Trust And Reputation In Multi-Agent Resilient Systems

Consistent and accurate understanding of trust and reputation in multi-agent systems is a prerequisite for evaluating system state and determining any needed corrective actions that preserve continued safe operation. In this paper we build on our prior reputation analysis work, which was based on evaluating satisfaction of transactions between agents and agent health. The evaluation was used to discount untrustworthy inputs to a Markov decision model that determines the actions taken by agents in a resilient system network. We extend our earlier work by including new health factors in the trust estimation along with a mechanism for updating each agent's belief state computation through modification of its emission probabilities.