – selling,buying products

Goals (G) – acquiring information, obtaining a product

Relationship IdentificationViewable Environment

Agents sense the environment to take decisions about which goals to perform or to verify results of actions

The resulting set of attributes describes a viewable environment (VE)

Relationship IdentificationRegion of Influence

Agents can affect the environment by performing actions

The set of attributes that they can affect define a region of influence (ROI)

Relationship IdentificationAgent Interaction Model

Agent A

Environment

viewable environment

region of influence

Relationship IdentificationAgent Interaction Model

Agent A

Environment

viewable environment

region of influence

Agent B

viewable environment

region of influence

region of influence

Relationship Characterisation

?

?

Which relationships exist?

?

Agent-Based Market Model

regimentedBy

SingleMarket

Organisation

Product

AtomicProduct

composedFrom

minCardinality 1

CompositeProduct

requiressells

sellsInaffiliatedTobuysFrom

Agent

consistOf

regimentedBy

Market

regimentedByconsistsOf

CompositeMarket

Institution

consistOf

sells sellsInbuysFromrequires

affiliatedTo

regimentedBy

Mapping

Buyer A

Environment

market

productto sell

goal(product to buy)

VEB

Trade-Dep

VEA

ROIA

GB

VEB

Comp-Sell

VEA

ROIA

ROIB

VEB

Comp-Buy

VEA

GBA

VEB

Collaboration

VEA

ROIA

GB

ROIB

GA

Tripartite Relationships

VEC

VEB

VEA

ROIBGC

ROIAGB

Relationship Interpretation

Trade-Dep

Competition

Who should I trust??

Coll

Trust Modelling Confidence:

Direct Interactions Starting value depending on agent’s perception of

environment Reputation:

Witnesses or other interacting agents. Trust function eg.

)()1(),(),( repConT

1

)),('(),(

n

ConnCon

nw

ConwConwrep

),(),()( 21

Specifying Parameters, how?

Starting confidence

Weights of confidence ratings in the reputation model

Relationships provide a context dependent means of doing this

Trust Inferences Intensity of Relationships

Socio-Economic concepts Relative value of goods traded (in Trade-Dep or Coll) Relative share of the market (in Comp-Buy, Comp-Sell) Context: C Relationship: R

]1,0[: RCI

Competition

Give low starting confidence

Give low weights to trust reported by those agents

VEBVEA ROIAROIB

VEBVEAGBA

),(1 RCIsconf

),(1 RCIw

Collaboration

Start with high confidence (proportional to I(C,R))

Give more weight to reported confidence ratings (Proportional to I(C,R)).

VEBVEA

ROIAGB

ROIBGA

Dependencies

A depends on B to achieve its goal A will give low starting confidence

B might give high starting confidence (I(C,R)) and may also give more importance to A’s reported trust values (I(C,R)).

VEBVEA

ROIAGB

),(1 RCIsconf

Collusion B depends on A and B collaborates

with/depends on C.

A will not trust B’s ratings of C if A depends on B and vice versa (decreases with the intensity of B and C’s relationship). E.g.

VEC

VEBVEA

ROIAROIBGC

ROIAGB

),(),(1 21 RCIRCIkw

Conclusions and Future Work An abstract model to analyse relationships Relationships are important in analysing trust

(e.g. Regret) Can provide agents with a context-dependent

means to define starting confidence and weights

Simulate and evaluate the model with a number of trust metrics

Learn to balance the importance of relationships with that of direct interactions and other information

Questions?

For more info:-Relationships: R. Ashri and M. Luck, actSMART: Building a SMART system, in Understanding Agent Systems, M. d'Inverno and M. Luck (eds), Springer, 2003

Trust and Reputation Models (Reviews):-S. D. Ramchurn, D. Huynh and N. R. Jennings (2004) "Trust in multiagent systems" The Knowledge Engineering Review 19 (1) 1-25.- Jordi Sabater & Carles Sierra, Review on Computational Trust and Reputation Models, Artificial Intelligence Review, Volume 24, Number 1, September 2005, pp. 33-60(28)