1 / 33

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.

clive
Download Presentation

Trust Analysis through Relationship Identification

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  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)

More Related