Tangming Yuan

473 citations
30 papers · 335 indexed · h-index 10

Tangming Yuan

26 papers receiving 303 citations

Peers

Tangming Yuan
Comparison fields: 5 of 67
  • Statistics, Probability and Uncertainty 46
  • Software 22
  • Safety, Risk, Reliability and Quality 46
  • Artificial Intelligence 136
  • Industrial and Manufacturing Engineering 41
Replace Guixiang Shen with:
Guixiang Shen China
Bryan M. O’Halloran United States
Thurston Sexton United States
Nga Nguyen France
Armand Toguyéni France
David C. Jensen United States
Siwar Kriaa France
Weixiang Xu China
Sylvain Piechowiak France
Ali K. Raz United States
Tangming Yuan relative to Guixiang Shen China Guixiang Shen's profile →
Citations per field
00.5×
Guixiang Shen · 1×
Citations per year

Countries citing papers authored by Tangming Yuan

Since Specialization
Citations

This map shows the geographic impact of Tangming Yuan's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Tangming Yuan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tangming Yuan more than expected).

Fields of papers citing papers by Tangming Yuan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Tangming Yuan. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Tangming Yuan. The network helps show where Tangming Yuan may publish in the future.

Co-authorship network

The 22 scholars most cited alongside Tangming Yuan, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Tangming Yuan Line = papers co-authored together Tangming Yuan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown
#Work
1 20240
2 20228
3 20225
4 20217
5 20190
6
Reinforcement Learning of Dialogue Coherence and Relevance.
20191
7
Reinforcement Learning for Dialogue Game Based Argumentation.
20195
8 201829
9
Reinforcement Learning for Argumentation: Describing a PhD Research.
20172
10 20141
11 20138
12 20137
13 20124
14 201111
15 201117
16 20112
17 20102
18 20103
19 200823
20 200613

About Tangming Yuan

Tangming Yuan is a scholar working on Statistics, Probability and Uncertainty, Software and Industrial and Manufacturing Engineering, having authored 30 papers that have together received 335 indexed citations. Recurring topics across this work include Multi-Agent Systems and Negotiation (13 papers), Speech and dialogue systems (8 papers), Logic, Reasoning, and Knowledge (5 papers), Topic Modeling (5 papers), Risk and Safety Analysis (4 papers), Natural Language Processing Techniques (4 papers), Software Engineering Research (3 papers) and Safety Systems Engineering in Autonomy (3 papers). The work is most often cited by research in Statistics, Probability and Uncertainty (46 citations), Software (22 citations) and Safety, Risk, Reliability and Quality (46 citations). Tangming Yuan has collaborated with scholars based in United Kingdom, China and United States. Frequent co-authors include Haifeng Wang, Tao Tang, Tianhua Xu, David Moore, Guang Wang, Tim Kelly, Jidong Lv, Yuan Li, Guang Wang and MengChu Zhou. Their work appears in journals such as Expert Systems with Applications, Sensors and Artificial Intelligence.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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