Alexey Ignatiev

1.9k total citations
38 papers, 433 citations indexed

About

Alexey Ignatiev is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Networks and Communications. According to data from OpenAlex, Alexey Ignatiev has authored 38 papers receiving a total of 433 indexed citations (citations by other indexed papers that have themselves been cited), including 34 papers in Artificial Intelligence, 11 papers in Computational Theory and Mathematics and 7 papers in Computer Networks and Communications. Recurrent topics in Alexey Ignatiev's work include Explainable Artificial Intelligence (XAI) (15 papers), Formal Methods in Verification (8 papers) and Machine Learning and Data Classification (7 papers). Alexey Ignatiev is often cited by papers focused on Explainable Artificial Intelligence (XAI) (15 papers), Formal Methods in Verification (8 papers) and Machine Learning and Data Classification (7 papers). Alexey Ignatiev collaborates with scholars based in Australia, Portugal and France. Alexey Ignatiev's co-authors include João Marques‐Silva, António Morgado, Nina Narodytska, Filipe S. Pereira, Peter J. Stuckey, Mikoláš Janota, Alessandro Previti, Alexander Semenov, Martin Cooper and Sam Buss and has published in prestigious journals such as SHILAP Revista de lepidopterología, Artificial Intelligence and IEEE Robotics and Automation Letters.

In The Last Decade

Alexey Ignatiev

36 papers receiving 422 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Alexey Ignatiev Australia 12 368 93 55 53 42 38 433
Du Zhang United States 7 164 0.4× 61 0.7× 57 1.0× 78 1.5× 116 2.8× 11 286
Michael Stepp United States 9 185 0.5× 67 0.7× 49 0.9× 91 1.7× 159 3.8× 11 346
Jieke Shi Singapore 12 209 0.6× 13 0.1× 65 1.2× 96 1.8× 157 3.7× 28 343
Martin Wehrle Switzerland 13 318 0.9× 97 1.0× 81 1.5× 92 1.7× 33 0.8× 34 404
Mingyue Jiang China 11 103 0.3× 28 0.3× 64 1.2× 133 2.5× 96 2.3× 48 303
Vasco Manquinho Portugal 10 128 0.3× 158 1.7× 140 2.5× 73 1.4× 47 1.1× 54 316
Sæmundur Ó. Haraldsson United Kingdom 10 140 0.4× 22 0.2× 41 0.7× 204 3.8× 170 4.0× 23 361
Dilsun Kaynar United States 12 197 0.5× 211 2.3× 129 2.3× 78 1.5× 59 1.4× 24 429
Adriaan Moors Switzerland 9 333 0.9× 89 1.0× 123 2.2× 100 1.9× 122 2.9× 19 433
Dietmar Seipel Germany 8 186 0.5× 40 0.4× 39 0.7× 66 1.2× 125 3.0× 60 291

Countries citing papers authored by Alexey Ignatiev

Since Specialization
Citations

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

Fields of papers citing papers by Alexey Ignatiev

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Alexey Ignatiev

This figure shows the co-authorship network connecting the top 25 collaborators of Alexey Ignatiev. A scholar is included among the top collaborators of Alexey Ignatiev 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 Alexey Ignatiev. Alexey Ignatiev 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.
Li, Boying, Lizhen Qu, Julián Gutiérrez, et al.. (2025). NEUSIS: A Compositional Neuro-Symbolic Framework for Autonomous Perception, Reasoning, and Planning in Complex UAV Search Missions. IEEE Robotics and Automation Letters. 10(9). 9502–9509. 1 indexed citations
2.
Albert, Elvira, et al.. (2024). SuperStack: Superoptimization of Stack-Bytecode via Greedy, Constraint-Based, and SAT Techniques. Proceedings of the ACM on Programming Languages. 8(PLDI). 1437–1462. 1 indexed citations
3.
Ignatiev, Alexey, et al.. (2024). Delivering Inflated Explanations. Proceedings of the AAAI Conference on Artificial Intelligence. 38(11). 12744–12753. 1 indexed citations
4.
Ignatiev, Alexey, et al.. (2023). On computing probabilistic abductive explanations. International Journal of Approximate Reasoning. 159. 108939–108939. 5 indexed citations
5.
Ignatiev, Alexey, et al.. (2023). Eliminating the Impossible, Whatever Remains Must Be True: On Extracting and Applying Background Knowledge in the Context of Formal Explanations. Proceedings of the AAAI Conference on Artificial Intelligence. 37(4). 4123–4131. 3 indexed citations
6.
Marques‐Silva, João & Alexey Ignatiev. (2023). No silver bullet: interpretable ML models must be explained. Frontiers in Artificial Intelligence. 6. 1128212–1128212. 8 indexed citations
7.
Ignatiev, Alexey, et al.. (2022). On Tackling Explanation Redundancy in Decision Trees. Journal of Artificial Intelligence Research. 75. 261–321.
8.
Marques‐Silva, João & Alexey Ignatiev. (2022). Delivering Trustworthy AI through Formal XAI. Proceedings of the AAAI Conference on Artificial Intelligence. 36(11). 12342–12350. 38 indexed citations
9.
Ignatiev, Alexey, et al.. (2022). Using MaxSAT for Efficient Explanations of Tree Ensembles. Proceedings of the AAAI Conference on Artificial Intelligence. 36(4). 3776–3785. 18 indexed citations
10.
Ignatiev, Alexey, et al.. (2021). A Scalable Two Stage Approach to Computing Optimal Decision Sets. Proceedings of the AAAI Conference on Artificial Intelligence. 35(5). 3806–3814. 3 indexed citations
11.
Bonet, Marı́a Luisa, Sam Buss, Alexey Ignatiev, António Morgado, & João Marques‐Silva. (2021). Propositional proof systems based on maximum satisfiability. Artificial Intelligence. 300. 103552–103552. 5 indexed citations
12.
Cabodi, Gianpiero, et al.. (2021). Optimizing Binary Decision Diagrams for Interpretable Machine Learning Classification. HAL (Le Centre pour la Communication Scientifique Directe). 1122–1125. 6 indexed citations
13.
Ignatiev, Alexey, João Marques‐Silva, Nina Narodytska, & Peter J. Stuckey. (2021). Reasoning-Based Learning of Interpretable ML Models. Monash University Research Portal (Monash University). 4458–4465. 8 indexed citations
14.
Ignatiev, Alexey, Nina Narodytska, & João Marques‐Silva. (2019). On relating explanations and adversarial examples. Monash University Research Portal (Monash University). 32. 15857–15867. 34 indexed citations
15.
Ignatiev, Alexey, António Morgado, & João Marques‐Silva. (2019). RC2: an Efficient MaxSAT Solver. 11(1). 53–64. 50 indexed citations
16.
Ignatiev, Alexey, João Marques‐Silva, Carlos Mencía, & Rafael Peñaloza. (2017). Debugging EL+ ontologies through horn MUS enumeration. View. 1879. 54. 2 indexed citations
17.
Ignatiev, Alexey, António Morgado, & João Marques‐Silva. (2017). Cardinality Encodings for Graph Optimization Problems. 652–658. 2 indexed citations
18.
Marques‐Silva, João, Mikoláš Janota, Alexey Ignatiev, & António Morgado. (2015). Efficient model based diagnosis with maximum satisfiability. International Conference on Artificial Intelligence. 1966–1972. 20 indexed citations
19.
Morgado, António, Alexey Ignatiev, & João Marques‐Silva. (2015). MSCG: Robust Core-Guided MaxSAT Solving. 9(1). 129–134. 17 indexed citations
20.
Previti, Alessandro, Alexey Ignatiev, António Morgado, & João Marques‐Silva. (2015). Prime compilation of non-clausal formulae. 1980–1987. 15 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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