Annual Conference of ITA ACITA 2009

1. Annual Conference of ITA ACITA 2009 Agent Assistance in Forming Swift Trust in Ad-Hoc Decision-Making Teams Katia Sycara katia+@cs.cmu.edu Chris Burnett cburnett@abdn.ac.uk Timothy J. Norman t.j.norman@abdn.ac.uk • 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 References [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