Results 51 to 60 of about 8,113 (297)

Scalable Task Planning via Large Language Models and Structured World Representations

Advanced Robotics Research, EarlyView.
This work efficiently combines graph‐based world representations with the commonsense knowledge in Large Language Models to enhance planning techniques for the large‐scale environments that modern robots will need to face. Planning methods often struggle with computational intractability when solving task‐level problems in large‐scale environments ...
Rodrigo Pérez‐Dattari, Zhaoting Li, Robert Babuška, Jens Kober, Cosimo Della Santina   +4 more
wiley   +1 more source

Temporal knowledge graph reasoning using global and recent history information

Scientific Reports
Since the static Knowledge Graph cannot meet the dynamics of knowledge in the real world, Temporal Knowledge Graph has become a potential method for processing temporal knowledge.
Changlong Wang, Jianlong Cao, Wenzheng Guo, Jie Hu, Yaoyao Hu, Yi Liu, Yawei Li, Huimei Tian, Pengjuan Lu, Wenhao Zhao, Zhixiong Yin   +10 more
doaj   +1 more source

Construction and application of integrated knowledge graph for mine disasters

Meikuang Anquan
In order to achieve a novel disaster early warning mode of “autonomous modeling + integrated early warning + root cause tracing”, and improve the knowledge engineering infrastructure for integrated intelligent disaster warning, this study developed an ...
Yabo HE
doaj   +1 more source

JOINT FEATURES-BASED KNOWLEDGE GRAPH COMPLETION [PDF]

International Journal of Intelligent Computing and Information Sciences
Knowledge graphs (KGs) help in resolving data inconsistencies and redundancies by organizing information in a unified structure, paving the way for building scalable, interpretable AI systems, as they provide a transparent way to trace reasoning paths ...
Maha Farghaly, Mostafa Aref, Mahmoud Mounir   +2 more
doaj   +1 more source

Grounding Large Language Models for Robot Task Planning Using Closed‐Loop State Feedback

Advanced Robotics Research, EarlyView.
BrainBody‐Large Language Model (LLM) introduces a hierarchical, feedback‐driven planning framework where two LLMs coordinate high‐level reasoning and low‐level control for robotic tasks. By grounding decisions in real‐time state feedback, it reduces hallucinations and improves task reliability.
Vineet Bhat, Ali Umut Kaypak, Prashanth Krishnamurthy, Ramesh Karri, Farshad Khorrami   +4 more
wiley   +1 more source

Optimal temporal planning using the plangraph framework [PDF]

, 2007
The past few years have seen a rapid development in AI Planning and Scheduling. Many algorithms and techniques have been studied and improved to deal with more complex and difficult planning domains.
Dinh, Tien Ba
core  

Temporal Knowledge Graph Reasoning Based on Entity Relationship Similarity Perception [PDF]

Temporal knowledge graphs (TKGs) are used for dynamically modeling facts in the temporal dimension, and are widely used in various fields. However, existing reasoning models often fail to consider the similarity features between entity relationships and ...
Xunyang Ji, Mengxing Huang, Cong Zhou, Siling Feng, Qian Liu   +4 more
core   +1 more source

Context-Aware Knowledge Graph Learning for Point-of-Interest Recommendation

ISPRS International Journal of Geo-Information
Existing point-of-interest (POI) recommendation methods often fail to capture complex contextual dependencies and suffer from severe data sparsity in location-based social networks (LBSNs).
Yan Zhou, Di Zhang, Kaixuan Zhou, Pengcheng Han   +3 more
doaj   +1 more source

LLM‐Integrated Human–Robot Interaction System for Microrobots

Advanced Robotics Research, EarlyView.
This paper proposes an LLM‐based control framework for guiding microrobots using human natural language. This framework can convert the natural human speech into safe and executable command sets for reliable navigation in complex environments. The experimental results show high accuracy and robustness in task performance, demonstrating the potential of
Bairong Zhu, Amar Salehi, Tingting Yu
wiley   +1 more source

Intelligent Maintenance Review for Robots: Multimodal Information, Deep Diagnosis and Embodied Artificial Intelligence

Advanced Robotics Research, EarlyView.
This review maps the methods to monitor robots’ health by fusing vibration, sound, control signals, vision, force, and oil information with artificial intelligence. It identifies deep learning, transfer learning, digital twins, and physics‐informed models as key methodological pathways enabling earlier diagnosis, safer human–robot collaboration, and ...
Yuting Qiao, Hongbo Wang, Naipeng Li, Shuo Wang, Yaguo Lei, Tonghai Wu, Junyi Cao   +6 more
wiley   +1 more source