We introduce an online active exploration algorithm for data-efficiently learning an abstract symbolic model of an environment. Our algorithm is divided into two parts: the first part quickly generates an intermediate Bayesian symbolic model from the data that the agent has collected so far, which the agent can then use along with the second part to guide its future exploration towards regions of the state space that the model is uncertain about. We show that our algorithm outperforms random and greedy exploration policies on two different computer game domains. The first domain is an Asteroids-inspired game with complex dynamics but basic logical structure. The second is the Treasure Game, with simpler dynamics but more complex logical structure.
NeuroSymActive: Differentiable Neural-Symbolic Reasoning with Active Exploration for Knowledge Graph Question Answering
University of Macau（澳门大学）；University of Chinese Academy of Sciences（中国科学院大学）；The Australian National University（澳大利亚国立大学）；Juntendo University（静冈大学）；Guangdong Institute of Intelligence Science and Technology（广东智能科学与技术研究院）；Central South University（中南大学）；Southeast University（东南大学）；China Agricultural University（中国农业大学）；Adelaide University（阿德莱德大学）
AI4S-SDS: A Neuro-Symbolic Solvent Design System via Sparse MCTS and Differentiable Physics Alignment
State Key Laboratory for Novel Software Technology at Nanjing University（南京大学新型软件技术国家重点实验室）；School of Computer Science, Nanjing University（南京大学计算机科学学院）；Suzhou Laboratory（苏州实验室）
MINT: Minimal Information Neuro-Symbolic Tree for Objective-Driven Knowledge-Gap Reasoning and Active Elicitation
Department of Electrical and Computer Engineering, George Washington University（乔治华盛顿大学电气与计算机工程系）；Department of Electrical and Computer Engineering, Northeastern University（东北大学电气与计算机工程系）
North Carolina A & T State University（北卡罗来纳农工州立大学）；Clark Altanta University（克拉克亚特兰大大学）
In this paper, we proposed a new clustering-based active learning framework, namely Active Learning using a Clustering-based Sampling (ALCS), to address the shortage of labeled data. ALCS employs a density-based clustering approach to explore the cluster structure from the data without requiring exhaustive parameter tuning. A bi-cluster boundary-based sample query procedure is introduced to improve the learning performance for classifying highly overlapped classes. Additionally, we developed an effective diversity exploration strategy to address the redundancy among queried samples. Our experi…
Learning Soft Robotic Dynamics with Active Exploration
Soft Robotics Lab, D-MAVT, ETH Zurich（软机器人实验室，ETH Zurich）；Learning & Adaptive Systems Group, D-INFK, ETH Zurich（学习与自适应系统组，ETH Zurich）；ETH AI Center, ETH Zurich（ETH人工智能中心，ETH Zurich）
ActPC-Geom: Towards Scalable Online Neural-Symbolic Learning via Accelerating Active Predictive Coding with Information Geometry & Diverse Cognitive Mechanisms
Human Adults and LLMs as Scientists: Who Benefits from Active Exploration?
Mila - Quebec AI Institute（魁北克人工智能研究所）；McGill University（麦吉尔大学）；University of California Berkeley（加州大学伯克利分校）；New York University（纽约大学）；Meta FAIR；MIT Media Lab（麻省理工学院媒体实验室）；Montreal Neurological Institute（蒙特利尔神经科学研究所）
TORE: Token Recycling in Vision Transformers for Efficient Active Visual Exploration
University of Warsaw（华沙大学）；Faculty of Mathematics and Computer Science, Jagiellonian University（雅盖隆大学数学与计算机科学学院）；Ardigen SA（Ardigen SA公司）；Center for Molecular Medicine Cologne (CMMC), University of Cologne（科隆大学科隆分子医学中心）；IDEAS NCBR
