Daniel Kudenko⋆

3.2k citations
160 papers · 2.2k · h-index 26

Impact in

Papers in

    • Reinforcement Learning in Robotics 58
    • Evolutionary Algorithms and Applications 23
    • Artificial Intelligence in Games 22
    • Multi-Agent Systems and Negotiation 11
    • Metaheuristic Optimization Algorithms Research 7
    • AI-based Problem Solving and Planning 7

Daniel Kudenko⋆

152 papers receiving 2.1k citations

Peers

Daniel Kudenko⋆
Comparison fields: 5 of 122
  • Artificial Intelligence 1.4k
  • Management Science and Operations Research 298
  • Computer Networks and Communications 431
  • Computational Theory and Mathematics 244
  • Signal Processing 152
Replace Matteo Gaeta with:
Matteo Gaeta Italy
Simon Colton United Kingdom
Steve Hanks United States
Neil Yorke‐Smith Netherlands
David V. Pynadath United States
Mark d’Inverno United Kingdom
Guy Shani Israel
Eugénio Oliveira Portugal
John R. Josephson United States
Daniel Kudenko⋆ relative to Matteo Gaeta Italy Matteo Gaeta's profile →
Citations per field
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Matteo Gaeta · 1×
Citations per year

Countries citing papers authored by Daniel Kudenko⋆

Since Specialization
Citations

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

Fields of papers citing papers by Daniel Kudenko⋆

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Kudenko⋆. 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 Daniel Kudenko⋆. The network helps show where Daniel Kudenko⋆ may publish in the future.

Co-authors

The 25 scholars most cited alongside Daniel Kudenko⋆, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel Kudenko⋆ Line = papers co-authored together Daniel Kudenko⋆ links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 160 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2002135
2 2001116
3 201793
4 201279
5 201559
6 201157
7 200855
8 201549
9 201149
10 201448
11 200348
12 201047
13 200747
14 201445
15 200841
16 201039
17 200736
18 201935
19 200433
20
Feature generation for sequence categorization
199827

About Daniel Kudenko⋆

Daniel Kudenko⋆ is a scholar working on Artificial Intelligence, Computer Networks and Communications, Information Systems, Management Science and Operations Research and Sociology and Political Science, having authored 160 papers that have together received 2.2k indexed citations. Recurring topics across this work include Reinforcement Learning in Robotics (58 papers), Evolutionary Algorithms and Applications (23 papers), Artificial Intelligence in Games (22 papers), Digital Games and Media (12 papers), Multi-Agent Systems and Negotiation (11 papers), Educational Games and Gamification (9 papers), Metaheuristic Optimization Algorithms Research (7 papers) and AI-based Problem Solving and Planning (7 papers). The work is most often cited by research in Artificial Intelligence (1.4k citations), Management Science and Operations Research (298 citations), Computer Networks and Communications (431 citations), Computational Theory and Mathematics (244 citations) and Signal Processing (152 citations). Daniel Kudenko⋆ has collaborated with scholars based in United Kingdom, Germany and United States. Frequent co-authors include Sam Devlin, Marek Grześ, Kleanthis Malialis, Eduardo Alonso, Dimitar Kazakov, Peter Cowling, Chris Kimble, I‐Hsien Ting, G. Flucke and Jason Noble. Their work appears in journals such as The Knowledge Engineering Review, Lecture notes in computer science, Neural Computing and Applications, Connection Science and User Modeling and User-Adapted Interaction.

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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