Advancing Human-Machine Teaming: Concepts, Challenges, and Applications

• URL: http://arxiv.org/abs/2503.16518v1
• Date: Sun, 16 Mar 2025 19:32:17 GMT
• Title: Advancing Human-Machine Teaming: Concepts, Challenges, and Applications
• Authors: Dian Chen, Han Jun Yoon, Zelin Wan, Nithin Alluru, Sang Won Lee, Richard He, Terrence J. Moore, Frederica F. Nelson, Sunghyun Yoon, Hyuk Lim, Dan Dongseong Kim, Jin-Hee Cho,
• Abstract summary: Human-Machine Teaming (HMT) is revolutionizing collaboration across domains such as defense, healthcare, and autonomous systems by integrating AI-driven decision-making, trust calibration, and adaptive teaming.<n>This survey presents a comprehensive taxonomy of HMT, analyzing theoretical models, including reinforcement learning, instance-based learning, and interdependence theory, alongside interdisciplinary methodologies.
• Score: 11.61824291102382
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Human-Machine Teaming (HMT) is revolutionizing collaboration across domains such as defense, healthcare, and autonomous systems by integrating AI-driven decision-making, trust calibration, and adaptive teaming. This survey presents a comprehensive taxonomy of HMT, analyzing theoretical models, including reinforcement learning, instance-based learning, and interdependence theory, alongside interdisciplinary methodologies. Unlike prior reviews, we examine team cognition, ethical AI, multi-modal interactions, and real-world evaluation frameworks. Key challenges include explainability, role allocation, and scalable benchmarking. We propose future research in cross-domain adaptation, trust-aware AI, and standardized testbeds. By bridging computational and social sciences, this work lays a foundation for resilient, ethical, and scalable HMT systems.

Related papers

• Advancing Education through Tutoring Systems: A Systematic Literature Review [3.276010440333338]
  This study systematically reviews the transformative role of Tutoring Systems, encompassing Intelligent Tutoring Systems (ITS) and Robot Tutoring Systems (RTS)<n>The findings reveal significant advancements in AI techniques that enhance adaptability, engagement, and learning outcomes.<n>The study highlights the complementary strengths of ITS and RTS, proposing integrated hybrid solutions to maximize educational benefits.
  arXiv  Detail & Related papers  (2025-03-12T18:47:07Z)
• Evaluating Human-AI Collaboration: A Review and Methodological Framework [4.41358655687435]
  The use of artificial intelligence (AI) in working environments with individuals, known as Human-AI Collaboration (HAIC), has become essential.<n> evaluating HAIC's effectiveness remains challenging due to the complex interaction of components involved.<n>This paper provides a detailed analysis of existing HAIC evaluation approaches and develops a fresh paradigm for more effectively evaluating these systems.
  arXiv  Detail & Related papers  (2024-07-09T12:52:22Z)
• Towards Bidirectional Human-AI Alignment: A Systematic Review for Clarifications, Framework, and Future Directions [101.67121669727354]
  Recent advancements in AI have highlighted the importance of guiding AI systems towards the intended goals, ethical principles, and values of individuals and groups, a concept broadly recognized as alignment. The lack of clarified definitions and scopes of human-AI alignment poses a significant obstacle, hampering collaborative efforts across research domains to achieve this alignment. We introduce a systematic review of over 400 papers published between 2019 and January 2024, spanning multiple domains such as Human-Computer Interaction (HCI), Natural Language Processing (NLP), Machine Learning (ML)
  arXiv  Detail & Related papers  (2024-06-13T16:03:25Z)
• Towards interactive evaluations for interaction harms in human-AI systems [8.989911701384788]
  We argue for a paradigm shift toward evaluation centered on. textitinteractional ethics. We propose principles for evaluating generative models through interaction scenarios and human impact metrics.
  arXiv  Detail & Related papers  (2024-05-17T08:49:34Z)
• Explainable Interface for Human-Autonomy Teaming: A Survey [12.26178592621411]
  This paper conducts a thoughtful study on the underexplored domain of Explainable Interface (EI) in HAT systems. We explore the design, development, and evaluation of EI within XAI-enhanced HAT systems. We contribute to a novel framework for EI, addressing the unique challenges in HAT.
  arXiv  Detail & Related papers  (2024-05-04T06:35:38Z)
• Understanding the Application of Utility Theory in Robotics and Artificial Intelligence: A Survey [5.168741399695988]
  The utility is a unifying concept in economics, game theory, and operations research, even in the Robotics and AI field. This paper introduces a utility-orient needs paradigm to describe and evaluate inter and outer relationships among agents' interactions.
  arXiv  Detail & Related papers  (2023-06-15T18:55:48Z)
• Training Socially Aligned Language Models on Simulated Social Interactions [99.39979111807388]
  Social alignment in AI systems aims to ensure that these models behave according to established societal values. Current language models (LMs) are trained to rigidly replicate their training corpus in isolation. This work presents a novel training paradigm that permits LMs to learn from simulated social interactions.
  arXiv  Detail & Related papers  (2023-05-26T14:17:36Z)
• DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanations [119.1953397679783]
  We focus on advancing the state-of-the-art in interpreting multimodal models. Our proposed approach, DIME, enables accurate and fine-grained analysis of multimodal models.
  arXiv  Detail & Related papers  (2022-03-03T20:52:47Z)
• Active Inference in Robotics and Artificial Agents: Survey and Challenges [51.29077770446286]
  We review the state-of-the-art theory and implementations of active inference for state-estimation, control, planning and learning. We showcase relevant experiments that illustrate its potential in terms of adaptation, generalization and robustness.
  arXiv  Detail & Related papers  (2021-12-03T12:10:26Z)
• Human-Robot Collaboration and Machine Learning: A Systematic Review of Recent Research [69.48907856390834]
  Human-robot collaboration (HRC) is the approach that explores the interaction between a human and a robot. This paper proposes a thorough literature review of the use of machine learning techniques in the context of HRC.
  arXiv  Detail & Related papers  (2021-10-14T15:14:33Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.