In recent years, data-intensive AI, particularly the domain of natural language processing and understanding, has seen significant progress driven by the advent of large datasets and deep neural networks that have sidelined more classic AI approaches to the field. These systems can apparently demonstrate sophisticated linguistic understanding or generation capabilities, but often fail to transfer their skills to situations they have not encountered before. We argue that computational situated grounding provides a solution to some of these learning challenges by creating situational representations that both serve as a formal model of the salient phenomena, and contain rich amounts of exploitable, task-appropriate data for training new, flexible computational models. Our model reincorporates some ideas of classic AI into a framework of neurosymbolic intelligence, using multimodal contextual modeling of interactive situations, events, and object properties. We discuss how situated grounding provides diverse data and multiple levels of modeling for a variety of AI learning challenges, including learning how to interact with object affordances, learning semantics for novel structures and configurations, and transferring such learned knowledge to new objects and situations.
Neurosymbolic Framework for Concept-Driven Logical Reasoning in Skeleton-Based Human Action Recognition
Department of ECSE, Faculty of Engineering, Monash University, Australia（莫纳什大学工程学院电子与计算机工程系，澳大利亚）；AIM for Health Lab, Faculty of Information Technology, Monash University, Australia（莫纳什大学信息科技学院健康人工智能实验室，澳大利亚）；Department of DSAI, Faculty of Information Technology, Monash University, Australia（莫纳什大学信息科技学院数据科学与人工智能系，澳大利亚）
Neurosymbolic Graph Enrichment for Grounded World Models
Institute for Cognitive Sciences and Technologies - National Research Council（国家研究委员会认知科学与技术研究所）；Department of Philosophy - University of Bologna（博洛尼亚大学哲学系）
Sound and Complete Neurosymbolic Reasoning with LLM-Grounded Interpretations
University of Amsterdam（阿姆斯特丹大学）；University of Southern California（南加州大学）；Rensselaer Polytechnic Institute（拉特格斯理工学院）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）
IndustryAssetEQA: A Neurosymbolic Operational Intelligence System for Embodied Question Answering in Industrial Asset Maintenance
Artificial Intelligence Institute, University of South Carolina（南卡罗来纳大学人工智能研究所）；University of South Carolina（南卡罗来纳大学）；IBM Yorktown（IBM约克镇分公司）；Indian AI Research Organization（印度人工智能研究组织）
MUSE: Benchmarking Large Vision-Language Models on Multi-Modal Understanding in Situated Education
AI Singapore, National University of Singapore（新加坡国立大学新加坡人工智能）；School of Computing, National University of Singapore（新加坡国立大学计算学院）；Institute of Advanced Intelligence and Computing, A*STAR（新加坡科技研究局先进智能与计算研究所）
Ground-Compose-Reinforce: Grounding Language in Agentic Behaviours using Limited Data
Department of Computer Science, University of Toronto（计算机科学系，多伦多大学）；Vector Institute for Artificial Intelligence（人工智能矢量研究所）；Schwartz Reisman Institute for Technology and Society（技术与社会施瓦茨-雷曼研究所）
A Benchmark for Systematic Generalization in Grounded Language Understanding
University of Amsterdam（阿姆斯特丹大学）；Massachusetts Institute of Technology（麻省理工学院）；ICREA（加泰罗尼亚高等研究院）；Facebook Artificial Intelligence Research（脸书人工智能研究院）；Facebook AI Research（脸书人工智能研究院）；New York University（纽约大学）
Large Language Models in Peer-Run Community Behavioral Health Services: Understanding Peer Specialists and Service Users' Perspectives on Opportunities, Risks, and Mitigation Strategies
School of Computer Science（计算机科学系）；Carnegie Mellon University（卡内基梅隆大学）；Human-Computer Interaction Institute（人机交互研究所）；University of Pittsburgh（匹兹堡大学）；Graduate School of Applied and Professional Psychology（应用与专业心理学研究生院）；Rutgers University（罗格斯大学）；Software and Societal Systems Department（软件与社会系统部门）
