تقرير
Neural Amortized Inference for Nested Multi-agent Reasoning
| العنوان: | Neural Amortized Inference for Nested Multi-agent Reasoning |
|---|---|
| المؤلفون: | Jha, Kunal, Le, Tuan Anh, Jin, Chuanyang, Kuo, Yen-Ling, Tenenbaum, Joshua B., Shu, Tianmin |
| سنة النشر: | 2023 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Multiagent Systems, Computer Science - Robotics |
| الوصف: | 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. Comment: 8 pages, 10 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2308.11071 |
| رقم الأكسشن: | edsarx.2308.11071 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |