1. Trust Analysis through Relationship Identification Ronald Ashri 1, Sarvapali D. Ramchurn 1, Jordi Sabater 2, Michael Luck 1 and Nick Jennings 1 1.Intelligence, Agents, Multimedia, University of Southampton 2.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 Identification Foundational 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 Identification Agents  An agent is described by Attributes – budget,organisation,products Actions – selling,buying products Goals (G) – acquiring information, obtaining a product

12. Relationship Identification Viewable 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 Identification Region 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 Identification Agent Interaction Model Agent A Environment viewable environment region of influence

15. Relationship Identification Agent Interaction Model Agent A Environment viewable environment region of influence Agent B viewable environment 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. VE B Trade-Dep VE A ROI A GBGB

20. VE B Comp-Sell VE A ROI A ROI B

21. VE B Comp-Buy VE A G BA

22. VE B Collaboration VE A ROI A GBGB ROI B GAGA

23. Tripartite Relationships VE C VE B VE A ROI B GCGC ROI A GBGB

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

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. Competition  Give low starting confidence  Give low weights to trust reported by those agents VE B VE A ROI A ROI B VE B VE A G BA

29. Collaboration  Start with high confidence (proportional to I(C,R))  Give more weight to reported confidence ratings (Proportional to I(C,R)). VE B VE A ROI A GBGB ROI B GAGA

30. 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)). VE B VE A ROI A GBGB

31. 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. VE C VE B VE A ROI A ROI B GCGC ROI A GBGB

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""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,Artificial Intelligence Review September 2005, pp. 33-60(28)