This paper proposes a decision theory for a symbolic generalization of probability theory (SP). Darwiche and Ginsberg [2,3] proposed SP to relax the requirement of using numbers for uncertainty while preserving desirable patterns of Bayesian reasoning. SP represents uncertainty by symbolic supports that are ordered partially rather than completely as in the case of standard probability. We show that a preference relation on acts that satisfies a number of intuitive postulates is represented by a utility function whose domain is a set of pairs of supports. We argue that a subjective interpretation is as useful and appropriate for SP as it is for numerical probability. It is useful because the subjective interpretation provides a basis for uncertainty elicitation. It is appropriate because we can provide a decision theory that explains how preference on acts is based on support comparison.
End-to-End Neuro-Symbolic Reinforcement Learning with Textual Explanations
School of Intelligence Science and Technology, Peking University（北京大学智能科学与技术学院）；State Key Laboratory of General Artificial Intelligence, BIGAI（北京智源人工智能研究院通用人工智能国家重点实验室）
Neural Decision-Propagation for Answer Set Programming
Vienna University of Technology (TU Wien)（维也纳技术大学（ TU Wien））；National Institute of Informatics（日本信息处理学会）；The Graduate University for Advanced Studies, SOKENDAI（高级研究大学，SOKENDAI）
ReSS: Learning Reasoning Models for Tabular Data Prediction via Symbolic Scaffold
Department of Computer Science & Engineering, Texas A&M University（德克萨斯A&M大学计算机科学与工程系）；Department of Computer Science, University of Florida（佛罗里达大学计算机科学系）
Howard University（霍华德大学）；College of Engineering and Architecture（工程与建筑学院）；DoD Center of Excellence in Artificial Intelligence and Machine Learning（美国国防部人工智能与机器学习卓越中心）；U.S. Army Research Laboratory（美国陆军研究实验室）
Personalized Decision Modeling: Utility Optimization or Textualized-Symbolic Reasoning
Department of Civil and Systems Engineering（土木与系统工程系）；Johns Hopkins University（约翰霍普金斯大学）；Department of Systems and Information Engineering（系统与信息工程系）；University of Virginia（弗吉尼亚大学）；Johns Hopkins Data Science and AI Institute（约翰霍普金斯大学数据科学与人工智能研究所）
Perspective-Shifted Neuro-Symbolic World Models: A Framework for Socially-Aware Robot Navigation
Institute for Systems and Robotics, Instituto Superior Técnico, Universidade de Lisboa（系统机器人研究所，理工学院，里斯本大学）；LAAS-CNRS, Artificial and Natural Intelligence Toulouse Institute (ANITI)（LAAS-CNRS，图卢兹人工智能研究所（ANITI））
Breaking Up with Normatively Monolithic Agency with GRACE: A Reason-Based Neuro-Symbolic Architecture for Safe and Ethical AI Alignment
German Research Center for Artificial Intelligence（德国人工智能研究中心）；Center for European Research in Trusted AI (CERTAIN)（可信AI欧洲研究中心）；Saarland Informatics Campus（萨尔州信息学校区）；Computer Science Department, TU Darmstadt（图宾根大学计算机科学系）
Towards Explainable Neural-Symbolic Visual Reasoning
Segula Technologies（赛古拉技术公司）；Institut des Systèmes Intelligents et de Robotique, Sorbonne Universite（索邦大学智能系统与机器人研究所）；INRIA（法国国家信息与自动化研究所）；ENSTA Paris（巴黎高等先进技术学院）
许多高性能模型都缺乏可解释性。有越来越多的工作涌入可解释的人工智能（XAI），以解开什么是XAI的意思和期望。然而，对于如何产生和判断解释，并没有普遍的共识。在本文中，我们讨论了为什么整合连接主义和符号范式的技术是为非技术用户提供解释的最有效解决方案，并且我们提出了一个基于Doran et al. [2017]（arXiv：1710.00794）定义的推理模型来解释神经网络的决策。我们使用这种解释来纠正网络决策原理中的偏差。我们根据Burns等人[2018]（arXiv：1803.09797）中的图像描述方法，为该模型提供了一个潜在用途的示例。
Neurosymbolic AI for Travel Demand Prediction: Integrating Decision Tree Rules into Neural Networks
Department of Information Systems University of Maryland Baltimore County Baltimore, MD, US（信息系统系马里兰大学巴尔的摩县）；Department of Mechanical Engineering Baylor University Waco,TX, US（机械工程系贝勒大学）
Industrial brain: a human-like autonomous neuro-symbolic cognitive decision-making system
State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing, China（多模态人工智能系统国家重点实验室，自动化研究所，中国科学院，北京，中国）；School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China（人工智能学院，中国科学院大学，北京，中国）；School of Computer Science and Technology, East China Normal University, Shanghai, China（计算机科学与技术学院，华东师范大学，上海，中国）
Dept. of Mathematics and Computer Science University of Ferrara, Italy（费拉拉大学数学与计算机科学系）；Dept. of Mathematical, Physical and Computer Sciences University of Parma, Italy（帕尔马大学数学、物理与计算机科学系）
Automaton Distillation: Neuro-Symbolic Transfer Learning for Deep Reinforcement Learning
University of Texas at San Antonio（德克萨斯大学圣安东尼奥分校）；University of Central Florida（中佛罗里达大学）；Air Force Research Laboratory（空军研究实验室）；Florida International University（佛罗里达国际大学）；University of Colorado（科罗拉多大学）
Toward Auditable Neuro-Symbolic Reasoning in Pathology: SQL as an Explicit Trace of Evidence
Capital Normal University, Beijing, China（首都师范大学）；Leibniz-Institut für Analytische Wissenschaften-ISAS, Dortmund, Germany（莱比锡分析科学研究所-ISAS）；Harbin Institute of Technology, Shenzhen, China（哈尔滨工业大学深圳校区）
A Neuro-Symbolic Framework for Accountability in Public-Sector AI
University of Maryland College Park MD USA；The Hebrew University of Jerusalem \& University of Maryland Jerusalem Israel；University of Maryland；The Hebrew University of Jerusalem \& University of Maryland
