Adjustable autonomy refers to entities dynamically varying their own autonomy, transferring decision-making control to other entities (typically agents transferring control to human users) in key situations. Determining whether and when such transfers-of-control should occur is arguably the fundamental research problem in adjustable autonomy. Previous work has investigated various approaches to addressing this problem but has often focused on individual agent-human interactions. Unfortunately, domains requiring collaboration between teams of agents and humans reveal two key shortcomings of these previous approaches. First, these approaches use rigid one-shot transfers of control that can result in unacceptable coordination failures in multiagent settings. Second, they ignore costs (e.g., in terms of time delays or effects on actions) to an agent's team due to such transfers-of-control. To remedy these problems, this article presents a novel approach to adjustable autonomy, based on the notion of a transfer-of-control strategy. A transfer-of-control strategy consists of a conditional sequence of two types of actions: (i) actions to transfer decision-making control (e.g., from an agent to a user or vice versa) and (ii) actions to change an agent's pre-specified coordination constraints with team members, aimed at minimizing miscoordination costs. The goal is for high-quality individual decisions to be made with minimal disruption to the coordination of the team. We present a mathematical model of transfer-of-control strategies. The model guides and informs the operationalization of the strategies using Markov Decision Processes, which select an optimal strategy, given an uncertain environment and costs to the individuals and teams. The approach has been carefully evaluated, including via its use in a real-world, deployed multi-agent system that assists a research group in its daily activities.
A Survey on Autonomy-Induced Security Risks in Large Model-Based Agents
Dept. of Comp. Sci. & Tech.（计算机科学与技术系）；College of AI（人工智能学院）；Institute for AI（人工智能研究院）；BNRist Center（BNRist中心）；THBI Lab（THBI实验室）；Tsinghua-Bosch Joint Center for ML（清华大学-博世联合机器学习中心）；Tsinghua University（清华大学）
Flow: A Modular Learning Framework for Mixed Autonomy Traffic
Laboratory for Information and Decision Systems, Massachusetts Institute of Technology（信息与决策实验室，麻省理工学院）；Institute of Data, Systems, and Society, Massachusetts Institute of Technology（数据、系统与社会研究所，麻省理工学院）；Department of Mechanical Engineering, University of California, Berkeley（机械工程系，加州大学伯克利分校）
From Automation to Autonomy in Smart Manufacturing: A Bayesian Optimization Framework for Modeling Multi-Objective Experimentation and Sequential Decision Making
Bangladesh University of Engineering and Technology（孟加拉工程技术大学）；West Virginia University（西弗吉尼亚大学）
Amico: An Event-Driven Modular Framework for Persistent and Embedded Autonomy
Department of Aeronautics and Astronautics, Zhejiang University（浙江大学航空航天学院）；Department of Computer Science, University College London（伦敦大学学院计算机系）；Steinhardt School of Culture, Education, and Human Development, New York University（纽约大学文化、教育与人类发展学院）
The Rational Selection of Goal Operations and the Integration ofSearch Strategies with Goal-Driven Autonomy
Wright State University（莱特州立大学）；Michigan State University（密歇根州立大学）；Georgia Institute of Technology（佐治亚理工学院）；University of Georgia（佐治亚大学）；Skidaway Institute of Oceanography（斯基达韦海洋研究所）
Learning Autonomy: Off-Road Navigation Enhanced by Human Input
J. Mike Walker ’66 Department of Mechanical Engineering, Texas A&M University（J. Mike Walker ’66 机械工程系，德克萨斯A&M大学）；Department of Engineering technology and Industrial Distribution, Texas A&M University（工程技术与工业分销系，德克萨斯A&M大学）
Onboard Science Instrument Autonomy for the Detection of Microscopy Biosignatures on the Ocean Worlds Life Surveyor
Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）；Portland State University（波特兰州立大学）；University of California, Berkeley（加州大学伯克利分校）
Simulating Human-like Daily Activities with Desire-driven Autonomy
Institute for Artificial Intelligence, Peking University（北京大学人工智能研究院）；The University of Hong Kong（香港大学）；School of Artificial Intelligence, Beijing Normal University（北京师范大学人工智能学院）；Academy for Advanced Interdisciplinary Studies, Peking University（北京大学先进跨学科研究学院）；State Key Laboratory of General Artificial Intelligence, BIGAI（通用人工智能国家重点实验室，BIGAI）；Center on Frontiers of Computing Studies, School of Computer Science, Nat’l Eng. Research Center of Visual Technology, Peking University（计算前沿研究中心，计算机科学学院，国家工程视觉技术研究中心，北京大学）
Dark Patterns Meet GUI Agents: LLM Agent Susceptibility to Manipulative Interfaces and the Role of Human Oversight
University of Notre Dame（诺丁汉大学）；University of Michigan（密歇根大学）；Northeastern University（东北大学）；University of Washington（华盛顿大学）；Johns Hopkins University（约翰霍普金斯大学）
NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge
NASA Jet Propulsion Laboratory（美国国家航空航天局喷气推进实验室）；California Institute of Technology（加州理工学院）；Massachusetts Institute of Technology（麻省理工学院）；University of Southern California（南加州大学）；University of Colorado Boulder（科罗拉多大学博尔德分校）
