Akihiro Kishimoto

2.0k total citations
82 papers, 984 citations indexed

About

Akihiro Kishimoto is a scholar working on Artificial Intelligence, Computer Networks and Communications and Electrical and Electronic Engineering. According to data from OpenAlex, Akihiro Kishimoto has authored 82 papers receiving a total of 984 indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Artificial Intelligence, 15 papers in Computer Networks and Communications and 14 papers in Electrical and Electronic Engineering. Recurrent topics in Akihiro Kishimoto's work include Artificial Intelligence in Games (20 papers), AI-based Problem Solving and Planning (11 papers) and Molten salt chemistry and electrochemical processes (9 papers). Akihiro Kishimoto is often cited by papers focused on Artificial Intelligence in Games (20 papers), AI-based Problem Solving and Planning (11 papers) and Molten salt chemistry and electrochemical processes (9 papers). Akihiro Kishimoto collaborates with scholars based in Japan, Ireland and Canada. Akihiro Kishimoto's co-authors include Martin Müller, Jonathan Schaeffer, Adi Botea, Yngvi Björnsson, Tetsuya Uda, Steve Sutphen, Neil Burch, Robert W. Lake, Paul Lu and Alex Fukunaga and has published in prestigious journals such as Science, SHILAP Revista de lepidopterología and Applied Physics Letters.

In The Last Decade

Akihiro Kishimoto

72 papers receiving 909 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Akihiro Kishimoto Japan 17 498 186 140 131 127 82 984
Jian An China 19 219 0.4× 173 0.9× 48 0.3× 57 0.4× 7 0.1× 85 922
Cong Li China 15 155 0.3× 696 3.7× 104 0.7× 87 0.7× 14 0.1× 108 1.3k
Lei Jin China 22 429 0.9× 380 2.0× 220 1.6× 6 0.0× 71 0.6× 170 1.8k
Ke Hu China 20 302 0.6× 54 0.3× 71 0.5× 26 0.2× 8 0.1× 115 1.1k
Amit Chakrabarti United States 22 450 0.9× 235 1.3× 74 0.5× 9 0.1× 14 0.1× 70 1.6k
Xuebo Chen China 16 97 0.2× 146 0.8× 206 1.5× 17 0.1× 12 0.1× 205 1.2k
Peng Shi China 16 160 0.3× 56 0.3× 86 0.6× 25 0.2× 9 0.1× 100 814
Wenbin Yu China 19 97 0.2× 321 1.7× 64 0.5× 55 0.4× 13 0.1× 94 1.0k

Countries citing papers authored by Akihiro Kishimoto

Since Specialization
Citations

This map shows the geographic impact of Akihiro Kishimoto'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 Akihiro Kishimoto with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Akihiro Kishimoto more than expected).

Fields of papers citing papers by Akihiro Kishimoto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Akihiro Kishimoto. 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 Akihiro Kishimoto. The network helps show where Akihiro Kishimoto may publish in the future.

Co-authorship network of co-authors of Akihiro Kishimoto

This figure shows the co-authorship network connecting the top 25 collaborators of Akihiro Kishimoto. A scholar is included among the top collaborators of Akihiro Kishimoto 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 Akihiro Kishimoto. Akihiro Kishimoto is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Takeda, Seiji, et al.. (2023). Foundation Model for Material Science. Proceedings of the AAAI Conference on Artificial Intelligence. 37(13). 15376–15383. 8 indexed citations
2.
Fokoue, Achille, Ibrahim Abdelaziz, Shajith Ikbal, et al.. (2023). An Ensemble Approach for Automated Theorem Proving Based on Efficient Name Invariant Graph Neural Representations. 3221–3229. 1 indexed citations
3.
Palmes, Paulito, Akihiro Kishimoto, Radu Marinescu, Parikshit Ram, & Elizabeth Daly. (2023). Designing Machine Learning Pipeline Toolkit for AutoML Surrogate Modeling Optimization. 1(1). 129–129. 1 indexed citations
4.
Giro, Ronaldo, Akihiro Kishimoto, Rodrigo Neumann Barros Ferreira, et al.. (2023). AI powered, automated discovery of polymer membranes for carbon capture. npj Computational Materials. 9(1). 20 indexed citations
5.
Hayashi-Nishino, Mitsuko, K. Aoki, Akihiro Kishimoto, et al.. (2022). Identification of Bacterial Drug-Resistant Cells by the Convolutional Neural Network in Transmission Electron Microscope Images. Frontiers in Microbiology. 13. 839718–839718. 7 indexed citations
6.
Kishimoto, Akihiro, et al.. (2020). Low Temperature Electrodeposition of Titanium in Fluoride-Added LiCl–KCl–CsCl Molten Salt. MATERIALS TRANSACTIONS. 61(8). 1651–1656. 6 indexed citations
7.
Kishimoto, Akihiro, Beat Buesser, Bei Chen, & Adi Botea. (2019). Depth-First Proof-Number Search with Heuristic Edge Cost and Application to Chemical Synthesis Planning. Neural Information Processing Systems. 32. 7224–7234. 13 indexed citations
8.
Kai, Reo, et al.. (2017). Validity of a LES/flamelet approach to a transcritical O2/H2 jet flame. Bulletin of the American Physical Society. 1 indexed citations
9.
Kishimoto, Akihiro, Radu Marinescu, & Adi Botea. (2015). Parallel recursive best-first AND/OR search for exact MAP inference in graphical models. Neural Information Processing Systems. 28. 928–936.
10.
Kishimoto, Akihiro & Radu Marinescu. (2014). Recursive best-first AND/OR search for optimization in graphical models. Uncertainty in Artificial Intelligence. 400–409. 5 indexed citations
11.
Kishimoto, Akihiro, Alex Fukunaga, & Adi Botea. (2012). Evaluation of a simple, scalable, parallel best-first search strategy. Artificial Intelligence. 195. 222–248. 25 indexed citations
12.
Kobayashi, Yoshikazu, Akihiro Kishimoto, & Osamu Watanabe. (2011). Evaluations of hash distributed A* in optimal sequence alignment. International Joint Conference on Artificial Intelligence. 584–590. 9 indexed citations
13.
Kishimoto, Akihiro & Nathan Sturtevant. (2008). Optimized algorithms for multi-agent routing. Adaptive Agents and Multi-Agents Systems. 1585–1588. 9 indexed citations
14.
Yoshizoe, Kazuki, Akihiro Kishimoto, & Martin Müller. (2007). Lambda depth-first proof number search and its application to go. International Joint Conference on Artificial Intelligence. 44(3). 2404–2409. 18 indexed citations
15.
Yoshizoe, Kazuki, et al.. (2006). Monte Carlo go has a way to go. National Conference on Artificial Intelligence. 7(4). 1070–1075. 10 indexed citations
16.
Schaeffer, Jonathan, Yngvi Björnsson, Neil Burch, et al.. (2005). Solving checkers. International Joint Conference on Artificial Intelligence. 292–297. 22 indexed citations
17.
Kishimoto, Akihiro & Martin Müller. (2005). Search versus knowledge for solving life and death problems in Go. National Conference on Artificial Intelligence. 1374–1379. 13 indexed citations
18.
Kishimoto, Akihiro & Martin Müller. (2005). Dynamic Decomposition Search: A Divide and Conquer Approach and its Application to the One-Eye Problem in Go.. 3 indexed citations
19.
Kishimoto, Akihiro. (2005). Correct and efficient search algorithms in the presence of repetitions. University of Alberta Library. 9 indexed citations
20.
Kishimoto, Akihiro & Martin Müller. (2004). A general solution to the graph history interaction problem. National Conference on Artificial Intelligence. 644–649. 20 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.

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