Epistemic planning can be used for decision making in multi-agent situations with distributed knowledge and capabilities. Dynamic Epistemic Logic (DEL) has been shown to provide a very natural and expressive framework for epistemic planning. In this paper, we aim to give an accessible introduction to DEL-based epistemic planning. The paper starts with the most classical framework for planning, STRIPS, and then moves towards epistemic planning in a number of smaller steps, where each step is motivated by the need to be able to model more complex planning scenarios.
Dynamic Term-Modal Logics for First-Order Epistemic Planning
DTU Compute, Technical University of Denmark（丹麦技术大学DTU计算学院）；London School of Economics（伦敦政治经济学院）；Center for Information and Bubble Studies, University of Copenhagen（哥本哈根大学信息与泡沫研究中心）
Parameterized Complexity Results for a Model of Theory of Mind Based on Dynamic Epistemic Logic
Institute for Logic, Language and Computation（逻辑、语言与计算研究所）；University of Amsterdam（阿姆斯特丹大学）；Donders Institute for Brain, Cognition, and Behaviour（唐德斯大脑、认知与行为研究所）；Radboud University（拉德堡德大学）
Position: Agent Should Invoke External Tools ONLY When Epistemically Necessary
University of Edinburgh（爱丁堡大学）；University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；Northwestern University（西北大学）；Princeton University（普林斯顿大学）；The Chinese University of Hong Kong（香港中文大学）
CUPID: A Plug-in Framework for Joint Aleatoric and Epistemic Uncertainty Estimation with a Single Model
Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore（南洋理工大学李科钦医学院，新加坡）；College of Computing and Data Science, Nanyang Technological University, Singapore（南洋理工大学计算与数据科学学院，新加坡）；Centre for Medical Technologies & Innovations, National Health Group, Singapore（国家健康集团医学技术与创新中心，新加坡）
A Decision-Theoretic Approach for Managing Misalignment
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Georgia Institute of Technology（佐治亚理工学院）；University of California, Berkeley（加州大学伯克利分校）；Amrita Vishwa Vidyapeetham（阿米特拉维瓦大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Bayesian uncertainty estimation improves clinical decision making in medical AI agents
University Hospital RWTH Aachen（亚琛工业大学附属医院）；Heidelberg University Hospital（海德堡大学医院）；TU Dresden（德累斯顿工业大学）；TUD Dresden University of Technology（德累斯顿工业大学）
