A Factor Graph Model of Trust for a Collaborative Multi-Agent System

TL;DR

This paper presents a novel factor graph model for trust in multi-agent systems, enabling decentralized, Bayesian trust evaluation to improve autonomous robot cooperation and navigation.

Contribution

It introduces a graphical factor graph approach to model trust among agents, incorporating behavior, safety, and cooperation factors for dynamic trust assessment.

Findings

Effective trust evaluation in multi-agent systems demonstrated through simulations

Improved robot navigation at intersections using trust-based decision making

Decentralized trust modeling enhances system robustness and cooperation

Abstract

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.