Neural Amortized Inference for Nested Multi-Agent Reasoning

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.

Authors

• Kunal Jha Dartmouth College
• Tuan Anh Le Google Research
• Chuanyang Jin Johns Hopkins University
• Yen-Ling Kuo Department of Computer Science, University of Virginia
• Joshua B. Tenenbaum MIT Computer Science and Artificial Intelligence Laboratory
• Tianmin Shu Johns Hopkins University

Keywords

• CMS: Social Cognition And Interaction
• RU: Applications