Multi-Agent Goal Recognition with Team- and Goal-Conditioned Reinforcement Learning and Factorized Branch-and-Bound
For observers in multi-agent systems like drone surveillance and collaborative robotics, this work provides a scalable method to jointly infer team structure and goals from behavior alone.
Multi-agent goal recognition requires inferring team partitions and goals from agent trajectories. MAGR-BB uses a team- and goal-conditioned policy as a scoring model inside factorized branch-and-bound search, achieving the same top-ranked hypothesis as exhaustive search while reducing hypothesis materialization by orders of magnitude and cumulative runtime substantially.
Multi-agent goal recognition asks an observer to jointly infer which agents act together and what each team is trying to achieve, so the hypothesis space grows combinatorially with the number of team partitions and goals per team. Real applications such as drone surveillance and collaborative robotics expose only the agents' trajectory, which forces the observer to rank team-goal hypotheses from behavior alone. Multi-Agent Goal Recognition with Branch-and-Bound (MAGR-BB) addresses this setting with a shared team- and goal-conditioned policy used as the scoring model inside a factorized branch-and-bound search. On a controlled multi-agent Blocksworld benchmark, MAGR-BB returns the same top-ranked hypothesis as exhaustive search throughout the trajectory while cutting hypothesis materialization by orders of magnitude and reducing cumulative recognition runtime substantially.