A Quantitative Trust Model for A Quantitative Trust Model for Negotiating Negotiating AgentsAgents

Jamal Bentahar, John Jules Ch. MeyerConcordia University (Canada)

Utrecht University (the Netherlands)

Imperial College London, June 08, 2007Imperial College London, June 08, 2007

2

Overview

• Problem and Motivations

• Negotiation Framework

• Trustworthiness Model

• Implementation

• Related Work and Conclusion

3

Context and Problem

• Multi-agent Systems: interacting autonomous agents

• Communication Protocols: specifying allowed communicative acts

• Open and dynamic MAS need flexible protocols: logic-based

dialogue games

• Example: negotiation dialogue games

• Security engineering: a new challenge in agent-based software

engineering

• Distributed setting: e.g. semantic-grid computing

• Computational efficiency

4

Proposed Approaches for Interacting Agents

Mental Approach

Mental Approach

Private states: Beliefs, Desires, Intentions, etc.

Social Approach

Social Approach

Public states: Social

commitments

Argumentative Approach

Argumentative Approach

Argumentation theory +

reasoning

Allen and Perrault, 1980

Cohen and Levesque, 1990

and others

Singh, 2000

Colombetti, 2000

and others

Amgoud and Maudet, 1999

McBurney et al., 2002

and others

5

Motivations

• How to trust negotiating agents within a multi-agent

system:

• Resources sharing and mutual access

Centralized Approaches

Vulnerable to attacks

Vulnerable to attacks Reasoning

CapabilitiesReasoning Capabilities

QuantitativeProbabilistic-based

QuantitativeProbabilistic-based

Decentralized Approach

6

Overview

Problem and Motivations

• Negotiation Framework

• Trustworthiness Model

• Implementation

• Related Work and Conclusion

7

Agent Architecture

8

Negotiation Framework

Agent 1Agent 1 Agent 2Agent 2

Social Commitments

+

Argumentation

Social Commitments

+

Argumentation

Speech Act Theory + Action Logic

Speech Act Theory + Action Logic

Negotiation

SpecificationSpecification

Reasoning + SemanticsReasoning + Semantics

9

Negotiation Framework

Argumentation Theory

Agent Negotiation

SupportSupport

FlexibilityFlexibility EfficiencyEfficiency

Dialogue Games

Dialogue Games

RelevanceTheory

RelevanceTheory

Logic-based

Reasoning

Logic-based

Reasoning

10

Dialogue Games

• Abstract structures that can be composed:• Sequencing:

• Embedding:

• Parallelization:

• Argumentation-driven decision making process

Game 1 Game 2,

Game1

Game 2… …

Game 1 Game 2//

11

Dialogue Games: Specification

• Initiative / reactive dialogue games

• A simple language

• Cond: generating arguments from the agent’s argumentation system

Action_Ag1 Action_Ag2

Cond

12

Agent Communication

• Action_Agi {Make-Offer, Make-Counter-

Offer, Withdraw, Satisfy, Violate, Accept,

Refuse, challenge, Justify, Defend, Attack}

Argumentation system

Argumentation system

Communicative Actions

Communicative ActionsSupportsSupports

13

• The notion of argument:

a pair <Premises, Conclusion>

• An argument is a pair (P, c) where P is a

set of beliefs and c is a formula, such

that:

i) P is consistent, ii) P c et iii) P is minimal

Argumentation

14

• Attack relation: binary relation between

arguments

• An argument (P1, c1) attacks another

argument (P2, c2) iff

• c1 c2 or x P2 | c1 x

Argumentation

15

Overview

Problem and Motivations

Negotiation Framework

• Trustworthiness Model

• Implementation

• Related Work and Conclusion

16

• Probability function:• Rep : AAD [0, 1]

• Local beliefs • Global beliefs: testimonies of witnesses

_ ( ) _ ( ) ( )

_ _ ( ) _ _ ( )a a

aa a

Ag Agb bAgb

Ag Agb b

Nb Arg Nb CAg AgRep Ag

T Nb Arg T Nb CAg Ag

Foundation

17

Illustration

18

• Central Limit Theorem and the Law of Large Numbers

• If M > w Then Return True

Else Return False

1

1

( ) ( ) ( ) ( )

( ) ( ) ( )a b b i

b ba

nAg Ag Ag Agi i i bi

nAg Agi i ii Ag

Rep N TR RepAg Ag Ag AgM

Rep N TRAg Ag Ag

Assessing Agent’s Reputation

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Timely Relevance Function

ln( )( )

AgbAgb i

i

tAgAgTR t e

20

Reputation Graph

• Algorithm 1: Graph Construction

21

Algorithm2: Node Evaluation

Evaluate-Node(Agy) { Arc(Agx, Agy)

If Node(Agx) is note evaluated Then Evaluate-Node(Agx)

m1 := 0, m2 := 0 Arc(Agx, Agy) {

m1 = m1 + Weight(Node(Agx)) * Weight(Arc(Agx, Agy)) m2 = m2 + Weight(Node(Agx))

} Weight(Node(Agy)) = m1 / m2

}

Algorithm 2

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Complexity

• Construction of the trust graph with n nodes and a

edges

• n recursive calls of the function Evaluate-Node (Agy)

• Each node is visited once:

• Assessing the weight of a node

• Using the weight of its neighbors and input edges:

• Run time of the reputation algorithm:

( )O n

( )O a

(max( , ))O a n

23

Overview

Problem and Motivations

Negotiation Framework

Trustworthiness Model

• Implementation

• Related Work and Conclusion

24

System Architecture

• The system is designed as a society of

interacting agents

• Agents are equipped with knowledge bases and

argumentation systems

• Knowledge bases contain propositional formulae

and arguments

• Platform: Jack Intelligent Agents + Java

25

System Architecture

26

Architecture of Negotiating Agent

27

28

29

Overview

Problem and Motivations

Negotiation Framework

Trustworthiness Model

Implementation

• Related Work and Conclusion

30

Related Work

• Two approach types into trusting multi-agent systems: centralized

and decentralized

• Centralized approaches: e.g. eBay and Amazon Auctions

• The ratings are stored centrally and summed up to give an overall rating

• Reputation is a global single value

• The model can be unreliable, particularly when some buyers do not

return ratings

• These models are not suitable for applications in open MAS such as

agent negotiation

31

Related Work

• Three main decentralized approaches:

• Building on agents’ direct experiences of

interaction partners

• Using information provided by other agents

• Certified information provided by referees

32

Related Work

• Regret:

• Direct trust: weighted means of all ratings

• Referral:

• Direct trust

• Trust network

33

Related Work

• Fire:

• Direct interaction trust

• Role-based trust

• Witness reputation

• Certified reputation

34

Conclusion

• Proposition and implementation of a probabilistic

model to secure negotiating autonomous agents

• Formal and efficient computational framework for

secure argumentation-based agents in multi-agent

settings

• Tacking into account the reputation of confidence agents

• Considering the timely relevance of the transmitted

information

35

Future Work

• Reducing the complexity of argumentation-based

reasoning for agent-oriented systems

• Propositional logic vs. Horn logic

• Evaluate the model using concrete scenarios in e-

business settings

• A general framework for secure and verifiable grid-

computing-based applications with the underlying

formal semantics

A Quantitative Trust Model for A Quantitative Trust Model for Negotiating Negotiating AgentsAgents

Jamal Bentahar, John Jules Ch. MeyerConcordia University (Canada)

Utrecht University (the Netherlands)

Imperial College London, June 08, 2007Imperial College London, June 08, 2007