The purpose of this tutorial is to help individuals use the \underline{FireCommander} game environment for research applications. The FireCommander is an interactive, probabilistic joint perception-action reconnaissance environment in which a composite team of agents (e.g., robots) cooperate to fight dynamic, propagating firespots (e.g., targets). In FireCommander game, a team of agents must be tasked to optimally deal with a wildfire situation in an environment with propagating fire areas and some facilities such as houses, hospitals, power stations, etc. The team of agents can accomplish their mission by first sensing (e.g., estimating fire states), communicating the sensed fire-information among each other and then taking action to put the firespots out based on the sensed information (e.g., dropping water on estimated fire locations). The FireCommander environment can be useful for research topics spanning a wide range of applications from Reinforcement Learning (RL) and Learning from Demonstration (LfD), to Coordination, Psychology, Human-Robot Interaction (HRI) and Teaming. There are four important facets of the FireCommander environment that overall, create a non-trivial game: (1) Complex Objectives: Multi-objective Stochastic Environment, (2)Probabilistic Environment: Agents' actions result in probabilistic performance, (3) Hidden Targets: Partially Observable Environment and, (4) Uni-task Robots: Perception-only and Action-only agents. The FireCommander environment is first-of-its-kind in terms of including Perception-only and Action-only agents for coordination. It is a general multi-purpose game that can be useful in a variety of combinatorial optimization problems and stochastic games, such as applications of Reinforcement Learning (RL), Learning from Demonstration (LfD) and Inverse RL (iRL).
University of Georgia（佐治亚大学）；Teesside University（提赛德大学）；Queen’s University Belfast（贝尔法斯特女王大学）
Planning for ad hoc teamwork is challenging because it involves agents collaborating without any prior coordination or communication. The focus is on principled methods for a single agent to cooperate with others. This motivates investigating the ad hoc teamwork problem in the context of individual decision making frameworks. However, individual decision making in multiagent settings faces the task of having to reason about other agents' actions, which in turn involves reasoning about others. An established approximation that operationalizes this approach is to bound the infinite nesting from …
X-Teaming: Multi-Turn Jailbreaks and Defenses with Adaptive Multi-Agents
University of California, Los Angeles（加州大学洛杉矶分校）；University of Washington（华盛顿大学）；Qatar Computing Research Institute（卡塔尔计算研究所）；Google（谷歌）；Stanford University（斯坦福大学）
本文提出Claw AI Lab，一种自主研究平台，通过隐藏的提示到论文流程实现自动化研究，并提供交互式AI实验室。该平台允许用户通过一个提示创建完整的研究团队，支持自定义角色、协作流程、实时监控、 artifact检查和回滚/恢复控制。Claw-Code Harness连接本地代码库、数据集和检查点，提高实验执行、完成和结果完整性。在内部评估中，Claw AI Lab在想法新颖性、实验完整性和论文质量上被AI专家评委一致偏好。
Tempe, Arizona（亚利桑那州坦佩（这里Tempe是城市，严格来说不属于机构，原文本中Yu Zhang的affiliation里Tempe, Arizona可能是地址，非机构，原提取范围只提取机构，所以这里可能没有正确机构？但原文本中Mehrdad Zakershahrak的affiliation是Computer Science and Engineering Department, Arizona State University，所以应该提取这个。Yu Zhang的affiliation中Tempe, Arizona不是机构，所以只保留第一个））
Human-robot teaming is one of the most important applications of artificial intelligence in the fast-growing field of robotics. For effective teaming, a robot must not only maintain a behavioral model of its human teammates to project the team status, but also be aware that its human teammates' expectation of itself. Being aware of the human teammates' expectation leads to robot behaviors that better align with human expectation, thus facilitating more efficient and potentially safer teams. Our work addresses the problem of human-robot cooperation with the consideration of such teammate models…
SIFT, LLC（SIFT有限责任公司）；MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）
As robotic teammates become more common in society, people will assess the robots' roles in their interactions along many dimensions. One such dimension is effectiveness: people will ask whether their robotic partners are trustworthy and effective collaborators. This begs a crucial question: how can we quantitatively measure the helpfulness of a robotic partner for a given task at hand? This paper seeks to answer this question with regards to the interactive robot's decision making. We describe a clear, concise, and task-oriented metric applicable to many different planning and execution parad…
Dynamic Knowledge Exchange and Dual-diversity Review: Concisely Unleashing the Potential of a Multi-Agent Research Team
East China Normal University（华东师范大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Peking University（北京大学）；University of Chinese Academy of Sciences（中国科学院大学）；Bytedance Inc（字节跳动）；Canadian Collaborative Entrepreneurs Organization Inc（加拿大合作企业家组织）
Value-based methods of multi-agent reinforcement learning (MARL), especially the value decomposition methods, have been demonstrated on a range of challenging cooperative tasks. However, current methods pay little attention to the interaction between agents, which is essential to teamwork in games or real life. This limits the efficiency of value-based MARL algorithms in the two aspects: collaborative exploration and value function estimation. In this paper, we propose a novel cooperative MARL algorithm named as interactive actor-critic~(IAC), which models the interaction of agents from the pe…
University of California, Berkeley（加州大学伯克利分校）；Unveristy of California, San Diego（加州大学圣地亚哥分校）；Tsinghua University（清华大学）；Facebook AI Research（Facebook人工智能研究院）
Recent advances in multi-agent reinforcement learning (MARL) have achieved super-human performance in games like Quake 3 and Dota 2. Unfortunately, these techniques require orders-of-magnitude more training rounds than humans and don't generalize to new agent configurations even on the same game. In this work, we propose Collaborative Q-learning (CollaQ) that achieves state-of-the-art performance in the StarCraft multi-agent challenge and supports ad hoc team play. We first formulate multi-agent collaboration as a joint optimization on reward assignment and show that each agent has an approxim…
Institute for Systems and Robotics（系统与机器人研究所）；Instituto Superior Técnico, Universidade de Lisboa（里斯本大学高等理工学院）；Queen Mary University of London（伦敦玛丽女王大学）；KTH Royal Institute of Technology（KTH皇家理工学院）
A growing field in robotics and Artificial Intelligence (AI) research is human-robot collaboration, whose target is to enable effective teamwork between humans and robots. However, in many situations human teams are still superior to human-robot teams, primarily because human teams can easily agree on a common goal with language, and the individual members observe each other effectively, leveraging their shared motor repertoire and sensorimotor resources. This paper shows that for cognitive robots it is possible, and indeed fruitful, to combine knowledge acquired from interacting with elements…
HGIC: A Hand Gesture Based Interactive Control System for Efficient and Scalable Multi-UAV Operations
College of Aeronautics and Engineering, Kent State University（肯特州立大学航空与工程学院）；Department of Computer Science, Kent State University（肯特州立大学计算机科学系）；College of Engineering, Cornell University（康奈尔大学工程学院）；Department of Systems Science and Industrial Engineering, Binghamton University（宾汉姆顿大学系统科学与工业工程系）
A Reflectance Transformation Imaging technique (RTI) realized by multi-rotor Unmanned Aerial Vehicles (UAVs) with a focus on deployment in difficult to access buildings is presented in this letter. RTI is a computational photographic method that captures a surface shape and color of a subject and enables its interactive re-lighting from any direction in a software viewer, revealing details that are not visible with the naked eye. The input of RTI is a set of images captured by a static camera, each one under illumination from a different known direction. We present an innovative approach apply…
