CREW: Facilitating Human-AI Teaming Research

• URL: http://arxiv.org/abs/2408.00170v1
• Date: Wed, 31 Jul 2024 21:43:55 GMT
• Title: CREW: Facilitating Human-AI Teaming Research
• Authors: Lingyu Zhang, Zhengran Ji, Boyuan Chen,
• Abstract summary: We introduce CREW, a platform to facilitate Human-AI teaming research and engage collaborations from multiple scientific disciplines. It includes pre-built tasks for cognitive studies and Human-AI teaming with expandable potentials from our modular design. CREW benchmarks real-time human-guided reinforcement learning agents using state-of-the-art algorithms and well-tuned baselines.
• Score: 3.7324091969140776
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the increasing deployment of artificial intelligence (AI) technologies, the potential of humans working with AI agents has been growing at a great speed. Human-AI teaming is an important paradigm for studying various aspects when humans and AI agents work together. The unique aspect of Human-AI teaming research is the need to jointly study humans and AI agents, demanding multidisciplinary research efforts from machine learning to human-computer interaction, robotics, cognitive science, neuroscience, psychology, social science, and complex systems. However, existing platforms for Human-AI teaming research are limited, often supporting oversimplified scenarios and a single task, or specifically focusing on either human-teaming research or multi-agent AI algorithms. We introduce CREW, a platform to facilitate Human-AI teaming research and engage collaborations from multiple scientific disciplines, with a strong emphasis on human involvement. It includes pre-built tasks for cognitive studies and Human-AI teaming with expandable potentials from our modular design. Following conventional cognitive neuroscience research, CREW also supports multimodal human physiological signal recording for behavior analysis. Moreover, CREW benchmarks real-time human-guided reinforcement learning agents using state-of-the-art algorithms and well-tuned baselines. With CREW, we were able to conduct 50 human subject studies within a week to verify the effectiveness of our benchmark.

Related papers

• Aligning Generalisation Between Humans and Machines [74.120848518198]
  Recent advances in AI have resulted in technology that can support humans in scientific discovery and decision support but may also disrupt democracies and target individuals. The responsible use of AI increasingly shows the need for human-AI teaming. A crucial yet often overlooked aspect of these interactions is the different ways in which humans and machines generalise.
  arXiv  Detail & Related papers  (2024-11-23T18:36:07Z)
• The Model Mastery Lifecycle: A Framework for Designing Human-AI Interaction [0.0]
  The utilization of AI in an increasing number of fields is the latest iteration of a long process. There is an urgent need for methods to determine how AI should be used in different situations.
  arXiv  Detail & Related papers  (2024-08-23T01:00:32Z)
• Explainable Human-AI Interaction: A Planning Perspective [32.477369282996385]
  AI systems need to be explainable to the humans in the loop. We will discuss how the AI agent can use mental models to either conform to human expectations, or change those expectations through explanatory communication. While the main focus of the book is on cooperative scenarios, we will point out how the same mental models can be used for obfuscation and deception.
  arXiv  Detail & Related papers  (2024-05-19T22:22:21Z)
• The AI Collaborator: Bridging Human-AI Interaction in Educational and Professional Settings [3.506120162002989]
  AI Collaborator, powered by OpenAI's GPT-4, is a groundbreaking tool designed for human-AI collaboration research. Its standout feature is the ability for researchers to create customized AI personas for diverse experimental setups. This functionality is essential for simulating various interpersonal dynamics in team settings.
  arXiv  Detail & Related papers  (2024-05-16T22:14:54Z)
• Human-AI Collaboration in Real-World Complex Environment with Reinforcement Learning [8.465957423148657]
  We show that learning from humans is effective and that human-AI collaboration outperforms human-controlled and fully autonomous AI agents. We develop a user interface to allow humans to assist AI agents effectively.
  arXiv  Detail & Related papers  (2023-12-23T04:27:24Z)
• Applying HCAI in developing effective human-AI teaming: A perspective from human-AI joint cognitive systems [10.746728034149989]
  Research and application have used human-AI teaming (HAT) as a new paradigm to develop AI systems. We elaborate on our proposed conceptual framework of human-AI joint cognitive systems (HAIJCS) We propose a conceptual framework of human-AI joint cognitive systems (HAIJCS) to represent and implement HAT.
  arXiv  Detail & Related papers  (2023-07-08T06:26:38Z)
• Human-AI Coevolution [48.74579595505374]
  Coevolution AI is a process in which humans and AI algorithms continuously influence each other. This paper introduces Coevolution AI as the cornerstone for a new field of study at the intersection between AI and complexity science.
  arXiv  Detail & Related papers  (2023-06-23T18:10:54Z)
• BO-Muse: A human expert and AI teaming framework for accelerated experimental design [58.61002520273518]
  Our algorithm lets the human expert take the lead in the experimental process. We show that our algorithm converges sub-linearly, at a rate faster than the AI or human alone.
  arXiv  Detail & Related papers  (2023-03-03T02:56:05Z)
• Human-Centered Responsible Artificial Intelligence: Current & Future Trends [76.94037394832931]
  In recent years, the CHI community has seen significant growth in research on Human-Centered Responsible Artificial Intelligence. All of this work is aimed at developing AI that benefits humanity while being grounded in human rights and ethics, and reducing the potential harms of AI. In this special interest group, we aim to bring together researchers from academia and industry interested in these topics to map current and future research trends.
  arXiv  Detail & Related papers  (2023-02-16T08:59:42Z)
• Human-Centered AI for Data Science: A Systematic Approach [48.71756559152512]
  Human-Centered AI (HCAI) refers to the research effort that aims to design and implement AI techniques to support various human tasks. We illustrate how we approach HCAI using a series of research projects around Data Science (DS) works as a case study.
  arXiv  Detail & Related papers  (2021-10-03T21:47:13Z)
• Learning to Complement Humans [67.38348247794949]
  A rising vision for AI in the open world centers on the development of systems that can complement humans for perceptual, diagnostic, and reasoning tasks. We demonstrate how an end-to-end learning strategy can be harnessed to optimize the combined performance of human-machine teams.
  arXiv  Detail & Related papers  (2020-05-01T20:00:23Z)

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.