Coalition Free Energy and Adaptive Precision in Multi-Agent Cooperation

2026-05-25

AI Summary

This work addresses the challenge of credit assignment under uncertainty in multi-agent cooperation by proposing a variational framework grounded in the Game-Theoretic Free Energy Principle (GT-FEP). The approach models agent coalitions via Gibbs distributions and integrates Shapley values with variational inference. Its key innovation lies in uncovering a non-monotonic relationship between Shapley values and perceptual precision, leading to the design of an Adaptive Precision Control (APC) mechanism that dynamically optimizes observation precision without requiring prior hyperparameter tuning. Empirical evaluations on real-world Swiss roundabout trajectory data and multi-agent control tasks demonstrate that APC adapts online to varying noise levels, achieving performance comparable to the best fixed-precision baselines while eliminating the need for laborious hyperparameter pre-tuning.

Abstract

Cooperative multi-agent systems require robust mechanisms for credit assignment under uncertainty. Here we introduce a variational framework, termed the Game-Theoretic Free Energy Principle (GT-FEP), that models coalition formation through a Gibbs distribution over interacting agents. Within this framework, we derive a precision-dependent formulation of cooperative credit assignment and show that an agent's Shapley value exhibits a non-monotonic relationship with sensory precision beta, reflecting a trade-off between noisy inference and overconfident local estimation. Motivated by this observation, we propose Adaptive Precision Control (APC), an online adaptation algorithm that dynamically adjusts observation precision using local estimates of cooperative contribution. We evaluate APC on real-world Swiss roundabout trajectory datasets and on a multi-agent control task derived from the same trajectories. Across both settings, APC adapts to changing noise conditions online and achieves performance comparable to the best fixed precision without prior tuning. Our results connect variational inference, cooperative game theory, and adaptive multi-agent coordination, and suggest that precision adaptation can improve robust cooperation under uncertainty.

Problem

• multi-agent cooperation
• credit assignment
• uncertainty
• coalition formation
• sensory precision

Innovation

• Game-Theoretic Free Energy Principle
• Adaptive Precision Control
• Shapley value
• multi-agent cooperation
• variational inference