Adaptiveness and Social-Compliance in Trust Management. A Multi-Agent Approach
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Phd Thesis defense of Reda Yaich.
![Introduction
Research Scope
TMS
A-TMS
ASC-TMS
Evaluation
Conclusion
Open and Decentralised Virtual Communities
Dave
Helen
Bob
H
D
A
G
B
George
Alice
E
C
Carl
F
Elise
f
Frank
A group of people with a common purpose whose interactions are
mediated and supported by computer platforms” [Preece, 2004]
2](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Adaptiveness and Social-Compliance in Trust Management. A Multi-Agent Approach
- 1. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Adaptiveness and Social-Compliance in Trust Management – a Multi-Agent Based Approach Reda Yaich ISCOD Institut Henri Fayol Ecole des Mines Saint-Etienne 29 October 2013 1
- 2. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Open and Decentralised Virtual Communities Dave Helen Bob H D A G B George Alice E C Carl F Elise f Frank A group of people with a common purpose whose interactions are mediated and supported by computer platforms” [Preece, 2004] 2
- 3. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Trust in Virtual Communities In virtual communities, decisions made by members are risky and uncertain Who can access my resources? Who can join my community? Does security help? Resources and actors should be known ! 3
- 4. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Trust in Virtual Communities In virtual communities, decisions made by members are risky and uncertain Who can access my resources? Who can join my community? Does security help? Resources and actors should be known ! How much credit can I assign to the partner? Who is the best partner I can interact with? 3
- 5. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Trust in Virtual Communities In virtual communities, decisions made by members are risky and uncertain Who can access my resources? Who can join my community? Does security help? Resources and actors should be known ! How much credit can I assign to the partner? Who is the best partner I can interact with? Trust Trust enables people to make decisions in complex environments based on positive expectations [Luhmann, 1990] 3
- 6. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Trust in Virtual Communities In virtual communities, decisions made by members are risky and uncertain Who can access my resources? Who can join my community? Does security help? Resources and actors should be known ! How much credit can I assign to the partner? Who is the best partner I can interact with? Trust Trust enables people to make decisions in complex environments based on positive expectations [Luhmann, 1990] 3
- 7. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Objectives Design a system that assists members of open and decentralised virtual communities in their trust decisions Challenging Properties Openness: people can join and leave communities at will Dynamics: ever-evolving context Social-Compliance: self-interests vs. collective objectives Decentralization: no central authority 4
- 8. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Objectives Design a system that assists members of open and decentralised virtual communities in their trust decisions Challenging Properties Openness: people can join and leave communities at will Dynamics: ever-evolving context Social-Compliance: self-interests vs. collective objectives Decentralization: no central authority 4
- 9. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation 1 Introduction 2 Research Scope Trust Management Landscape Objectives 3 Trust Management System (TMS) 4 Adaptive TMS (A-TMS) 5 Adaptive and Socially-Compliant TMS (ASC-TMS) 6 Evaluation 7 Conclusion Conclusion 5
- 10. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Soft Trust Approaches Trust in Computer Science Social Relations Reputation ReGret Hard Trust Approaches LIAR FIRE STM Computational Trust Trust Model ForTrust MAS Social Trust 1970 hybrid Reliability PGP (WoT) Rei Social Trust Deontic Trust Management ACL X.509 (PKI) 1990 CTM PROTUNE Ponder Policy Maker 2000 RT Negotiation Roles XACML 1.0 Trust Builder Attributes 2005 IBM TE ATNAC XACML 2.0 XACML 3.0 2008 2012 6
- 11. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Hard vs Soft Trust Approaches How the challenging properties have been addressed? Openness Dynamics Social-Compliance Decentralization Hard Trust Attributes Policies Integration Delegation Soft Trust Experience Context-Awareness Social Control/Norms Multi-Agent 7
- 12. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Research Objectives Assist virtual community members in their trust decisions taking into account: Openness 8
- 13. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Research Objectives Assist virtual community members in their trust decisions taking into account: Openness Dynamics Trust Factors Trust Policy 8
- 14. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Research Objectives Assist virtual community members in their trust decisions taking into account: Openness Dynamics Social-Compliance Adaptiveness Individual Policy Individual Policy Adaptation to the Adaptation to the Environment Partner Trust Factors Trust Policy 8
- 15. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Research Objectives Assist virtual community members in their trust decisions taking into account: Openness Dynamics Social-Compliance Decentralization Social-Compliance Individual Policy Adaptation to the Collective Collective Policy Adaptation to the Individual Adaptiveness Individual Policy Individual Policy Adaptation to the Adaptation to the Environment Partner Trust Factors Trust Policy 8
- 16. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Research Objectives Assist virtual community members in their trust decisions taking into account: Openness Dynamics Social-Compliance Decentralization Multi-Agent Based Trust Management System Social-Compliance Individual Policy Adaptation to the Collective Collective Policy Adaptation to the Individual Adaptiveness Individual Policy Individual Policy Adaptation to the Adaptation to the Environment Partner Trust Factors Trust Policy 8
- 17. Introduction Research Scope TMS 1 Adaptive TMS (A-TMS) 5 Adaptive and Socially-Compliant TMS (ASC-TMS) Conclusion Trust Management System (TMS) Trust Factors Ontology Flexible Policy Language 4 Evaluation Research Scope 3 ASC-TMS Introduction 2 A-TMS 6 Trust Policy Evaluation 7 Trust Factors Conclusion 9
- 18. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Trust Factors Ontology (TFO) ∆f A hybrid trust management approach Trust Factor 10
- 19. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Trust Factors Ontology (TFO) ∆f A hybrid trust management approach Trust Factor Proof Indicator Subsumption Disjonction 10
- 20. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Trust Factors Ontology (TFO) ∆f A hybrid trust management approach Trust Factor Reliability Proof Indicator Selfishness Experience Reputation Subsumption Disjonction 10
- 21. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Trust Factors Ontology (TFO) ∆f A hybrid trust management approach Trust Factor Reliability Experience Competences Proof Indicator Selfishness Experience Membership Degree Reputation Subsumption Disjonction 10
- 22. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Trust Factors Ontology (TFO) ∆f A hybrid trust management approach Trust Factor Reliability Experience Proof Competences Indicator Selfishness Experience Membership Degree Reputation Prof. Bachelor Is A Master PhD Subsumption Disjonction Licence Engineer 10
- 23. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Trust Factors Ontology (TFO) ∆f A hybrid trust management approach Trust Factor Reliability Experience Proof Competences Indicator Selfishness Experience Membership Degree Reputation Prof. Bachelor Is A Master PhD Subsumption Disjonction Licence Higher Lower Engineer 10
- 24. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Trust Factors Ontology (TFO) ∆f A hybrid trust management approach Trust Factor Reliability Experience Proof Competences Indicator Selfishness Experience Membership Degree Reputation Prof. Bachelor Is A Master PhD Subsumption Disjonction Licence Higher Lower Engineer Equivalent 10
- 25. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Policy Language A policy is defined by a set of trust criteria πPattern = { f1 , o1 , v1 , w1 , t1 , ..., fn , on , vn , wn , tn } Issuer Where : fi is the trust factor name (f ∈ ∆f .T ) oi is a comparison operator from (oi ∈ {>, <, ≤, ≥, , =}) vi is a threshold value (vi ∈ ∆f .A ) wi is a weight value (wi ∈ Z) ti ∈ {’m’, ’o’} specifies if the criterion is mandatory or not Bob’s policy for the pattern access , notes access πbob ,notes = { identity , ≥, marginal , 2, m , age , >, 18, 2, m , age , <, 30, 2, m , reputation, ≥, 60%, 2, o , recommendation, ≤, 2, 1, o } 11
- 26. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Policy Evaluation The evaluation E(πx , ψb ) of: a The policy πx = { f1 , op1 , v1 , w1 , t1 , . . . , fn , opn , vn , wn , tn } a With respect to the profile ψb = q, b , { f1 , v1 , . . . , fm , vm } n i=1,j=1 E ( fi ,opi ,vi ,wi ,ti , fj ,vj ) n x b i =1 wi E(πa , ψ ) = 0 if a mandatory criterion is not satisfied where: wi if fi = fj and fi opi fj E ( fi , opi , vi , wi , ti , fj , vj ) = 0, otherwise (1) 12
- 27. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Illustration of Policy Evaluation Policy of the controller tc(identity, ≥, marginal, 2, m) tc(age, >, 18, 2, m) tc(age, <, 30, 2, m) tc(reputation, ≥, 70, 2, o) tc(recommendation, ≥, 3, 1, o) Profile of the requester credential(identity, alice, complete) credential(age, alice, 25) declaration(reputation, alice, 50) Policy Evaluation declaration(recommendation, alice, 0) 2+2+2+0+0 = 0.66 9 13
- 28. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Illustration of Policy Evaluation Policy of the controller tc(identity, ≥, marginal, 2, m) tc(age, >, 18, 2, m) tc(age, <, 30, 2, m) tc(reputation, ≥, 70, 2, o) tc(recommendation, ≥, 3, 1, o) Profile of the requester credential(identity, alice, unknown) credential(age, alice, 25) declaration(reputation, alice, 75) Policy Evaluation declaration(recommendation, alice, 0) 0 13
- 29. Introduction Research Scope TMS 1 Adaptive TMS (A-TMS) Individual to Environment Individual To Individual Conclusion Trust Management System (TMS) 4 Evaluation Research Scope 3 ASC-TMS Introduction 2 A-TMS 5 Adaptive and Socially-Compliant TMS (ASC-TMS) 6 Trust Factors Trust Policy Evaluation 7 Adaptiveness Individual Policy Individual Policy Adaptation to the Adaptation to the Environment Partner Conclusion 14
- 30. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Policies Obsolescence Dave Resources - Availability - Values - sensitivity Helen Bob H D A Risks - Credentials falsification - Id usurpation - Reputation collusion G B George Alice E C F ? I Trust Factors - Availability - Pertinence Carl Elise f Interaction outcome Frank Policies specification context current context 15
- 31. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Adaptation Meta-Policies Adaptive Trust Negotiation and Access Control [Ryutov et al., 2005] Extension of the policy language with Adaptation meta-policies. When policies should be adapted How they can be adapted Meta-policies Event : Condition ← Actions 16
- 32. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Adaptation Meta-Policies Adaptive Trust Negotiation and Access Control [Ryutov et al., 2005] Extension of the policy language with Adaptation meta-policies. When policies should be adapted How they can be adapted Meta-policies Event : Condition ← Actions Actions include (but not limited to) adaptation operators AddCriterion(π, tci ) DelCriterion(π, fi ) UpdateCriterion(π, fi ) RelaxCriterion(π, fi ) RestrictCriterion(π, fi ) LowerCriterion(π, fi ) HigherCriterion(π, fi ) 16
- 33. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Adaptation Meta-Policies Adaptive Trust Negotiation and Access Control [Ryutov et al., 2005] Extension of the policy language with Adaptation meta-policies. When policies should be adapted How they can be adapted Meta-policies Event : Condition ← Actions Actions include (but not limited to) adaptation operators AddCriterion(π, tci ) DelCriterion(π, fi ) UpdateCriterion(π, fi ) RelaxCriterion(π, fi ) RestrictCriterion(π, fi ) LowerCriterion(π, fi ) HigherCriterion(π, fi ) 16
- 34. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Illustration of adaptation to environment Adaptation to resource value _,file Instantiate (πBob , R ) :R .value t > R .value t −1 ∨ R .sensitivity t > R .sensitivity t −1 ← _,file RestrictCriterion(πBob , reputation), _,file RestrictCriterion(πBob , recommendation) Initial Policy read πbob ,file ={ identity , ≥, marginal , 2, m , age , >, 18, 2, m , age , <, 30, 2, o , reputation, ≥, 50%, 3, o , recommendation, >, 2, 1, o } 17
- 35. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Illustration of adaptation to environment Adaptation to resource value _,file Instantiate (πBob , R ) :R .value t > R .value t −1 ∨ R .sensitivity t > R .sensitivity t −1 ← _,file RestrictCriterion(πBob , reputation), _,file RestrictCriterion(πBob , recommendation) Adapted Policy read πbob ,file ={ identity , ≥, marginal , 2, m , age , >, 18, 2, m , age , <, 30, 2, o , reputation, ≥, 60%, 3, o , recommendation, >, 3, 1, o } 17
- 36. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Adaptation of the Individual to the Partner Multi-Agent Based Trust Management System Social-Compliance Individual Policy Adaptation to the Collective Collective Policy Adaptation to the Individual Adaptiveness Individual Policy Individual Policy Adaptation to the Adaptation to the Environment Partner Trust Factors Trust Policy 18
- 37. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Automated Trust Negotiation Credentials =⇒ contain sensitive information Trust Deadlock ! Controller Evaluation =⇒ Credentials Disclosure Requester Trust Builder [Yu et al., 2003, Lee et al., 2009], IBM TE [Herzberg et al., 2000], Trust−X [Bertino et al., 2003], RT [Li et al., 2002] subject S requests action A on resource R Evidence for property X ? Evidence for property Y ? Evidence for property Y Evidence for property X Authorization for S to perform A on R 19
- 38. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion The Adaptive Trust Negotiation Negotiation Strategy Negotiation Protocol 7 b: ACCEPT/REJECT -PROPOSAL a: PROPOSE b: ENDNEGOTIATION a: QUERY-IF a: CONFIRM / DISCONFIRM a: STARTNEGOTIATION b: REQUEST 0 1 b: ACCEPNEGOTIATION 2 a: QUERY-IF 3 4 b: QUERY-IF 5 b: ENDNEGOTIATION b: REFUSENEGOTIATION a: ENDNEGOTIATION b: CONFIRM/ DISCONFIRM b: PROPOSE a: ACCEPT/REJECT -PROPOSAL 6 a: ENDNEGOTIATION 20
- 39. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion The Adaptive Trust Negotiation Negotiation Strategy Utility Function query-If Controller C2: confirm C3: propose Controller Requester Controller Negotiation Protocol C1: dis- confirm 0, 0 C2.1 : endC2.1:Query-If negotiation (continue negotiation) 1, −1 3, 2 C3.1:refuseproposal C3.2:acceptproposal 0, 0 3, 3 20
- 40. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion The Adaptive Trust Negotiation Controller Negotiation Strategy uc = (Ocontroller + Requester Negotiation Protocol ur = (Orequester + update,r update,r Utility Function (xi .ν)) − (r.ς + r.ν) (xi .ς)) − (yi .ν) 20
- 41. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Illustration Query-If update,r Orequester = 3 r.ν = 2 c1.ν = 1 r.ς = 2 c2.ν = 2 c3.ν = 3 Ocontroller = 3 update,r Propose Confirm Disconfirm 0, 0 Accept Proposal accept 2, 0 Reject Proposal Endnegotiation −3, 3 Confirm 1, 1 Propose End-negotiation 0, 0 Accept Proposal Reject Proposal Confirm Confirm 0, 2 End-negotiation 0, 0 −3, 3 21
- 42. Introduction Research Scope 1 Trust Management System (TMS) ASC-TMS Evaluation Conclusion Research Scope 3 A-TMS Introduction 2 TMS 4 Adaptive TMS (A-TMS) 5 Adaptive and Socially-Compliant TMS (ASC-TMS) Individual to Collective Collective to Individual Multi-Agent Based TMS 6 Individual Policy Adaptation to the Collective Collective Policy Adaptation to the Individual Adaptiveness Individual Policy Individual Policy Adaptation to the Adaptation to the Environment Partner Trust Factors Trust Policy Evaluation 7 Social-Compliance Conclusion 22
- 43. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Adaptation of Individual Policies to Collective ones 23
- 44. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Adaptation of Individual Policies to Collective ones 23
- 45. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Adaptation of Individual Policies to Collective ones 23
- 46. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Integration Mechanism f1 , o1 , v1 , w1 , t1 f1 , o1 , v1 , w1 , t1 f2 , o2 , v2 , w2 , t2 f2 , o2 , v2 , w2 , t2 ......................... ......................... fn , on , vn , wn , tn Ri = Rj fn , on , vn , wn , tn Ri Ri Converge Rj Ri Rj Ri Diverges Rj Rj Ri Rj Integration Ri Extends Rj f1 , o1 , v1 , w1 , t1 f2 , o2 , v2 , w2 , t2 Ri Restricts Rj Ri Rj Ri Suffles Rj / Rj Suffles Ri ......................... fn , on , vn , wn , tn XACML [Humenn, 2003, Cover, 2007], Combination [Rao et al., 2009] and Integration [Rao et al., 2011] 24
- 47. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Policies Integration Heuristics h1 : p is at least as restrictive as the most restrictive policy I’m sure to deny all requests both policies would have denied h2 : p is at most as restrictive as the least restrictive policy I’m sure to accept request that both policies would have accepted h3 : p is at least as restrictive as the selected policy I’m sure to deny all requests me/my community would have denied h4 : p is at most as restrictive as the selected policy I’m sure to accept all requests me/my community would have accepted 25
- 48. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Illustration of Individual Adaptation to the Collective (identity, ≥, f air, 3, m) (reputation, ≥, 70, 2, o) (identity, ≥, marginal, 1, o) (reputation, ≥, 75, 2, o) (recommendation, ≥, 2, 3, o) Individual Policy Collective Policy Integration (identity, ≥, marginal, 4, m) (reputation, ≥, 75, 4, o) (recommendation, ≥, 2, 3, o) 26
- 49. Introduction Research Scope 1 Trust Management System (TMS) ASC-TMS Evaluation Conclusion Research Scope 3 A-TMS Introduction 2 TMS 4 Adaptive TMS (A-TMS) 5 Adaptive and Socially-Compliant TMS (ASC-TMS) Individual to Collective Collective to Individual Multi-Agent Based TMS 6 Individual Policy Adaptation to the Collective Collective Policy Adaptation to the Individual Adaptiveness Individual Policy Individual Policy Adaptation to the Adaptation to the Environment Partner Trust Factors Trust Policy Evaluation 7 Social-Compliance Conclusion 27
- 50. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Building Collective Policies from Individual Ones 28
- 51. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Building Collective Policies from Individual Ones 28
- 52. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Combination Mechanism f1 , o1 , v1 , w1 , t1 f2 , o2 , v2 , w2 , t2 ......................... fn , on , vn , wn , tn f1 , o1 , v1 , w1 , t1 f1 , o1 , v1 , w1 , t1 f2 , o2 , v2 , w2 , t2 f2 , o2 , v2 , w2 , t2 ......................... fn , on , vn , wn , tn Combination driven by heuristics ......................... Combination f1 , o1 , v1 , w1 , t1 fn , on , vn , wn , tn h1 : Selects the most restrictive criterion each time h2 : Selects the least restrictive criterion each time f2 , o2 , v2 , w2 , t2 ......................... fn , on , vn , wn , tn 29
- 53. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Coordination for Policies Combination 30
- 54. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Coordination for Policies Combination 1 Check if the collective policy exists 30
- 55. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Coordination for Policies Combination 1 Check if the collective policy exists 2 Broadcast the call 30
- 56. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Coordination for Policies Combination 1 Check if the collective policy exists 2 Broadcast the call 3 Policies are selected 30
- 57. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Coordination for Policies Combination 1 Check if the collective policy exists 2 Broadcast the call 3 Policies are selected 4 Policies are exchanged 30
- 58. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Coordination for Policies Combination 1 Check if the collective policy exists 2 Broadcast the call 3 Policies are selected 4 Policies are exchanged 5 Policies are combined 30
- 59. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Coordination for Policies Combination 1 Check if the collective policy exists 2 Broadcast the call 3 Policies are selected 4 Policies are exchanged 5 Policies are combined 6 The collective policy is generated 30
- 60. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Coordination for Policies Combination 1 Check if the collective policy exists 2 Broadcast the call 3 Policies are selected 4 Policies are exchanged 5 Policies are combined 6 The collective policy is generated 30
- 61. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Illustration of Individual Policies Combination tc(identity, ≥, f air, 3, 0) tc(skilf ulness, ≥, f air, 2, o) tc(reputation, ≥, 75, 3, o) tc(identity, ≥, marginal, 5, m) tc(skillf ulness, ≥, f air, 1, o) tc(reputation, ≥, 70, 2, o) tc(recommendation, ≥, 3, 1, o) tc(skilf ulness, ≥, f air, 2, o) tc(reputation, ≥, 65, 1, o) tc(recommendation, ≥, 2, 2, o) Individual Policy Individual Policy Individual Policy Combination tc(identity, ≥, marginal, 8, m) tc(skilf ulness, ≥, f air, 5, o) tc(reputation, ≥, 75, 6, o) tc(recommendation, ≥, 3, 3, o) h1 : Selects the most restrictive criterion each time 31
- 62. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Collective Policies Obsolescence 32
- 63. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Collective Policies Evolution Meta-policies Event : Condition ← Actions Actions include (but not limited to) adaptation operators AddCriterion(π, tci ) DelCriterion(π, fi ) UpdateCriterion(π, fi ) RelaxCriterion(π, fi ) RestrictCriterion(π, fi ) LowerCriterion(π, fi ) HigherCriterion(π, fi ) 33
- 64. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Coordination for Collective Policies Evolution 1 Detection of Policy Obsolescence 2 Call for evolution 34
- 65. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Coordination for Collective Policies Evolution 1 Detection of Policy Obsolescence 2 Call for evolution 3 Vote for adaptation 34
- 66. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Coordination for Collective Policies Evolution 1 Detection of Policy Obsolescence 2 Call for evolution 3 Vote for adaptation 4 Compute votes 34
- 67. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Coordination for Collective Policies Evolution 1 Detection of Policy Obsolescence 2 Call for evolution 3 Vote for adaptation 4 Compute votes 5 Adapt the policy if majority 34
- 68. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Coordination for Collective Policies Evolution 1 Detection of Policy Obsolescence 2 Call for evolution 3 Vote for adaptation 4 Compute votes 5 Adapt the policy if majority 34
- 69. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Synthesis on Social Compliance → Extension of the policy language with Adaptation meta-policies Meta-policies Event : Condition ← Actions Actions includes context-awareness and social-awareness operators RelaxCriterion(π, fi ) Integrate(π1 , π2 , ih ) ... Combine(Π , c , ch , π ) → Definition of coordination protocols 35
- 70. Introduction Research Scope 1 Trust Management System (TMS) ASC-TMS Evaluation Conclusion Research Scope 3 A-TMS Introduction 2 TMS 4 Adaptive TMS (A-TMS) 5 Adaptive and Socially-Compliant TMS (ASC-TMS) Individual to Collective Collective to Individual Multi-Agent Based TMS 6 Social-Compliance Individual Policy Adaptation to the Collective Collective Policy Adaptation to the Individual Adaptiveness Individual Policy Individual Policy Adaptation to the Adaptation to the Environment Partner Trust Factors Trust Policy Evaluation 7 Multi-Agent Based Trust Management System Conclusion 36
- 71. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Multi-Agent Based Trust Management System Organisation Community Role Collective Policies A Assistant Agent A A Interaction ASC-TMS T A T T Agents T Association A A A Individual Policies Adhesion Negotiation T T Public Resource Environment Private Resource Control T Operation Interactions Decentralized Trust Management 37
- 72. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Multi-Agent Based Trust Management System Organisation Community Role Collective Policies A Assistant Agent A A Interaction ASC-TMS T A T T Agents T Association A A A Individual Policies Adhesion Negotiation T T Public Resource Environment Private Resource Control T Operation Interactions Decentralized Trust Management 37
- 73. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Multi-Agent Based Trust Management System Organisation Community Role Collective Policies A Assistant Agent A A Interaction ASC-TMS T A T T Agents T Association A A A Individual Policies Adhesion Negotiation T T Environment Private Resource Public Resource Decentralized Trust Management Control T Operation Interactions Coordination Voting/Negotiation Protocols 37
- 74. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Multi-Agent Based Trust Management System Organisation Community Role Collective Policies A Assistant Agent A A Interaction ASC-TMS T A T T Agents T Association A A A Individual Policies Adhesion Negotiation T T Environment Private Resource Public Resource Decentralized Trust Management Control T Coordination Voting/Negotiation Protocols Operation Interactions Norms Organisations 37
- 75. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Multi-Agent Based Trust Management System Organisation Community Role Collective Policies A Assistant Agent A A Interaction ASC-TMS T A T T Agents T Association A A A Individual Policies Adhesion Negotiation T T Environment Private Resource Public Resource Decentralized Trust Management Control T Coordination Voting/Negotiation Protocols Operation Interactions Norms Organisations 37
- 76. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation 1 Introduction 2 Research Scope 3 Trust Management System (TMS) 4 Adaptive TMS (A-TMS) 5 Adaptive and Socially-Compliant TMS (ASC-TMS) 6 Evaluation Implementation Repast Simulation Results 7 Conclusion Conclusion 38
- 77. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Implementations Demonstrate the applicability of ASC-TMS Deploy the model on the JaCaMo Platform [Boissier et al., 2011] Use of ASC-TMS in Open Innovation Community Application Extension of the model for mobiles (JaCaAndroid) Evaluate ASC-TMS Implementation on Repast Simulation Platform [Collier, 2003] Run the model on large scale populations Observe the benefit of ASC-TMS Evaluate the impact of ASC-TMS on communities dynamics 39
- 78. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Evaluation Objective Study the benefit of using social compliance in trust management within virtual communities Impact of combination on communities dynamics Impact of social-compliance on communities dynamics Correlation between social-compliance and communities dynamics Impact of evolution on communities dynamics 40
- 79. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Experimental Settings Use case: Communities for Open innovation challenges Challenges Objectives: 10 000 resource units Deadline: 1000 steps Reward: 1000 $ Rules: Non Compliant members are ejected from their community Empty communities are destroyed (collapse) 41
- 80. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Experimental Settings Use case: Communities for Open innovation challenges Challenges Objectives: 10 000 resource units Deadline: 1000 steps Reward: 1000 $ Rules: Non Compliant members are ejected from their community Empty communities are destroyed (collapse) Simulation Metrics Number of communities Population of each community 41
- 81. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Experimental Settings Use case: Communities for Open innovation challenges Challenges Objectives: 10 000 resource units Deadline: 1000 steps Reward: 1000 $ Rules: Non Compliant members are ejected from their community Empty communities are destroyed (collapse) Simulation Metrics Number of communities Population of each community 41
- 82. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Parameters Agents Policies and Credentials are randomly generated Collaborativeness: uniform distribution ([0,1]) Competence: normal distribution ([0,1]) Interaction: probability of 0.8 Different populations in terms of social-compliance (With/Without) Combination With a probability (0/0.5/0.8/1) of Integration (With/Without) Evolution 42
- 83. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Impact of combination on communities dynamics 25 35 30 Average Population Size Number of Communities 20 15 10 5 25 20 15 10 5 0 0 No Combination - No Integration - No Evolution Combination - No Integration - No Evolution No Combination - No Integration - No Evolution Combination - No Integration - No Evolution -5 -5 0 2000 4000 6000 8000 Simulation Step 10000 12000 0 2000 4000 6000 8000 10000 12000 Simulation Step 43
- 84. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Impact of integration on communities dynamics 35 40 35 30 Average Population Size Number of Communities 30 25 20 15 10 5 25 20 15 10 5 0 0 No Combination - No Integration - No Evolution Combination - No Integration - No Evolution Combination - 1.0 Integration - No Evolution -5 No Combination - No Integration - No Evolution Combination - No Integration - No Evolution Combination - 1.0 Integration - No Evolution -5 0 2000 4000 6000 8000 Simulation Step 10000 12000 0 2000 4000 6000 8000 10000 12000 Simulation Step 44
- 85. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Compliance and Communities Dynamics Correlation 35 40 35 30 Average Population Size Number of Communities 30 25 20 15 10 5 25 20 15 10 5 No Combination - No Integration - No Evolution Combination - No Integration - No Evolution Combination - 0.5 Integration - No Evolution Combination - 0.8 Integration - No Evolution Combination - 1.0 Integration - No Evolution 0 No Combination - No Integration - No Evolution Combination - No Integration - No Evolution Combination - 0.5 Integration - No Evolution Combination - 0.8 Integration - No Evolution Combination - 1.0 Integration - No Evolution 0 -5 -5 0 2000 4000 6000 8000 Simulation Step 10000 12000 0 2000 4000 6000 8000 10000 12000 Simulation Step 45
- 86. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Impact of Evolution on Communities Dynamics 35 30 30 Average Population Size 40 35 Number of Communities 40 25 20 15 10 5 25 20 15 10 5 0 0 Combination - 1.0 Integration - No Evolution Combination - 0.8 Integration - Evolution Combination - 1.0 Integration - Evolution -5 Combination - 1.0 Integration - No Evolution Combination - 0.8 Integration - Evolution Combination - 1.0 Integration - Evolution -5 0 2000 4000 6000 8000 Simulation Step 10000 12000 0 2000 4000 6000 8000 10000 12000 Simulation Step 46
- 87. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Results Synthesis Without integration (i.e. adaptation of individual trust policies to collective ones), disappearing of communities is more frequent Social compliance helps communities to work better Combination and evolution are important mechanisms to help agent to maintain communities even if non social compliant members exist (up to 20%) 47
- 88. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Contributions ASC-TMS is a new Hybrid Trust Approach combining Hard and Soft Trust Approaches ASC-TMS proposes a rich, expressive and flexible policy language addressing both individual and collective dimensions ASC-TMS addresses both individual and collective Trust Management and Adaptation ASC-TMS bridges the gap between Social Science, Trust Management and Distributed Artificial Intelligence 48
- 89. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Future Works Extend and enrich the evaluation of ASC-TMS (w.r.t, Populations, Heuristics, Coordination) Confront ASC-TMS to Social Science Theories and Existing Trust Models Enrich the expressiveness of the ASC-TMS policy language Extend the adaptation mechanisms at the individual and collective levels with learning capabilities to learn from past experiences Apply the adaptation mechanism to the evolution of the Trust Factors Ontology 49
- 90. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Related Publications Yaich, R., Boissier, O., Picard, G., and Jaillon, P. (2013). Adaptiveness and social-compliance in trust management within virtual communities. Web Intelligence and Agent Systems (WIAS), Special Issue: Web Intelligence and Communities (to appear). Yaich, R., Boissier, O., Picard, G, and Jaillon, P. (2012). An agent based trust management system for multi-agent based virtual communities. In Demazeau, Y., Müller, J. P., Rodríguez, J. M. C., and Pérez, J. B., editors, Advances on Practical Applications of Agents and Multiagent Systems, Proc. of the 10th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS 12), volume 155 ofAdvances in Soft Computing Series, pages 217-223. Springer. Yaich, R., Boissier, O., Jaillon, P., and Picard, G. (2012). An adaptive and socially-compliant trust management system for virtual communities. InThe 27th ACM Symposium On Applied Computing (SAC 2012), pages 2022-2028. ACM Press. Yaich, R., Boissier, O., Picard, G., and Jaillon, P. (2011). Social-compliance in trust management within virtual communities. In European Workshop on Multi-agent Systems (EUMAS’11). Yaich, R., Boissier, O., Jaillon, P., and Picard, G. (2011). Social-compliance in trust management within virtual communities. In 3rd International Workshop on Web Intelligence and Communities (WIC’11) at the International Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT 2011), pages 322-325. IEEE Computer Society. ´ Yaich, R., Jaillon, P., Boissier, O., and Picard, G. (2011). Gestion de la confiance et intgration des exigences sociales au sein de communautés virtuelles. In 19es Journées francophones des systèmes multi-agents (JFSMA’11), pages 213-222. Cépaduès. Yaich, R., Jaillon, P., Picard, G., and Boissier, O. (2010). Toward an adaptive trust policy model for open and decentralized virtual communities. InWorkshop on Trust and Reputation. Interdisciplines. 50
- 91. Introduction Research Scope TMS A-TMS ASC-TMS Evaluation Conclusion Thank You for Your Attention ! Questions ? 51
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