Conflict-Based Search is one of the most popular methods for multi-agent path finding. Though it is complete and optimal, it does not scale well. Recent works have been proposed to accelerate it by introducing various heuristics. However, whether these heuristics can apply to non-grid-based problem settings while maintaining their effectiveness remains an open question. In this work, we find that the answer is prone to be no. To this end, we propose a learning-based component, i.e., the Graph Transformer, as a heuristic function to accelerate the planning. The proposed method is provably complete and bounded-suboptimal with any desired factor. We conduct extensive experiments on two environments with dense graphs. Results show that the proposed Graph Transformer can be trained in problem instances with relatively few agents and generalizes well to a larger number of agents, while achieving better performance than state-of-the-art methods.
Accelerating Focal Search in Multi-Agent Path Finding with Tighter Lower Bounds
Thomas Lord Department of Computer Science, University of Southern California（美国南加州大学计算机科学系）；University of Wisconsin–Madison（威斯康星大学麦迪逊分校）；Carnegie Mellon University（卡内基梅隆大学）；University of California, Irvine（加州大学尔湾分校）
SeekBrain: An Autonomous Multi-Agent System for Accelerating Neuroscience Discovery
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences（中国科学院脑科学与智能技术卓越创新中心）；The Chinese University of Hong Kong（香港中文大学）；Southeast University（东南大学）；Fudan University（复旦大学）；New York University（纽约大学）
Fast and scalable retrosynthetic planning with a transformer neural network and speculative beam search
SUPSI, IDSIA（SUPSI、IDSIA）；Pfizer Research and Development（辉瑞研发）；Institute for Digital Technologies for Personalized Healthcare（个性化医疗数字技术研究所）；KAUST（王国立科技大学）；Center for Generative AI（生成人工智能中心）
