Bayesian Methods for Trust in Collaborative Multi-Agent Autonomy

arXiv:2403.16956v1 Announce Type: cross
Abstract: Multi-agent, collaborative sensor fusion is a vital component of a multi-national intelligence toolkit. In safety-critical and/or contested environments, adversaries may infiltrate and compromise a number of agents. We analyze state of the art multi-target tracking algorithms under this compromised agent threat model. We prove that the track existence probability test ("track score") is significantly vulnerable to even small numbers of adversaries. To add security awareness, we design a trust estimation framework using hierarchical Bayesian updating. …

adversaries agent agents algorithms art arxiv compromise compromised critical cs.cr cs.ro cs.sy eess.sy environments fusion intelligence may national prove safety safety-critical sensor state target threat threat model toolkit track tracking trust under