Jak Kirman
Impact in
- Artificial Intelligence top 5%
- AI-based Problem Solving and Planning
- Logic, Reasoning, and Knowledge
- Reinforcement Learning in Robotics
- Bayesian Modeling and Causal Inference
Papers in
-
- AI-based Problem Solving and Planning 6
- Bayesian Modeling and Causal Inference 4
- Reinforcement Learning in Robotics 2
- Machine Learning and Algorithms 1
- Logic, Reasoning, and Knowledge 1
-
- Constraint Satisfaction and Optimization 2
- Co-authors
- Thomas Dean (3 shared papers)Ann E. Nicholson (2 shared papers)Leslie Pack Kaelbling (2 shared papers)Taraneh Dean (2 shared papers)
- Journals
- Artificial Intelligence (1 paper)IEEE Expert (1 paper)Clark Digital Commons (Clark University) (1 paper)National Conference on Artificial Intelligence (1 paper)
- Partner nations
- United States
In The Last Decade
Jak Kirman
5 papers receiving 251 citations
Peers
Comparison fields: 5 of 41
- Artificial Intelligence 239
- Software 16
- Computational Theory and Mathematics 59
- Management Science and Operations Research 35
- Computer Vision and Pattern Recognition 56
Countries citing papers authored by Jak Kirman
This map shows the geographic impact of Jak Kirman'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 Jak Kirman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jak Kirman more than expected).
Fields of papers citing papers by Jak Kirman
This network shows the impact of papers produced by Jak Kirman. 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 Jak Kirman. The network helps show where Jak Kirman may publish in the future.
Co-authors
The 4 scholars most cited alongside Jak Kirman, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 1995 | 133 | |
| 2 | Planning with deadlines in stochastic domains | 1993 | 122 |
| 3 | 1992 | 27 | |
| 4 | 2002 | 5 | |
| 5 | Predicting Real-Time Planner Preformance by Domain Characterization | 1994 | 4 |
| 6 | Challenges for Theory and Practice in Planning | 1996 | 2 |
About Jak Kirman
Jak Kirman is a scholar working on Artificial Intelligence, Computer Networks and Communications, Computational Theory and Mathematics, Management Science and Operations Research and Infectious Diseases, having authored 6 papers that have together received 293 indexed citations. Recurring topics across this work include AI-based Problem Solving and Planning (6 papers), Bayesian Modeling and Causal Inference (4 papers), Reinforcement Learning in Robotics (2 papers), Constraint Satisfaction and Optimization (2 papers), Machine Learning and Algorithms (1 paper), Formal Methods in Verification (1 paper), Logic, Reasoning, and Knowledge (1 paper) and Complex Systems and Decision Making (1 paper). The work is most often cited by research in Artificial Intelligence (239 citations), Software (16 citations), Computational Theory and Mathematics (59 citations), Management Science and Operations Research (35 citations) and Computer Vision and Pattern Recognition (56 citations). Jak Kirman has collaborated with scholars based in United States. Frequent co-authors include Thomas Dean, Ann E. Nicholson, Leslie Pack Kaelbling and Taraneh Dean. Their work appears in journals such as Artificial Intelligence, IEEE Expert, Clark Digital Commons (Clark University) 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.