A novel trust architecture integrating differentiated trust and response strategies for a team of agents

摘要

Trust has been widely recognized as particularly significant among the factors influencing team performance. Trust directly impacts team performance as it plays a pivotal role in the decisions that each team member (each agent) makes regarding their own actions and their interactions with fellow team members (other agents). Hence, determining the appropriate actions of each agent in the team based on perceived trust information is critical to ensure optimal team performance. However, there is no generic mechanism dedicated to such a problem. This paper addresses this problem by proposing a novel trust architecture which integrates differentiated trust with response strategies. Differentiated trust is multidimensional trust with each trust dimension representing trust in an agent's abilities to perform the task associated with that dimension. With differentiated trust, an agent can differentiate the trustworthiness of another agent in performing different subtasks (a secondary task or a portion of the primary task). To further fulfill the transition from perceptual trust to practical actions, responses strategies are introduced. Each response strategy associates trust levels with the available actions through a distinct deterministic strategy. The high dimensional trust enabled by the differentiated trust is used as the input of a response strategy for more nuanced manipulation of the interactions. The impact of the proposed trust architecture is demonstrated through an experimental investigation. A platform simulating team performance is built based on a food foraging task. Scenarios embodying different types and proportions of a priori flawed agents are introduced to enable the system to distinguish between agents in terms of trust; thus, providing a potential to optimize team performance through the trust architecture. It is demonstrated that the proposed trust architecture enables optimized team performance in various scenarios involving agents with different trustworthiness by the appropriate determination of each agent's actions in the interactive teamwork.