Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Countries citing papers authored by Maxim Likhachev
Since
Specialization
Citations
This map shows the geographic impact of Maxim Likhachev'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 Maxim Likhachev with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maxim Likhachev more than expected).
This network shows the impact of papers produced by Maxim Likhachev. 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 Maxim Likhachev. The network helps show where Maxim Likhachev may publish in the future.
Co-authorship network of co-authors of Maxim Likhachev
This figure shows the co-authorship network connecting the top 25 collaborators of Maxim Likhachev.
A scholar is included among the top collaborators of Maxim Likhachev 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 Maxim Likhachev. Maxim Likhachev is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Phillips, Mike, Venkatraman Narayanan, Sandip Aine, & Maxim Likhachev. (2015). Efficient search with an ensemble of heuristics. International Conference on Artificial Intelligence. 784–791.19 indexed citations
12.
Aine, Sandip, et al.. (2014). Multi-Heuristic A*.12 indexed citations
13.
Normoyle, Aline, John H. Drake, Maxim Likhachev, & Alla Safonova. (2012). Game-Based Data Capture for Player Metrics. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. 8(1). 44–50.5 indexed citations
14.
Ferguson, Dave & Maxim Likhachev. (2008). Efficiently Using Cost Maps For Planning Complex Maneuvers. Scholarly Commons (University of Pennsylvania).18 indexed citations
15.
Likhachev, Maxim & Sven Koenig. (2006). Incremental Heuristic Search in Games: The Quest for Speed. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. 2(1). 118–120.1 indexed citations
16.
Koenig, Sven & Maxim Likhachev. (2006). A new principle for incremental heuristic search: theoretical results. International Conference on Automated Planning and Scheduling. 402–405.16 indexed citations
17.
Likhachev, Maxim, Sebastian Thrun, & Geoffrey J. Gordon. (2004). Planning for Markov Decision Processes with Sparse Stochasticity. Neural Information Processing Systems. 17. 785–792.17 indexed citations
18.
Likhachev, Maxim, Geoffrey J. Gordon, & Sebastian Thrun. (2003). ARA*: Anytime A* with Provable Bounds on Sub-Optimality. Neural Information Processing Systems. 16. 767–774.449 indexed citations
19.
Koenig, Sven & Maxim Likhachev. (2002). D*lite. National Conference on Artificial Intelligence. 476–483.312 indexed citations
20.
Koenig, Sven & Maxim Likhachev. (2001). Incremental A. Neural Information Processing Systems. 14. 1539–1546.75 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.