activity
20172019
most citedOverview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios

91 citations · 93 across the 3 of their papers we have counts for

collaborators

5 papers

cs.MA2019

MANELA: A Multi-Agent Algorithm for Learning Network Embeddings

Han Zhang, Hong Xu

Playing an essential role in data mining, machine learning has a long history of being applied to networks on multifarious tasks and has played an essential role in data mining. Ho…

cs.LG2018

Learning Embeddings of Directed Networks with Text-Associated Nodes---with Applications in Software Package Dependency Networks

Kexuan Sun, Shudan Zhong, Hong Xu

A network embedding consists of a vector representation for each node in the network. Its usefulness has been shown in many real-world application domains, such as social networks…

cs.AI2018

Overview: A Hierarchical Framework for Plan Generation and Execution in Multi-Robot Systems

Hang Ma, Wolfgang Hönig, Liron Cohen +5

The authors present an overview of a hierarchical framework for coordinating task- and motion-level operations in multirobot systems. Their framework is based on the idea of using…

stat.AP20172 cited

Measuring Territorial Control in Civil Wars Using Hidden Markov Models: A Data Informatics-Based Approach

Therese Anders, Hong Xu, Cheng Cheng +1

Territorial control is a key aspect shaping the dynamics of civil war. Despite its importance, we lack data on territorial control that are fine-grained enough to account for subna…

cs.AI201791 cited

Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios

Hang Ma, Sven Koenig, Nora Ayanian +7

Multi-agent path finding (MAPF) is well-studied in artificial intelligence, robotics, theoretical computer science and operations research. We discuss issues that arise when genera…