Lonnie Chrisman
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 10%
- Aerospace Engineering
- Computational Theory and Mathematics top 10%
- Control and Systems Engineering
- Co-authors
- Reid SimmonsFábio Gagliardi CozmanEric KrotkovRichard GoodwinGita KrishnaswamyW. WhittakerMartial HebertSven Koenig
- Topics
- Bayesian Modeling and Causal Inference (4 papers)Logic, Reasoning, and Knowledge (2 papers)Machine Learning and Algorithms (2 papers)
- Cited by
- Artificial IntelligenceComputer Vision and Pattern RecognitionComputational Theory and Mathematics
- Journals
- International Journal of Approximate ReasoningConnection SciencearXiv (Cornell University)
- Partner nations
- United States
In The Last Decade
Lonnie Chrisman
9 papers receiving 301 citations
Peers
Comparison fields: 5 of 58
- Artificial Intelligence 244
- Computer Vision and Pattern Recognition 96
- Aerospace Engineering 62
- Computational Theory and Mathematics 47
- Control and Systems Engineering 41
Countries citing papers authored by Lonnie Chrisman
This map shows the geographic impact of Lonnie Chrisman'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 Lonnie Chrisman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lonnie Chrisman more than expected).
Fields of papers citing papers by Lonnie Chrisman
This network shows the impact of papers produced by Lonnie Chrisman. 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 Lonnie Chrisman. The network helps show where Lonnie Chrisman may publish in the future.
Co-authorship network of co-authors of Lonnie Chrisman
This figure shows the co-authorship network connecting the top 25 collaborators of Lonnie Chrisman. A scholar is included among the top collaborators of Lonnie Chrisman based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Lonnie Chrisman. Lonnie Chrisman is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 7 | |
| 2 | 0 | |
| 3 | 7 | |
| 4 | 60 | |
| 5 | 13 | |
| 6 | 16 | |
| 7 | Reasoning About Probabilistic Actions At Multiple Levels of Granularity | 3 |
| 8 | Reinforcement learning with perceptual aliasing: the perceptual distinctions approach | 197 |
| 9 | Sensible planning: focusing perceptual attention | 20 |
| 10 | 44 |
About Lonnie Chrisman
Lonnie Chrisman is a scholar working on Artificial Intelligence, Numerical Analysis and Computer Vision and Pattern Recognition, having authored 10 papers that have together received 367 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (4 papers), Logic, Reasoning, and Knowledge (2 papers) and Machine Learning and Algorithms (2 papers). The work is most often cited by research in Artificial Intelligence (244 citations), Computer Vision and Pattern Recognition (96 citations) and Computational Theory and Mathematics (47 citations). Lonnie Chrisman has collaborated with scholars based in United States. Frequent co-authors include Reid Simmons, Fábio Gagliardi Cozman, Eric Krotkov, Richard Goodwin, Gita Krishnaswamy, W. Whittaker, Martial Hebert, Sven Koenig, Pat Langley and Stephen D. Bay. Their work appears in journals such as International Journal of Approximate Reasoning, Connection Science and arXiv (Cornell University).
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.