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Annual Conference of ITA ACITA 2009. Agent Assistance in Forming Swift Trust in Ad-Hoc Decision-Making Teams. Katia Sycara Chris Burnett Timothy J. Norman Problem

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Annual Conference of ITA

ACITA 2009

Agent Assistance in Forming Swift Trust in

Ad-Hoc Decision-Making Teams

Katia Sycara

Chris Burnett

Timothy J. Norman

  • Problem
  • Modern coalition operations frequently require the integration and collaboration of highly diverse forces, often with very limited experience working together
  • Within such ad-hoc teams, members must be able to delegate tasks to, and subsequently rely upon, each other, requiring trust
  • Such teams are characterized by unfamiliarity, diversity, rapid formation and short life-span, all are barriers to trust formation [4]
  • Approach
  • We are interested in how intelligent agents may support this process by automatically collecting and integrating data to support trust evaluations for human decision makers, specifically in ad-hoc team environments
  • Traditional multi-agent trust approaches rely on direct and reputational experiences lacking in ad-hoc team environments
  • Categorical Assumptions and Monitoring can provide evidence to a trust model in the absence of experiential evidence [3]
  • Model
  • Agents require a cognitive model of trust which integrates available evidence to produce trust beliefs [1] (Figure 1)
  • Subjective Logic [2]provides our underlying subjective belief representation
  • When both direct and reputational evidence is insufficient, categorical assumptions and monitoring provide agents with additional evidence
  • These support tentative decisions which provide stronger direct experiences
  • Monitoring
  • Delegation in ad-hoc teams does not rely on trust alone. When trust is insufficient, a mixture of trust and monitoring behaviors is used
  • With monitoring, trust is only required for the elements of a task for which it is lacking
  • However, monitoring will incur costs on both parties in a delegation relationship, hampering effectiveness
  • Monitoring should provide rapid feedback of evidence to the trust model, and be reduced as trust increases

Fig. 1: Trust model overview

  • Trust Dimensions
  • Competence – Does the candidate have the ability to undertake task T?
  • Disposition – Will the candidate behave the way I expect?
    • Normative Consistency – can the candidate be trusted to observe norms?
    • Normative Conflict – will the candidate encounter normative conflicts?
    • Conflict Resolution – what are the candidates priorities over norms?
  • These distinctions affect monitoring and intervention strategies
  • Monitoring Types
  • Passive
    • Trustor (A) observes trustee (B)
    • No communication required
    • Violations must be inferred from observation alone – costly for A
  • Reactive
    • A requests reports from B at A’s discretion – B cannot anticipate monitoring requests
  • Proactive
    • B sends reports to A at B’s discretion - A must trust B to honestly and accurately report
  • Scheduled
    • Monitoring communication occurs at predefined intervals
    • Frequency crucial in determining effectiveness
    • Both agents can anticipate monitoring activity
  • Categorical Trust
  • Humans deal with lack of evidence by importing categorical information from previous collaborative settings. This is done by stereotyping; generalizing from individuals to types of agents and their trustworthiness.
  • Agents can engage in such behavior by learningrelationships between features of collaboration partners and expected performance.
  • This allows for an agent to form a tentativetrust evaluation even when there are no direct or reputational evidence sources for aparticular candidate

Fig. 2: Monitoring


[1] R. Falcone and C. Castelfranchi. Social trust: a cognitive approach. Trust and Deception in Virtual Societies, pages 55–90, 2001.

[2] A. Jøsang, R. Hayward, and S. Pope. Trust network analysis with subjective logic. In Proceedings of the 29th Australasian Computer Science Conference-Volume 48, pages 85–94. Australian Computer Society, Inc. Darlinghurst, Australia, Australia, 2006.

[3] D. Meyerson, K. Weick, and R. Kramer. Swift trust and temporary groups. Trust in Organizations: Frontiers of Theory and Research, 195, 1996.

[4] R. Pascual, M. Mills, and C. Blendell. Supporting distributed and ad-hoc team interaction. In People In Control: An International Conference on Human Interfaces in Control Rooms, Cockpits and Command Centres, 1999., pages 64–71, 1999