Neural-symbolic computing has now become the subject of interest of both academic and industry research laboratories. Graph Neural Networks (GNN) have been widely used in relational and symbolic domains, with widespread application of GNNs in combinatorial optimization, constraint satisfaction, relational reasoning and other scientific domains. The need for improved explainability, interpretability and trust of AI systems in general demands principled methodologies, as suggested by neural-symbolic computing. In this paper, we review the state-of-the-art on the use of GNNs as a model of neural-symbolic computing. This includes the application of GNNs in several domains as well as its relationship to current developments in neural-symbolic computing.
University of Edinburgh（爱丁堡大学）；Alan Turing Institute（艾伦·图灵研究所）
The tension between deduction and induction is perhaps the most fundamental issue in areas such as philosophy, cognition and artificial intelligence (AI). The deduction camp concerns itself with questions about the expressiveness of formal languages for capturing knowledge about the world, together with proof systems for reasoning from such knowledge bases. The learning camp attempts to generalize from examples about partial descriptions about the world. In AI, historically, these camps have loosely divided the development of the field, but advances in cross-over areas such as statistical rela…
The ability to observe the effects of actions performed by others and to infer their intent, most likely goals, or course of action, is known as a plan or intention recognition cognitive capability and has long been one of the fundamental research challenges in AI. Deep learning has recently been making significant inroads on various pattern recognition problems, except for intention recognition. While extensively explored since the seventies, the problem remains unsolved for most interesting cases in various areas, ranging from natural language understanding to human behavior understanding ba…
Federal University of Rio Grande do Sul（南里奥格兰德联邦大学）
Deep learning has consistently defied state-of-the-art techniques in many fields over the last decade. However, we are just beginning to understand the capabilities of neural learning in symbolic domains. Deep learning architectures that employ parameter sharing over graphs can produce models which can be trained on complex properties of relational data. These include highly relevant NP-Complete problems, such as SAT and TSP. In this work, we showcase how Graph Neural Networks (GNN) can be engineered -- with a very simple architecture -- to solve the fundamental combinatorial problem of graph …
University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Google Research（谷歌研究院）
The neural network has become an integral part of modern software systems. However, they still suffer from various problems, in particular, vulnerability to adversarial attacks. In this work, we present a novel program reasoning framework for neural-network verification, which we refer to as symbolic reasoning. The key components of our framework are the use of the symbolic domain and the quadratic relation. The symbolic domain has very flexible semantics, and the quadratic relation is quite expressive. They allow us to encode many verification problems for neural networks as quadratic program…
Large Language Models Meet Graph Neural Networks: A Perspective of Graph Mining
School of Computer Science and Engineering（计算机科学与工程学院）；University of Electronic Science and Technology of China（电子科技大学）；th Research Institute of CETC（中国电子科技集团公司第五十四研究所）
Tensor Networks Meet Neural Networks: A Survey and Future Perspectives
City University of Hong Kong（香港城市大学）；Harbin Institute of Technology Shenzhen（哈尔滨工业大学（深圳））；Fudan University（复旦大学）；Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area（粤港澳大湾区量子科学中心）；Information Engineering University（信息工程大学）；Imperial College London（伦敦帝国学院）；Systems Research Institute, Polish Academy of Sciences（波兰科学院系统研究所）；Artificial Intelligence Project, Riken（理化学研究所人工智能项目）
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
We propose unifying techniques from probabilistic databases and relational embedding models with the goal of performing complex queries on incomplete and uncertain data. We formalize a probabilistic database model with respect to which all queries are done. This allows us to leverage the rich literature of theory and algorithms from probabilistic databases for solving problems. While this formalization can be used with any relational embedding model, the lack of a well-defined joint probability distribution causes simple query problems to become provably hard. With this in mind, we introduce \…
Samsung Research（三星研究院）；Open University of Cyprus（塞浦路斯开放大学）；Research Center on Interactive Media, Smart Systems, and Emerging Technologies（互动媒体、智能系统与新兴技术研究中心）
Despite significant progress in the development of neural-symbolic frameworks, the question of how to integrate a neural and a symbolic system in a \emph{compositional} manner remains open. Our work seeks to fill this gap by treating these two systems as black boxes to be integrated as modules into a single architecture, without making assumptions on their internal structure and semantics. Instead, we expect only that each module exposes certain methods for accessing the functions that the module implements: the symbolic module exposes a deduction method for computing the function's output on …
CentraleSupelec（巴黎中央理工-高等电力学院）；Université Paris Saclay（巴黎萨克雷大学）；Sorbonne Université（索邦大学）；Université d’Artois（阿尔图瓦大学）
Modal logics have proved useful for many reasoning tasks in symbolic artificial intelligence (AI), such as belief revision, spatial reasoning, among others. On the other hand, mathematical morphology (MM) is a theory for non-linear analysis of structures, that was widely developed and applied in image analysis. Its mathematical bases rely on algebra, complete lattices, topology. Strong links have been established between MM and mathematical logics, mostly modal logics. In this paper, we propose to further develop and generalize this link between mathematical morphology and modal logic from a t…
Understanding the Language Model to Solve the Symbolic Multi-Step Reasoning Problem from the Perspective of Buffer Mechanism
Institute of Natural Sciences, MOE-LSC, Shanghai Jiao Tong University（上海交通大学自然科学院）；School of Mathematical Sciences, Shanghai Jiao Tong University（上海交通大学数学学院）；The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Huawei Noah’s Ark Lab（华为诺亚实验室）；Key Laboratory of Marine Intelligent Equipment and System, Ministry of Education（教育部海洋智能装备与系统重点实验室）
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））
Massachusetts Institute of Technology（麻省理工学院）；The NSF Institute for Artificial Intelligence and Fundamental Interactions（NSF人工智能与基本相互作用研究所）；California Institute of Technology（加州理工学院）
GOFAI meets Generative AI: Development of Expert Systems by means of Large Language Models
Quantitative Methods Department, Comillas Pontifical University Institute of Research in Technology (IIT)（定量方法部门，科利马斯天主教大学技术研究所）；Computer Science Department, ICAI School of Engineering, Comillas Pontifical University（计算机科学系，ICAI工程学院，科利马斯天主教大学）
When 2D Tasks Meet 1D Serialization: On Serialization Friction in Structured Tasks
Northeastern University（东北大学）；University of Pennsylvania（宾夕法尼亚大学）；UC Santa Cruz（加州大学圣克鲁兹分校）；Mila - Quebec AI Institute（魁北克人工智能研究所）；University of Montreal（蒙特利尔大学）；BAIR, UC Berkeley（伯克利大学BAIR实验室）
Recent machine reading comprehension datasets such as ReClor and LogiQA require performing logical reasoning over text. Conventional neural models are insufficient for logical reasoning, while symbolic reasoners cannot directly apply to text. To meet the challenge, we present a neural-symbolic approach which, to predict an answer, passes messages over a graph representing logical relations between text units. It incorporates an adaptive logic graph network (AdaLoGN) which adaptively infers logical relations to extend the graph and, essentially, realizes mutual and iterative reinforcement betwe…
Zhejiang University（浙江大学）；University of Oxford（牛津大学）；The University of Edinburgh（爱丁堡大学）
Zero-shot learning (ZSL) which aims at predicting classes that have never appeared during the training using external knowledge (a.k.a. side information) has been widely investigated. In this paper we present a literature review towards ZSL in the perspective of external knowledge, where we categorize the external knowledge, review their methods and compare different external knowledge. With the literature review, we further discuss and outlook the role of symbolic knowledge in addressing ZSL and other machine learning sample shortage issues.
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Texas A&M University（德州农工大学）
Recent studies on Learning to Optimize (L2O) suggest a promising path to automating and accelerating the optimization procedure for complicated tasks. Existing L2O models parameterize optimization rules by neural networks, and learn those numerical rules via meta-training. However, they face two common pitfalls: (1) scalability: the numerical rules represented by neural networks create extra memory overhead for applying L2O models, and limit their applicability to optimizing larger tasks; (2) interpretability: it is unclear what an L2O model has learned in its black-box optimization rule, nor …
Neural networks have been rapidly expanding in recent years, with novel strategies and applications. However, challenges such as interpretability, explainability, robustness, safety, trust, and sensibility remain unsolved in neural network technologies, despite the fact that they will unavoidably be addressed for critical applications. Attempts have been made to overcome the challenges in neural network computing by representing and embedding domain knowledge in terms of symbolic representations. Thus, the neuro-symbolic learning (NeSyL) notion emerged, which incorporates aspects of symbolic r…
A complete approach to reasoning under uncertainty requires support for incremental and interactive formulation and revision of, as well as reasoning with, models of the problem domain capable of representing our uncertainty. We present a hybrid reasoning scheme which combines symbolic and numeric methods for uncertainty management to provide efficient and effective support for each of these tasks. The hybrid is based on symbolic techniques adapted from Assumption-based Truth Maintenance systems (ATMS), combined with numeric methods adapted from the Dempster/Shafer theory of evidence, as exten…
City, University of London（伦敦大学城市学院）；Federal University of Rio Grande do Sul（南里奥格兰德联邦大学）
Current advances in Artificial Intelligence (AI) and Machine Learning (ML) have achieved unprecedented impact across research communities and industry. Nevertheless, concerns about trust, safety, interpretability and accountability of AI were raised by influential thinkers. Many have identified the need for well-founded knowledge representation and reasoning to be integrated with deep learning and for sound explainability. Neural-symbolic computing has been an active area of research for many years seeking to bring together robust learning in neural networks with reasoning and explainability v…
Politecnico di Torino（都灵理工大学）；University of Edinburgh（爱丁堡大学）；Italian Institute of Technology（意大利理工学院）
Deep learning-based recognition systems are deployed at scale for several real-world applications that inevitably involve our social life. Although being of great support when making complex decisions, they might capture spurious data correlations and leverage sensitive attributes (e.g. age, gender, ethnicity). How to factor out this information while keeping a high prediction performance is a task with still several open questions, many of which are shared with those of the domain adaptation and generalization literature which focuses on avoiding visual domain biases. In this work, we propose…
In the last years, abstract argumentation has met with great success in AI, since it has served to capture several non-monotonic logics for AI. Relations between argumentation framework (AF) semantics and logic programming ones are investigating more and more. In particular, great attention has been given to the well-known stable extensions of an AF, that are closely related to the answer sets of a logic program. However, if a framework admits a small incoherent part, no stable extension can be provided. To overcome this shortcoming, two semantics generalizing stable extensions have been studi…
UAVs Meet LLMs: Overviews and Perspectives Toward Agentic Low-Altitude Mobility
The State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所多模态人工智能系统国家重点实验室）；Macau University of Science and Technology（澳门科技大学）；School of Automation, Beijing Institute of Technology（北京理工大学自动化学院）；School of Software, Northwestern Polytechnical University（西北工业大学软件学院）；College of Mechanical and Vehicle Engineering, Hunan University（湖南大学机械与运载工程学院）；John von Neumann Faculty of Informatics, Obuda University（欧布达大学约翰·冯·诺依曼信息学院）
Mind Meets Space: Rethinking Agentic Spatial Intelligence from a Neuroscience-inspired Perspective
School of EEE, Nanyang Technological University（南洋理工大学电子工程系）；Department of Civil Engineering, Tsinghua University（清华大学土木工程系）；Dr. B. R. Ambedkar National Institute of Technology, Jalandhar（B.R.阿姆贝卡尔国家理工学院，贾兰德赫）；KTH Royal Institute of Technology（皇家理工学院）；College of AI, Tsinghua University（清华大学人工智能学院）；CCDS, Nanyang Technological University（南洋理工大学CCDS）
