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Working together on complex collaborative tasks requires agents to coordinatetheir actions. Doing this explicitly or completely prior to the actualinteraction is not always possible nor sufficient. Agents also need tocontinuously understand the current actions of others and quickly adapt theirown behavior appropriately. Here we investigate how efficient, automaticcoordination processes at the level of mental states (intentions, goals), whichwe call belief resonance, can lead to collaborative situated problem-solving.We present a model of hierarchical active inference for collaborative agents(HAICA). It combines efficient Bayesian Theory of Mind processes with aperception-action system based on predictive processing and active inference.Belief resonance is realized by letting the inferred mental states of one agentinfluence another agent's predictive beliefs about its own goals andintentions. This way, the inferred mental states influence the agent's own taskbehavior without explicit collaborative reasoning. We implement and evaluatethis model in the Overcooked domain, in which two agents with varying degreesof belief resonance team up to fulfill meal orders. Our results demonstratethat agents based on HAICA achieve a team performance comparable to recentstate of the art approaches, while incurring much lower computational costs. Wealso show that belief resonance is especially beneficial in settings were theagents have asymmetric knowledge about the environment. The results indicatethat belief resonance and active inference allow for quick and efficient agentcoordination, and thus can serve as a building block for collaborativecognitive agents.

Resonating Minds -- Emergent Collaboration Through Hierarchical Active Inference