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.
Transfer Learning for Reinforcement Learning Domains: A Survey
2009902 citationsMatthew E. Taylor, Peter StoneJournal of Machine Learning Researchprofile →
A Multiagent Approach to Autonomous Intersection Management
2008888 citationsKurt Dresner, Peter Stoneprofile →
Multiagent Systems: A Survey from a Machine Learning Perspective
This map shows the geographic impact of Peter Stone'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 Peter Stone with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter Stone more than expected).
This network shows the impact of papers produced by Peter Stone. 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 Peter Stone. The network helps show where Peter Stone may publish in the future.
Co-authorship network of co-authors of Peter Stone
This figure shows the co-authorship network connecting the top 25 collaborators of Peter Stone.
A scholar is included among the top collaborators of Peter Stone 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 Peter Stone. Peter Stone is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Sharon, Guni, et al.. (2016). Delta-Tolling: Adaptive Tolling for Optimizing Traffic Throughput.. International Joint Conference on Artificial Intelligence.5 indexed citations
5.
Sinapov, Jivko, et al.. (2016). Learning to order objects using haptic and proprioceptive exploratory behaviors. International Joint Conference on Artificial Intelligence. 3462–3468.18 indexed citations
6.
Thomason, Jesse, et al.. (2016). Learning multi-modal grounded linguistic semantics by playing I Spy. International Joint Conference on Artificial Intelligence. 3477–3483.39 indexed citations
7.
Hausknecht, Matthew & Peter Stone. (2015). The Impact of Determinism on Learning Atari 2600 Games. National Conference on Artificial Intelligence.11 indexed citations
Taylor, Matthew E. & Peter Stone. (2009). Transfer Learning for Reinforcement Learning Domains: A Survey. Journal of Machine Learning Research. 10(56). 1633–1685.902 indexed citations breakdown →
Dresner, Kurt & Peter Stone. (2007). Sharing the road: autonomous vehicles meet human drivers. International Joint Conference on Artificial Intelligence. 1263–1268.90 indexed citations
Pardoe, David & Peter Stone. (2006). TacTex-05: a champion supply chain management agent. National Conference on Artificial Intelligence. 1489–1494.14 indexed citations
16.
Sridharan, Mohan & Peter Stone. (2005). Autonomous color learning on a mobile robot. National Conference on Artificial Intelligence. 1318–1323.10 indexed citations
Stone, Peter & Richard S. Sutton. (2001). Scaling Reinforcement Learning toward RoboCup Soccer. International Conference on Machine Learning. 537–544.101 indexed citations
Stone, Peter & Manuela Veloso. (1996). User-guided interleaving of planning and execution. IOS Press eBooks. 103–112.16 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.