William Uther

472 citations
19 papers · 314 · h-index 9

Impact in

Papers in

William Uther

18 papers receiving 279 citations

Peers

William Uther
Comparison fields: 5 of 52
  • Artificial Intelligence 198
  • Computer Vision and Pattern Recognition 76
  • Computational Theory and Mathematics 39
  • Control and Systems Engineering 42
  • Software 7
Replace Lonnie Chrisman with:
Lonnie Chrisman United States
Juan Carlos Santamaria United States
M. Gethsiyal Augasta India
Henryk Michalewski Poland
Tae Jong Choi South Korea
Sean P. Engelson Israel
Zhang-Wei Hong Taiwan
Qing Wei China
Noah Siegel United Kingdom
Anthony José Da Cunha Carneiro Lins Brazil
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Citations per field
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Citations per year

Countries citing papers authored by William Uther

Since Specialization
Citations

This map shows the geographic impact of William Uther'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 William Uther with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites William Uther more than expected).

Fields of papers citing papers by William Uther

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by William Uther. 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 William Uther. The network helps show where William Uther may publish in the future.

Co-authors

The 20 scholars most cited alongside William Uther, 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 William Uther Line = papers co-authored together William Uther links everyone, so they are left out of the graph.

All Works

19 of 19 papers shown
#Work
1
Tree based discretization for continuous state space reinforcement learning
1998109
2
Automatic Gait Optimisation for Quadruped Robots
200346
3 200232
4 200221
5 199919
6 200813
7
Tree based hierarchical reinforcement learning
200213
8 200912
9
Vision, Strategy, and Localization Using the Sony Robots at RoboCup-98
200012
10 20007
11
Approximate Dynamic Programming with Affine ADDs
20107
12 20025
13 20135
14
A Description of the rUNSWift 2003 Legged Robot Soccer Team
20035
15
Motivated agents
20053
16 20003
17 20061
18 20071
19 20030

About William Uther

William Uther is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Control and Systems Engineering, Aerospace Engineering and Computational Theory and Mathematics, having authored 19 papers that have together received 314 indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (7 papers), Robotic Locomotion and Control (5 papers), Bayesian Modeling and Causal Inference (4 papers), Robotics and Sensor-Based Localization (4 papers), Robotic Path Planning Algorithms (4 papers), AI-based Problem Solving and Planning (3 papers), Evolutionary Algorithms and Applications (3 papers) and Rough Sets and Fuzzy Logic (2 papers). The work is most often cited by research in Artificial Intelligence (198 citations), Computer Vision and Pattern Recognition (76 citations), Computational Theory and Mathematics (39 citations), Control and Systems Engineering (42 citations) and Software (7 citations). William Uther has collaborated with scholars based in United States, Australia and France. Frequent co-authors include Manuela Veloso, Hiroaki Kitano, Minoru Asada, Kee Siong Ng, John Wylie Lloyd, Manuela Veloso, Bernhard Hengst, James Bruce, Scott R. Lenser and Scott Sanner. Their work appears in journals such as Journal of Applied Logic, Lecture notes in computer science, Annals of Mathematics and Artificial Intelligence, Contact-less Assessment of In-vivo Body Signals Using Microwave Doppler Radar (InTech) and National Conference on 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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