Neural Amortized Inference for Nested Multi-agent Reasoning
This addresses the problem of scalable social inference for AI systems in multi-agent interactions, though it appears incremental as it builds on existing reasoning models.
The paper tackled the computational complexity of nested multi-agent reasoning by proposing a neural network approach to amortize high-order social inference, resulting in computational efficiency with minimal accuracy degradation in two challenging domains.
Multi-agent interactions, such as communication, teaching, and bluffing, often rely on higher-order social inference, i.e., understanding how others infer oneself. Such intricate reasoning can be effectively modeled through nested multi-agent reasoning. Nonetheless, the computational complexity escalates exponentially with each level of reasoning, posing a significant challenge. However, humans effortlessly perform complex social inferences as part of their daily lives. To bridge the gap between human-like inference capabilities and computational limitations, we propose a novel approach: leveraging neural networks to amortize high-order social inference, thereby expediting nested multi-agent reasoning. We evaluate our method in two challenging multi-agent interaction domains. The experimental results demonstrate that our method is computationally efficient while exhibiting minimal degradation in accuracy.