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Trust Analysis through Relationship Identification

Trust Analysis through Relationship Identification. Ronald Ashri 1 , Sarvapali D. Ramchurn 1 , Jordi Sabater 2, Michael Luck 1 and Nick Jennings 1. Intelligence, Agents, Multimedia, University of Southampton Institute of Cognitive Science and Technology, CNR, Roma. Talk Outline.

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Trust Analysis through Relationship Identification

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  1. Trust Analysis through Relationship Identification Ronald Ashri1, Sarvapali D. Ramchurn1, Jordi Sabater2, Michael Luck1 and Nick Jennings1 • Intelligence, Agents, Multimedia, • University of Southampton • Institute of Cognitive Science and Technology, CNR, Roma

  2. Talk Outline • Motivation • Relationship Identification • Relationship Characterisation • Relationship Interpretation

  3. 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)

  4. Motivation • Most mechanisms for evaluating trust depend on using: • history of interactions to form Confidence: • recommendations from other agents to get Reputation

  5. 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?

  6. 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

  7. 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

  8. 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

  9. Relationship Identification What are relationships?

  10. 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

  11. Relationship IdentificationAgents • An agent is described by • Attributes – budget,organisation,products • Actions – selling,buying products • Goals (G) – acquiring information, obtaining a product

  12. 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)

  13. 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)

  14. Relationship IdentificationAgent Interaction Model Agent A Environment viewable environment region of influence

  15. Relationship IdentificationAgent Interaction Model Agent A Agent B Environment viewable environment viewable environment region of influence region of influence region of influence

  16. Relationship Characterisation ? ? ? Which relationships exist?

  17. Agent-Based Market Model

  18. Mapping Buyer A Environment market product to sell goal (product to buy)

  19. Trade-Dep VEB VEA ROIA GB

  20. Comp-Sell VEB VEA ROIA ROIB

  21. Comp-Buy VEB VEA GBA

  22. Collaboration VEA VEB ROIB GA ROIA GB

  23. Tripartite Relationships VEA VEB ROIA VEC GB ROIB GC

  24. Relationship Interpretation Coll Competition Trade-Dep Who should I trust??

  25. Trust Modelling • Confidence: • Direct Interactions • Starting value depending on agent’s perception of environment • Reputation: • Witnesses or other interacting agents. • Trust function eg.

  26. Specifying Parameters, how? • Starting confidence • Weights of confidence ratings in the reputation model Relationships provide a context dependent means of doing this

  27. 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

  28. VEB VEB VEA VEA ROIA GBA ROIB Competition • Give low starting confidence • Give low weights to trust reported by those agents

  29. VEA VEB ROIB GA ROIA GB Collaboration • Start with high confidence (proportional to I(C,R)) • Give more weight to reported confidence ratings (Proportional to I(C,R)).

  30. VEB VEA ROIA GB 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)).

  31. VEA VEB ROIA VEC ROIA GB ROIB GC 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.

  32. 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

  33. 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)

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