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).
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.
Bacchus, Fahiem & Adam J. Grove. (1999). Looking Forward in Constraint Satisfaction Algorithms.3 indexed citations
4.
Grove, Adam J. & Dale Schuurmans. (1998). Boosting in the limit: maximizing the margin of learned ensembles. National Conference on Artificial Intelligence. 692–699.161 indexed citations
5.
Grove, Adam J. & Dan Roth. (1997). Linear Concepts and Hidden Variables: An Empirical Study. Neural Information Processing Systems. 10. 500–506.2 indexed citations
6.
Bacchus, Fahiem, Craig Boutilier, & Adam J. Grove. (1997). Structured solution methods for non-Markovian decision processes. National Conference on Artificial Intelligence. 112–117.21 indexed citations
Bacchus, Fahiem, Craig Boutilier, & Adam J. Grove. (1996). Rewarding behaviors. National Conference on Artificial Intelligence. 1160–1167.42 indexed citations
9.
Greiner, Russell, Adam J. Grove, & Alexander Kogan. (1996). Exploiting the omission of irrelevant data. International Conference on Machine Learning. 216–224.4 indexed citations
10.
Bacchus, Fahiem & Adam J. Grove. (1996). Utility independence in a qualitative decision theory. Principles of Knowledge Representation and Reasoning. 542–552.34 indexed citations
11.
Greiner, Russell, Adam J. Grove, & Dan Roth. (1996). Learning active classifiers. International Conference on Machine Learning. 207–215.21 indexed citations
Bacchus, Fahiem, Adam J. Grove, Joseph Y. Halpern, & Daphne Koller. (1994). Forming beliefs about a changing world. National Conference on Artificial Intelligence. 222–229.3 indexed citations
16.
Grove, Adam J., Joseph Y. Halpern, & Daphne Koller. (1994). Random Worlds and Maximum Entropy. Journal of Artificial Intelligence Research. 2. 33–88.46 indexed citations
17.
Bacchus, Fahiem, Adam J. Grove, Joseph Y. Halpern, & Daphne Koller. (1993). Statistical foundations for default reasoning. International Joint Conference on Artificial Intelligence. 563–569.33 indexed citations
18.
Grove, Adam J.. (1992). Semantics for Knowledge and Communication.. Principles of Knowledge Representation and Reasoning. 213–224.3 indexed citations
19.
Grove, Adam J. & Joseph Y. Halpern. (1991). Naming and identity in a multi-agent epistemic logic. Principles of Knowledge Representation and Reasoning. 301–312.6 indexed citations
20.
Christensen, Jens & Adam J. Grove. (1991). A formal model for classical planning. International Joint Conference on Artificial Intelligence. 246–251.3 indexed citations
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.