Robotic agents should be able to learn from sub-symbolic sensor data, and at the same time, be able to reason about objects and communicate with humans on a symbolic level. This raises the question of how to overcome the gap between symbolic and sub-symbolic artificial intelligence. We propose a semantic world modeling approach based on bottom-up object anchoring using an object-centered representation of the world. Perceptual anchoring processes continuous perceptual sensor data and maintains a correspondence to a symbolic representation. We extend the definitions of anchoring to handle multi-modal probability distributions and we couple the resulting symbol anchoring system to a probabilistic logic reasoner for performing inference. Furthermore, we use statistical relational learning to enable the anchoring framework to learn symbolic knowledge in the form of a set of probabilistic logic rules of the world from noisy and sub-symbolic sensor input. The resulting framework, which combines perceptual anchoring and statistical relational learning, is able to maintain a semantic world model of all the objects that have been perceived over time, while still exploiting the expressiveness of logical rules to reason about the state of objects which are not directly observed through sensory input data. To validate our approach we demonstrate, on the one hand, the ability of our system to perform probabilistic reasoning over multi-modal probability distributions, and on the other hand, the learning of probabilistic logical rules from anchored objects produced by perceptual observations. The learned logical rules are, subsequently, used to assess our proposed probabilistic anchoring procedure. We demonstrate our system in a setting involving object interactions where object occlusions arise and where probabilistic inference is needed to correctly anchor objects.
Symbolic Graph Networks for Robust PDE Discovery from Noisy Sparse Data
School of Software Engineering, Chengdu University of Information Technology（成都信息科技学院软件工程学院）；School of Statistics, Chengdu University of Information Technology（成都信息科技学院统计学院）；College of Applied Mathematics, Chengdu University of Information Technology（成都信息科技学院应用数学学院）；Key Laboratory of Mathematical Meteorology, Chengdu University of Information Technology（成都信息科技学院数学气象重点实验室）
Noisy Symbolic Abstractions for Deep RL: A case study with Reward Machines
University of Toronto（多伦多大学）；Vector Institute（矢量研究所）；Schwartz Reisman Institute for Technology and Society（施瓦茨·赖斯曼技术与社会研究所）；Pontificia Universidad Católica de Chile（智利天主教大学）；Centro Nacional de Inteligencia Artificial（国家人工智能中心）
ANDRE: An Attention-based Neuro-symbolic Differentiable Rule Extractor for Inductive Logic Programming
Dept. of Mechanical and Aerospace Engineering, George Washington University, USA（机械与航空航天工程系，乔治华盛顿大学）；Dept. of Mechanical Engineering Sciences, University of Surrey, UK（机械工程科学系，萨里大学）
Beihang University（北京航空航天大学）；Beijing Advanced Innovation Center for Big Data and Brain Computing（北京大数据与脑计算高精尖创新中心）；China’s Aviation System Engineering Research Institute（中国航空系统工程研究所）
Disentangling Long-Term Memory via Latent Neuro-Symbolic Reasoning
Tencent Youtu Lab（腾讯优图实验室）；Zhejiang Key Laboratory of Industrial Intelligence and Digital Twin, EIT, Ningbo, China（浙江省工业智能与数字孪生重点实验室）；Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China（香港理工大学计算学系）；Shenzhen Loop Area Institute, Shenzhen, China（深圳市环区研究所）
