The multi-agent path finding (MAPF) problem is a combinatorial search problem that aims at finding paths for multiple agents (e.g., robots) in an environment (e.g., an autonomous warehouse) such that no two agents collide with each other, and subject to some constraints on the lengths of paths. The real-world applications of MAPF require flexibility (e.g., solving variations of MAPF) as well as explainability. In this study, both of these challenges are addressed and some flexible and explainable solutions for MAPF and its variants are introduced.
Doctor-in-the-Loop: An Explainable, Multi-View Deep Learning Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer
Unit of Computer Systems and Bioinformatics, Department of Engineering（计算机系统与生物信息学单位，工程系）；Research Unit of Radiation Oncology, Department of Medicine and Surgery（放射肿瘤研究单位，医学与外科系）；Operative Research Unit of Radiation Oncology（放射肿瘤操作研究单位）；Research Unit of Anatomical Pathology, Department of Medicine and Surgery（解剖病理研究单位，医学与外科系）；Anatomical Pathology Operative Research Unit（解剖病理操作研究单位）；Department of Diagnostics and Intervention, Radiation Physics, Biomedical Engineering（诊断与介入部门，放射物理，生物医学工程）
Explainable Student Performance Prediction With Personalized Attention for Explaining Why A Student Fails
School of Computer Science (National Pilot Software Engineering School)（计算机科学学院（国家示范性软件工程学院））；Beijing University of Posts and Telecommunications（北京邮电大学）
A Path Towards Legal Autonomy: An interoperable and explainable approach to extracting, transforming, loading and computing legal information using large language models, expert systems and Bayesian networks
The University of Sussex（萨塞克斯大学）；Maastricht University（马斯特里赫特大学）；Massachusetts Institute of Technology（麻省理工学院）；VERSES Research Lab（VERSES 研究实验室）；Macquarie University（麦考瑞大学）；Université du Québec à Montréal（蒙特利尔魁北克大学）；University College London（伦敦大学学院）
Flexible categorization using formal concept analysis and Dempster-Shafer theory
Computational Science Lab, University of Amsterdam（阿姆斯特丹大学计算科学实验室）；KPMG（毕马威）；School of Business and Economics, Vrije Universiteit（自由大学商学院与经济学院）；Department of Mathematics and Applied Mathematics, University of Johannesburg（约翰内斯堡大学数学与应用数学系）；College of Business and Economics, University of Johannesburg（约翰内斯堡大学商学院与经济学院）；Faculty of Economics and Business, University of Amsterdam（阿姆斯特丹大学经济与商学院）
SoREX: Towards Self-Explainable Social Recommendation with Relevant Ego-Path Extraction
Gaoling School of Artificial Intelligence, Renmin University of China（中国人民大学 Gallagher人工智能学院）；Beijing Key Laboratory of Research on Large Models（北京大模型研究关键实验室）；Engineering Research Center of Next-Generation Intelligent Search（下一代智能搜索工程研究中心）
Knee-xRAI: An Explainable AI Framework for Automatic Kellgren-Lawrence Grading of Knee Osteoarthritis
Orthopaedic Department, Faculty of Medicine UIN Syarif Hidayatullah Jakarta（乌姆尼大学医学学院骨科部）；Informatics Engineering, Institut Teknologi Sepuluh Nopember（十月份技术研究所信息工程系）；Information Technology, Universitas Muhammadiyah Yogyakarta（尤科阿卡塔大学信息技术系）；Industrial and Systems Engineering, King Fahd University of Petroleum and Minerals（国王法赫德石油与矿物大学工业与系统工程系）
