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
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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
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• 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
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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
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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
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Reputation Graph
• Algorithm 1: Graph Construction
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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