Tom Everitt
Impact in
-
- Ethics and Social Impacts of AI
Papers in
-
- Reinforcement Learning in Robotics 6
- Bayesian Modeling and Causal Inference 4
- Logic, Reasoning, and Knowledge 3
- Machine Learning and Algorithms 2
- Evolutionary Algorithms and Applications 2
-
- Computability, Logic, AI Algorithms 7
- Co-authors
- Marcus Hütter (10 shared papers)Eric Langlois (3 shared papers)Ryan M. Carey (6 shared papers)Ben Goertzel (1 shared paper)Shane Legg (1 shared paper)Pedro A. Ortega (1 shared paper)Sebastian Farquhar (2 shared papers)Alexey Potapov (1 shared paper)
- Journals
- Artificial Intelligence (2 papers)Theory and Decision (1 paper)Lecture notes in computer science (8 papers)Studies in systems, decision and control (1 paper)Proceedings of the Python in Science Conferences (1 paper)
- Partner nations
- AustraliaUnited KingdomCanada
In The Last Decade
Tom Everitt
17 papers receiving 109 citations
Peers
Comparison fields: 5 of 57
- Health Informatics 5
- Safety Research 30
- Artificial Intelligence 56
- Computational Theory and Mathematics 21
- Software 4
Countries citing papers authored by Tom Everitt
This map shows the geographic impact of Tom Everitt'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 Tom Everitt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tom Everitt more than expected).
Fields of papers citing papers by Tom Everitt
This network shows the impact of papers produced by Tom Everitt. 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 Tom Everitt. The network helps show where Tom Everitt may publish in the future.
Co-authors
The 18 scholars most cited alongside Tom Everitt, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2018 | 22 | |
| 2 | 2021 | 13 | |
| 3 | 2016 | 12 | |
| 4 | 2023 | 9 | |
| 5 | 2016 | 9 | |
| 6 | 2017 | 8 | |
| 7 | 2015 | 7 | |
| 8 | 2021 | 5 | |
| 9 | 2015 | 5 | |
| 10 | 2016 | 5 | |
| 11 | 2017 | 5 | |
| 12 | 2022 | 4 | |
| 13 | 2015 | 4 | |
| 14 | 2014 | 4 | |
| 15 | 2022 | 3 | |
| 16 | 2021 | 3 | |
| 17 | 2023 | 2 | |
| 18 | Universal Induction and Optimisation: No Free Lunch | 2013 | 1 |
| 19 | 2021 | 0 | |
| 20 | 2024 | 0 |
About Tom Everitt
Tom Everitt is a scholar working on Artificial Intelligence, Computational Theory and Mathematics, Management Science and Operations Research, Safety Research and Software, having authored 21 papers that have together received 121 indexed citations. Recurring topics across this work include Computability, Logic, AI Algorithms (7 papers), Reinforcement Learning in Robotics (6 papers), Bayesian Modeling and Causal Inference (4 papers), Logic, Reasoning, and Knowledge (3 papers), Game Theory and Applications (2 papers), Machine Learning and Algorithms (2 papers), Evolutionary Algorithms and Applications (2 papers) and Ethics and Social Impacts of AI (2 papers). The work is most often cited by research in Health Informatics (5 citations), Safety Research (30 citations), Artificial Intelligence (56 citations), Computational Theory and Mathematics (21 citations) and Software (4 citations). Tom Everitt has collaborated with scholars based in Australia, United Kingdom and Canada. Frequent co-authors include Marcus Hütter, Eric Langlois, Ryan M. Carey, Ben Goertzel, Shane Legg, Pedro A. Ortega, Sebastian Farquhar, Alexey Potapov, Jonathan G. Richens and Ramana Kumar. Their work appears in journals such as Artificial Intelligence, Theory and Decision, Lecture notes in computer science, Studies in systems, decision and control and Proceedings of the Python in Science Conferences.
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.