  In the field of Multi-Agent Systems (MAS), known for their openness,
dynamism, and cooperative nature, the ability to trust the resources and
services of other agents is crucial. Trust, in this setting, is the reliance
and confidence an agent has in the information, behaviors, intentions,
truthfulness, and capabilities of others within the system. Our paper
introduces a new graphical approach that utilizes factor graphs to represent
the interdependent behaviors and trustworthiness among agents. This includes
modeling the behavior of robots as a trajectory of actions using a Gaussian
process factor graph, which accounts for smoothness, obstacle avoidance, and
trust-related factors. Our method for evaluating trust is decentralized and
considers key interdependent sub-factors such as proximity safety, consistency,
and cooperation. The overall system comprises a network of factor graphs that
interact through trust-related factors and employs a Bayesian inference method
to dynamically assess trust-based decisions with informed consent. The
effectiveness of this method is validated via simulations and empirical tests
with autonomous robots navigating unsignalized intersections.
