Adam J. Grove
- Artificial Intelligence top 1%
- Computational Theory and Mathematics top 2%
- Management Science and Operations Research top 5%
- Computer Vision and Pattern Recognition top 10%
- Computer Networks and Communications top 10%
- Co-authors
- Dale SchuurmansJoseph Y. HalpernFahiem BacchusDaphne KollerDan RothRussell GreinerNick LittlestoneCraig Boutilier
- Topics
- Bayesian Modeling and Causal Inference (14 papers)Logic, Reasoning, and Knowledge (13 papers)Machine Learning and Algorithms (10 papers)
- Cited by
- Artificial IntelligenceComputational Theory and MathematicsManagement Science and Operations Research
- Partner nations
- United StatesCanadaIsrael
In The Last Decade
Adam J. Grove
35 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 68
- Artificial Intelligence 1.2k
- Computational Theory and Mathematics 237
- Management Science and Operations Research 138
- Computer Vision and Pattern Recognition 124
- Computer Networks and Communications 114
Countries citing papers authored by Adam J. Grove
This map shows the geographic impact of Adam J. Grove'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 Adam J. Grove with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Adam J. Grove more than expected).
Fields of papers citing papers by Adam J. Grove
This network shows the impact of papers produced by Adam J. Grove. 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 Adam J. Grove. The network helps show where Adam J. Grove may publish in the future.
Co-authorship network of co-authors of Adam J. Grove
This figure shows the co-authorship network connecting the top 25 collaborators of Adam J. Grove. A scholar is included among the top collaborators of Adam J. Grove 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 Adam J. Grove. Adam J. Grove is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 110 | |
| 3 | Looking Forward in Constraint Satisfaction Algorithms | 3 |
| 4 | Boosting in the limit: maximizing the margin of learned ensembles | 161 |
| 5 | Structured solution methods for non-Markovian decision processes | 21 |
| 6 | Linear Concepts and Hidden Variables: An Empirical Study | 2 |
| 7 | 19 | |
| 8 | Rewarding behaviors | 42 |
| 9 | Learning active classifiers | 21 |
| 10 | Utility independence in a qualitative decision theory | 34 |
| 11 | Exploiting the omission of irrelevant data | 4 |
| 12 | 131 | |
| 13 | 1 | |
| 14 | 17 | |
| 15 | 21 | |
| 16 | Forming beliefs about a changing world | 3 |
| 17 | Statistical foundations for default reasoning | 33 |
| 18 | Semantics for Knowledge and Communication. | 3 |
| 19 | A formal model for classical planning | 3 |
| 20 | Naming and identity in a multi-agent epistemic logic | 6 |
About Adam J. Grove
Adam J. Grove is a scholar working on Artificial Intelligence, General Decision Sciences and Computational Theory and Mathematics, having authored 35 papers that have together received 1.3k indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (14 papers), Logic, Reasoning, and Knowledge (13 papers) and Machine Learning and Algorithms (10 papers). The work is most often cited by research in Artificial Intelligence (1.2k citations), Computational Theory and Mathematics (237 citations) and Management Science and Operations Research (138 citations). Adam J. Grove has collaborated with scholars based in United States, Canada and Israel. Frequent co-authors include Dale Schuurmans, Joseph Y. Halpern, Fahiem Bacchus, Daphne Koller, Dan Roth, Russell Greiner, Nick Littlestone, Craig Boutilier, Daphne Koller and Alexander Kogan. Their work appears in journals such as Pattern Recognition, Artificial Intelligence and Machine Learning.
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.