trust analysis through relationship identification ronald ashri 1, sarvapali d. ramchurn 1, jordi...
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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)