Michael Katz

1.8k total citations
82 papers, 1.2k citations indexed

About

Michael Katz is a scholar working on Artificial Intelligence, Computer Networks and Communications and Electrical and Electronic Engineering. According to data from OpenAlex, Michael Katz has authored 82 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 57 papers in Artificial Intelligence, 38 papers in Computer Networks and Communications and 17 papers in Electrical and Electronic Engineering. Recurrent topics in Michael Katz's work include AI-based Problem Solving and Planning (50 papers), Constraint Satisfaction and Optimization (26 papers) and Logic, Reasoning, and Knowledge (24 papers). Michael Katz is often cited by papers focused on AI-based Problem Solving and Planning (50 papers), Constraint Satisfaction and Optimization (26 papers) and Logic, Reasoning, and Knowledge (24 papers). Michael Katz collaborates with scholars based in Israel, Germany and United States. Michael Katz's co-authors include Carmel Domshlak, Shlomo Shamai, Shirin Sohrabi, Octavian Udrea, Jörg Hoffmann, Martin Wehrle, Malte Helmert, Dan Cassel, Edward J. Cragoe and Jöerg Hoffmann and has published in prestigious journals such as Physical Review Letters, Journal of Biological Chemistry and Analytical Chemistry.

In The Last Decade

Michael Katz

76 papers receiving 1.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Michael Katz Israel 19 683 416 352 128 80 82 1.2k
Jing Tao China 16 270 0.4× 261 0.6× 135 0.4× 24 0.2× 198 2.5× 93 940
Bill Lin United States 24 164 0.2× 860 2.1× 733 2.1× 241 1.9× 26 0.3× 116 1.8k
Saurabh Srivastava India 14 280 0.4× 83 0.2× 98 0.3× 220 1.7× 13 0.2× 52 766
Yuqing Lin Australia 15 251 0.4× 225 0.5× 107 0.3× 155 1.2× 24 0.3× 97 799
Shuang Liu China 17 408 0.6× 79 0.2× 136 0.4× 63 0.5× 79 1.0× 74 1.0k
Minseok Kim South Korea 8 178 0.3× 281 0.7× 89 0.3× 23 0.2× 56 0.7× 47 693
Nicholas Hopper United States 24 1.1k 1.7× 1.0k 2.4× 140 0.4× 40 0.3× 173 2.2× 99 1.7k
Jeffrey H. Dinitz United States 16 592 0.9× 153 0.4× 1.0k 3.0× 418 3.3× 47 0.6× 57 1.4k
Deokhwan Kim South Korea 15 227 0.3× 219 0.5× 146 0.4× 33 0.3× 10 0.1× 37 854

Countries citing papers authored by Michael Katz

Since Specialization
Citations

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

Fields of papers citing papers by Michael Katz

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Michael Katz

This figure shows the co-authorship network connecting the top 25 collaborators of Michael Katz. A scholar is included among the top collaborators of Michael Katz 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 Michael Katz. Michael Katz 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.
Srinivas, Kavitha, et al.. (2024). Large Language Models as Planning Domain Generators (Student Abstract). Proceedings of the AAAI Conference on Artificial Intelligence. 38(21). 23604–23605.
2.
Katz, Michael, et al.. (2024). Some Orders Are Important: Partially Preserving Orders in Top-Quality Planning. Proceedings of the International Symposium on Combinatorial Search. 17. 213–217. 2 indexed citations
3.
Katz, Michael, Junkyu Lee, & Shirin Sohrabi. (2024). Unifying and Certifying Top-Quality Planning. Proceedings of the International Conference on Automated Planning and Scheduling. 34. 319–323. 2 indexed citations
4.
Srinivas, Kavitha, et al.. (2024). Large Language Models as Planning Domain Generators. Proceedings of the International Conference on Automated Planning and Scheduling. 34. 423–431. 1 indexed citations
5.
Chakraborti, Tathagata, et al.. (2024). Interactive Plan Selection Using Linear Temporal Logic, Disjunctive Action Landmarks, and Natural Language Instruction. Proceedings of the AAAI Conference on Artificial Intelligence. 38(21). 23775–23777.
6.
Katz, Michael & Junkyu Lee. (2023). K∗ Search over Orbit Space for Top-k Planning. 5368–5376. 2 indexed citations
7.
Behnke, Gregor, David Speck, Michael Katz, & Shirin Sohrabi. (2023). On Partial Satisfaction Planning with Total-Order HTNs. Proceedings of the International Conference on Automated Planning and Scheduling. 33(1). 42–51.
8.
Katz, Michael & Junkyu Lee. (2023). K∗ and Partial Order Reduction for Top-Quality Planning. Proceedings of the International Symposium on Combinatorial Search. 16(1). 143–147. 4 indexed citations
9.
Ma, Tengfei, et al.. (2018). Adaptive Planner Scheduling with Graph Neural Networks.. arXiv (Cornell University). 2 indexed citations
10.
Domshlak, Carmel, et al.. (2012). Landmark-enhanced abstraction heuristics. Artificial Intelligence. 189. 48–68. 11 indexed citations
11.
Katz, Michael, Jörg Hoffmann, & Malte Helmert. (2012). How to relax a bisimulation?. edoc (University of Basel). 8 indexed citations
12.
Domshlak, Carmel, et al.. (2012). Enhanced Symmetry Breaking in Cost-Optimal Planning as Forward Search. Proceedings of the International Conference on Automated Planning and Scheduling. 22. 343–347. 35 indexed citations
13.
Karpas, Erez, Michael Katz, & Shaul Markovitch. (2011). When Optimal Is Just Not Good Enough: Learning Fast Informative Action Cost Partitionings. Proceedings of the International Conference on Automated Planning and Scheduling. 21. 122–129. 8 indexed citations
14.
Katz, Michael & Carmel Domshlak. (2010). Optimal admissible composition of abstraction heuristics. Artificial Intelligence. 174(12-13). 767–798. 56 indexed citations
15.
Katz, Michael & Carmel Domshlak. (2010). Implicit Abstraction Heuristics. Journal of Artificial Intelligence Research. 39. 51–126. 37 indexed citations
16.
Katz, Michael & Carmel Domshlak. (2009). Structural-Pattern Databases. Proceedings of the International Conference on Automated Planning and Scheduling. 19. 186–193. 11 indexed citations
17.
Katz, Michael & Carmel Domshlak. (2008). Structural patterns heuristics via fork decomposition. International Conference on Automated Planning and Scheduling. 9(8). 182–189. 28 indexed citations
18.
Katz, Michael & Carmel Domshlak. (2008). Optimal additive composition of abstraction-based admissible heuristics. International Conference on Automated Planning and Scheduling. 174–181. 43 indexed citations
19.
Katz, Michael & Carmel Domshlak. (2007). Structural patterns of tractable sequentially-optimal planning. International Conference on Automated Planning and Scheduling. 200–207. 12 indexed citations
20.
Katz, Michael & Shlomo Shamai. (2006). Relaying protocols for two colocated users. IEEE Transactions on Information Theory. 52(6). 2329–2344. 42 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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