Recently, a boxology (graphical language) with design patterns for hybrid AI was proposed, combining symbolic and sub-symbolic learning and reasoning. In this paper, we extend this boxology with actors and their interactions. The main contributions of this paper are: 1) an extension of the taxonomy to describe distributed hybrid AI systems with actors and interactions; and 2) showing examples using a few design patterns relevant in multi-agent systems and human-agent interaction.
Multi-agent Soft Actor-Critic Based Hybrid Motion Planner for Mobile Robots
College of Electronics and Information Engineering, Tongji University（同济大学电子与信息工程学院）；School of Cyber Science and Engineering, Southeast University（东南大学网络空间安全学院）；School of Automation, Southeast University（东南大学自动化学院）
F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院）；Tongji University（同济大学）；Shanghai Research Institute for Intelligent Autonomous Systems（上海智能自主系统研究院）；East China Normal University（华东师范大学）；Shanghai Jiao Tong University（上海交通大学）
Controlling Large Language Model-based Agents for Large-Scale Decision-Making: An Actor-Critic Approach
School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；SenseTime Research（商汤科技研究院）；Shanghai Jiao Tong University（上海交通大学）；The University of Manchester（曼彻斯特大学）
Search-R2: Enhancing Search-Integrated Reasoning via Actor-Refiner Collaboration
The Chinese University of Hong Kong（香港中文大学）；LLM Department, Tencent（腾讯语言模型部门）；Mohamed bin Zayed University of Artificial Intelligence（马尔代夫人工智能大学）；McGill University（麦吉尔大学）；City University of Hong Kong（香港城市大学）；The University of Edinburgh（爱丁堡大学）
LogicGuard: Improving Embodied LLM agents through Temporal Logic based Critics
Department of Mechanical Engineering, University of California at Santa Barbara（加州大学圣芭芭拉分校机械工程系）；Department of Electrical and Computer Engineering, Michigan State University（密歇根州立大学电气与计算机工程系）
EGMOF: Efficient Generation of Metal-Organic Frameworks Using a Hybrid Diffusion-Transformer Architecture
Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology（韩国科学技术院化学与生物分子工程系）；Department of Chemistry, University of Toronto（多伦多大学化学系）；Vector Institute for Artificial Intelligence（人工智能矢量研究所）；Department of Chemistry, Sungkyunkwan University（成均馆大学化学系）；Acceleration Consortium（加速联盟）；Department of Computer Science, University of Toronto（多伦多大学计算机科学系）；Department of Materials Science & Engineering, University of Toronto（多伦多大学材料科学与工程系）；Department of Chemical Engineering & Applied Chemistry, University of Toronto（多伦多大学化学工程与应用化学系）；Canadian Institute for Advanced Research (CIFAR)（加拿大高级研究 institute (CIFAR)）；NVIDIA
