A Quantitative Trust Model for Negotiating Agents A Quantitative Trust Model for Negotiating Agents Jamal Bentahar, John Jules Ch. Meyer Concordia University (Canada) Utrecht University (the Netherlands) Imperial 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 Private states: Beliefs, Desires, Intentions, etc. Social Approach Public states: Social commitments 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 Reasoning Capabilities Quantitative Probabilistic-based Decentralized Approach

6 Overview Problem and Motivations Negotiation Framework Trustworthiness Model Implementation Related Work and Conclusion

7 Agent Architecture

8 Negotiation Framework Agent 1 Agent 2 Social Commitments + Argumentation Speech Act Theory + Action Logic Negotiation Specification Reasoning + Semantics

9 Negotiation Framework Argumentation Theory Agent Negotiation Support FlexibilityEfficiency Dialogue Games Relevance Theory 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_Ag 1 Action_Ag 2 Cond

12 Agent Communication Action_Ag i  {Make-Offer, Make-Counter- Offer, Withdraw, Satisfy, Violate, Accept, Refuse, challenge, Justify, Defend, Attack} Argumentation system Communicative Actions Supports

13 The notion of argument: a pair 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 (P 1, c 1 ) attacks another argument (P 2, c 2 ) iff c 1 c 2 or x P 2 | c 1 x Argumentation

15 Overview Problem and Motivations Negotiation Framework Trustworthiness Model Implementation Related Work and Conclusion

16 Probability function: Rep : A  A  D  [0, 1] Local beliefs Global beliefs: testimonies of witnesses Foundation

17 Illustration

18 Central Limit Theorem and the Law of Large Numbers If M > w Then Return True Else Return False Assessing Agent’s Reputation

19 Timely Relevance Function

20 Reputation Graph Algorithm 1: Graph Construction

21 Algorithm2: Node Evaluation

22 Complexity Construction of the trust graph with n nodes and a edges n recursive calls of the function Evaluate-Node (Ag y ) 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:

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

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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 Negotiating Agents A Quantitative Trust Model for Negotiating Agents Jamal Bentahar, John Jules Ch. Meyer Concordia University (Canada) Utrecht University (the Netherlands) Imperial College London, June 08, 2007