4 citations · 19 across the 21 of their papers we have counts for
21 papers
Hierarchical State Abstraction Based on Structural Information Principles
Xianghua Zeng, Hao Peng, Angsheng Li +3
State abstraction optimizes decision-making by ignoring irrelevant environmental information in reinforcement learning with rich observations. Nevertheless, recent approaches focus…
Effective and Stable Role-Based Multi-Agent Collaboration by Structural Information Principles
Xianghua Zeng, Hao Peng, Angsheng Li
Role-based learning is a promising approach to improving the performance of Multi-Agent Reinforcement Learning (MARL). Nevertheless, without manual assistance, current role-based m…
Incremental Measurement of Structural Entropy for Dynamic Graphs
Runze Yang, Hao Peng, Chunyang Liu +1
Structural entropy is a metric that measures the amount of information embedded in graph structure data under a strategy of hierarchical abstracting. To measure the structural entr…
Structural Information Learning Machinery: Learning from Observing, Associating, Optimizing, Decoding, and Abstracting
Angsheng Li
In the present paper, we propose the model of {\it structural information learning machines} (SiLeM for short), leading to a mathematical definition of learning by merging the theo…
The idemetric property: when most distances are (almost) the same
George Barmpalias, Neng Huang, Andrew Lewis-Pye +4
We introduce the \emph{idemetric} property, which formalises the idea that most nodes in a graph have similar distances between them, and which turns out to be quite standard among…
Structure Entropy and Resistor Graphs
Angsheng Li, Yicheng Pan
We propose the notion of {\it resistance of a graph} as an accompanying notion of the structure entropy to measure the force of the graph to resist cascading failure of strategic v…