Problem Solving Through Human-AI Preference-Based Cooperation
TU Darmstadt（图宾根大学）；LMU Munich（慕尼黑大学）；University of Würzburg（弗赖堡大学）；RU Bochum（波恩大学）
Subhabrata Dutta, Timo Kaufmann, Goran Glavaš, Ivan Habernal, Kristian Kersting, Frauke Kreuter, Mira Mezini, Iryna Gurevych, Eyke Hüllermeier, Hinrich Schuetze
针对生成式 AI 在专家领域复杂问题中难以作为可靠人机协作伙伴的缺陷，提出了一种新颖的人机共建框架 HAICo2，并迈出了形式化该框架的第一步，同时讨论了其面临的开放研究问题。
摘要：
尽管人们普遍认为通用人工智能（AGI）——甚至是超人类 AI——即将到来，但专家领域中的复杂问题远未得到解决。我们认为，这类问题需要人机协作，而当前生成式 AI 的最先进技术由于诸多缺陷，无法扮演可靠合作伙伴的角色，这些缺陷包括难以跟踪复杂的解决方案工件（例如软件程序）、对灵活的人类偏好表达支持有限，以及在交互环境中缺乏对人类偏好的适应。为了应对这些挑战，我们提出了 HAICo2，一种新颖的人机共建框架。我们迈出了形式化 HAICo2 的第一步，并讨论了它面临的困难开放研究问题。
英文摘要：
While there is a widespread belief that artificial general intelligence (AGI) -- or even superhuman AI -- is imminent, complex problems in expert domains are far from being solved. We argue that such problems require human-AI cooperation and that the current state of the art in generative AI is unable to play the role of a reliable partner due to a multitude of shortcomings, including difficulty to keep track of a complex solution artifact (e.g., a software program), limited support for versatile human preference expression and lack of adapting to human preference in an interactive setting. To address these challenges, we propose HAICo2, a novel human-AI co-construction framework. We take first steps towards a formalization of HAICo2 and discuss the difficult open research problems that it faces.
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Human Implicit Preference-Based Policy Fine-tuning for Multi-Agent Reinforcement Learning in USV Swarm
Korea Military Academy (KMA)（韩国军事学院）；KAIST（韩国科学技术院）；Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院）；University of California, Riverside（加州大学河滨分校）
CV-Arena: An Open Benchmark for Instructional Computer Vision Problem Solving with Human-AI Collaborative Preferences
Texas A&M University（德克萨斯A&M大学）；Worcester Polytechnic Institute（沃斯特理工大学）；Tohoku University（东北大学）；Georgia Institute of Technology（佐治亚理工学院）；NVIDIA（英伟达）；UCSB（加州大学圣塔芭芭拉分校）；UC Merced（加州大学默塞德分校）
