A general parsing algorithm with context matching for context-sensitive graph grammars

2021 ◽  
Author(s):  
Yang Zou ◽  
Xiaoqin Zeng ◽  
Yun Zhu
Keyword(s):  
Graph Grammars ◽  
Context Sensitive ◽  
Parsing Algorithm

On the Expressiveness of Context-Sensitive Graph Grammars

2012 ◽  
Vol 23 (7) ◽  
pp. 1635-1655 ◽  
Author(s):  
Yang ZOU ◽  
Jian LÜ ◽  
Chun CAO ◽  
Hao HU ◽  
Wei SONG ◽  
...  
Keyword(s):  
Graph Grammars ◽  
Context Sensitive

scholarly journals Research on the Top-Down Parsing Method for Context-Sensitive Graph Grammars

PLoS ONE ◽  
2015 ◽  
Vol 10 (11) ◽  
pp. e0142776
Author(s):  
Yi Wang ◽  
XiaoQin Zeng ◽  
Han Ding
Keyword(s):  
Graph Grammars ◽  
Top Down ◽  
Context Sensitive

Constructing Confluent Context-sensitive Graph Grammars from Non-confluent Productions for Parsing Efficiency

2009 ◽  
pp. 135-147 ◽  
Author(s):  
Yang Zou ◽  
Jian Lü ◽  
Xiaoqin Zeng ◽  
Xiaoxing Ma ◽  
Qiliang Yang
Keyword(s):  
Graph Grammars ◽  
Context Sensitive

Approach to Context Computation for Implicit Context-sensitive Graph Grammars

2019 ◽  
Vol 2019 (1) ◽  
pp. 15-28
Author(s):  
Yang Zou ◽  
Xiaoqin Zeng ◽  
Yufeng Liu ◽  
Huiyi Liu
Keyword(s):  
Graph Grammars ◽  
Context Sensitive

An Automated Virtual Lab for Bond Graph Based Dynamics Modeling Using Graph Grammars and Tree Search

2016 ◽  
Author(s):  
Felice Mancini ◽  
Daniel Grande ◽  
Pradeep Radhakrishnan
Keyword(s):  
Graph Grammar ◽  
Tree Search ◽  
Graph Grammars ◽  
Dynamics Modeling ◽  
Bond Graphs ◽  
Grammar Rule ◽  
Context Sensitive ◽  
Rule Based System ◽  
Research Activities ◽  
Virtual Lab

This paper explores the concept of an automated virtual lab in the area of system design and analysis. The project combines different research activities in automated design analysis using the graph grammar and tree search methods. In particular, a graph grammar rule-based system to automatically generate bond graphs for various systems is developed. This is combined with similar grammar based rules and search algorithms to provide automation as well as context sensitive feedback to users of the virtual lab. Examples will be demonstrated to showcase the potential as well as how the virtual lab can be scaled using appropriate learning algorithms towards personalizing education.

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