Generating explanation to explain its behavior is an essential capability for a robotic teammate. Explanations help human partners better understand the situation and maintain trust of their teammates. Prior work on robot generating explanations focuses on providing the reasoning behind its decision making. These approaches, however, fail to heed the cognitive requirement of understanding an explanation. In other words, while they provide the right explanations from the explainer's perspective, the explainee part of the equation is ignored. In this work, we address an important aspect along this direction that contributes to a better understanding of a given explanation, which we refer to as the progressiveness of explanations. A progressive explanation improves understanding by limiting the cognitive effort required at each step of making the explanation. As a result, such explanations are expected to be smoother and hence easier to understand. A general formulation of progressive explanation is presented. Algorithms are provided based on several alternative quantifications of cognitive effort as an explanation is being made, which are evaluated in a standard planning competition domain.
HARMONIC: Cognitive and Control Collaboration in Human-Robotic Teams
Cognitive Science Department（认知科学系）；Department of Computer Science（计算机科学系）；Rensselaer Polytechnic Institute（拉特格斯理工学院）；University of Texas at Austin（德克萨斯大学奥斯汀分校）
Red Teaming Models for Hyperspectral Image Analysis Using Explainable AI
Warsaw University of Technology（华沙理工大学）；University of Warsaw（华沙大学）；Silesian University of Technology（西里西亚工业大学）；KP Labs（KP实验室）；European Space Agency（欧洲空间局）
Flexible and Inherently Comprehensible Knowledge Representation for Data-Efficient Learning and Trustworthy Human-Machine Teaming in Manufacturing Environments
Airbus Central R&T（空客中央研发部）
本文利用 Conceptual Space 框架和典型性量化模型表示智能体知识，解决制造领域数据稀缺下的可解释分类问题，提升人机协作的可信赖性。
Multi-Robot Open Adaptive Teaming Across Unseen Environments, Partners, and Scales
Shanghai Jiao Tong University（上海交通大学）；Sun Yat-sen University（中山大学）；National University of Singapore（新加坡国立大学）；Genisom AI；University of Bristol（布里斯托大学）；Purdue University（普渡大学）；Newcastle University（新castle大学）
Towards human-agent knowledge fusion (HAKF) in support of distributed coalition teams
IBM Research Europe（IBM欧洲研究院）；University of Brescia（布雷西亚大学）；Cardiff University（卡迪夫大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；US Army Research Laboratory（美国陆军研究实验室）；Defence Science and Technolog Laboratory（国防科学技术实验室）
Understanding LLM Behaviors via Compression: Data Generation, Knowledge Acquisition and Scaling Laws
Institute for Interdisciplinary Information Sciences, Tsinghua University（清华大学交叉信息研究院）；School of Computer Science and Engineering, Beihang University（北航计算机科学与工程学院）
