Daniel Kudenko⋆

3.2k total citations
116 papers, 1.5k citations indexed

About

Daniel Kudenko⋆ is a scholar working on Artificial Intelligence, Information Systems and Management Science and Operations Research. According to data from OpenAlex, Daniel Kudenko⋆ has authored 116 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 82 papers in Artificial Intelligence, 18 papers in Information Systems and 16 papers in Management Science and Operations Research. Recurrent topics in Daniel Kudenko⋆'s work include Reinforcement Learning in Robotics (42 papers), Artificial Intelligence in Games (15 papers) and Evolutionary Algorithms and Applications (12 papers). Daniel Kudenko⋆ is often cited by papers focused on Reinforcement Learning in Robotics (42 papers), Artificial Intelligence in Games (15 papers) and Evolutionary Algorithms and Applications (12 papers). Daniel Kudenko⋆ collaborates with scholars based in United Kingdom, Germany and United States. Daniel Kudenko⋆'s co-authors include Sam Devlin, Marek Grześ, Peter Cowling, Eduardo Alonso, Chris Kimble, G. Flucke, Mark d’Inverno, Jason Noble, I‐Hsien Ting and Ignazio Cabras and has published in prestigious journals such as Journal of Business Research, Computers in Human Behavior and Expert Systems with Applications.

In The Last Decade

Daniel Kudenko⋆

110 papers receiving 1.4k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Daniel Kudenko⋆ United Kingdom 21 883 264 215 197 191 116 1.5k
Dave Cliff United Kingdom 21 753 0.9× 259 1.0× 234 1.1× 367 1.9× 161 0.8× 86 1.8k
Matteo Gaeta Italy 24 840 1.0× 270 1.0× 496 2.3× 119 0.6× 120 0.6× 154 1.9k
Mehdi Dastani Netherlands 22 1.6k 1.8× 330 1.3× 252 1.2× 227 1.2× 196 1.0× 178 2.1k
Nathan Griffiths United Kingdom 16 609 0.7× 226 0.9× 274 1.3× 247 1.3× 330 1.7× 103 1.6k
David V. Pynadath United States 22 1.3k 1.4× 399 1.5× 107 0.5× 313 1.6× 220 1.2× 62 1.9k
Juan A. Rodríguez-Aguilar Spain 23 863 1.0× 522 2.0× 236 1.1× 733 3.7× 239 1.3× 162 2.1k
Mark d’Inverno United Kingdom 21 983 1.1× 289 1.1× 226 1.1× 204 1.0× 134 0.7× 98 1.7k
Lin Padgham Australia 24 1.5k 1.7× 422 1.6× 343 1.6× 214 1.1× 79 0.4× 115 2.1k
H. Van Dyke Parunak United States 27 1.1k 1.2× 842 3.2× 316 1.5× 266 1.4× 224 1.2× 133 2.6k
Ruimin Shen China 24 698 0.8× 221 0.8× 804 3.7× 96 0.5× 286 1.5× 115 2.5k

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-authorship network of co-authors of Daniel Kudenko⋆

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel Kudenko⋆. A scholar is included among the top collaborators of Daniel Kudenko⋆ 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 Daniel Kudenko⋆. Daniel Kudenko⋆ 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.
Kudenko⋆, Daniel, et al.. (2024). Reducing CO2 emissions in a peer-to-peer distributed payment network: Does geography matter in the lightning network?. Computer Networks. 243. 110297–110297. 1 indexed citations
2.
Kudenko⋆, Daniel, et al.. (2023). An effective single-model learning for multi-label data. Expert Systems with Applications. 232. 120887–120887. 1 indexed citations
3.
Ahmadi, Zahra, Zijian Zhang, Daniel Kudenko⋆, et al.. (2023). Inductive and Transductive Link Prediction for Criminal Network Analysis. SSRN Electronic Journal. 1 indexed citations
4.
Fernandes, Kiran, Ignazio Cabras, Feng Li, et al.. (2016). A Conceptual Framework of Business Model Emerging Resilience. Northumbria Research Link (Northumbria University). 3 indexed citations
5.
Fernandes, Kiran, Ignazio Cabras, Feng Li, et al.. (2016). A strategic roadmap for BM change for the video-games industry. Northumbria Research Link (Northumbria University). 2 indexed citations
6.
Devlin, Sam, et al.. (2016). Predicting Disengagement in Free-to-Play Games with Highly Biased Data. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. 12(2). 143–150. 6 indexed citations
7.
Devlin, Sam, et al.. (2016). Resource Abstraction for Reinforcement Learning in Multiagent Congestion Problems. arXiv (Cornell University). 503–511. 19 indexed citations
8.
Devlin, Sam, et al.. (2014). Potential-based difference rewards for multiagent reinforcement learning. Adaptive Agents and Multi-Agents Systems. 165–172. 43 indexed citations
9.
Efthymiadis, Kyriakos, Sam Devlin, & Daniel Kudenko⋆. (2013). Overcoming erroneous domain knowledge in plan-based reward shaping. Adaptive Agents and Multi-Agents Systems. 1245–1246. 2 indexed citations
10.
Devlin, Sam & Daniel Kudenko⋆. (2012). Dynamic potential-based reward shaping. Adaptive Agents and Multi-Agents Systems. 433–440. 75 indexed citations
11.
Hodhod, Rania, Daniel Kudenko⋆, & Paul Cairns. (2010). Character Education Using Pedagogical Agents and Socratic Voice. CSU ePress (Columbus State University). 1 indexed citations
12.
Grześ, Marek & Daniel Kudenko⋆. (2010). PAC-MDP learning with knowledge-based admissible models. Adaptive Agents and Multi-Agents Systems. 349–358. 1 indexed citations
13.
Ting, I‐Hsien, Chris Kimble, & Daniel Kudenko⋆. (2009). Finding unexpected navigation behaviour in clickstream data for website design improvement. Journal of Web Engineering. 8(1). 71–92. 4 indexed citations
14.
Kudenko⋆, Daniel, et al.. (2008). Generation of dilemma-based interactive narratives with a changeable story goal. 6. 14 indexed citations
15.
Kudenko⋆, Daniel, et al.. (2007). Multi-agent Reinforcement Learning for Intrusion Detection.. OpenGrey (Institut de l'Information Scientifique et Technique). 211–223. 19 indexed citations
16.
Kudenko⋆, Daniel, et al.. (2007). Dynamic Generation of Dilemma-Based Interactive Narratives. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment. 3(1). 2–7. 33 indexed citations
17.
Clark, Lillian, I‐Hsien Ting, Chris Kimble, Peter C. Wright, & Daniel Kudenko⋆. (2006). Combining Ethnographic and Clickstream Data to Identify User Web Browsing Strategies. RePEc: Research Papers in Economics. 11(2). 4. 22 indexed citations
18.
Kudenko⋆, Daniel, Dimitar Kazakov, & Eduardo Alonso. (2005). Adaptive Agents and Multi-Agent Systems II: Adaptation and Multi-Agent Learning (Lecture Notes in Computer Science). Springer eBooks. 1 indexed citations
19.
Kudenko⋆, Daniel & Haym Hirsh. (1999). Feature-Based Learners for Description Logics.. Description Logics. 1 indexed citations
20.
Hirsh, Haym & Daniel Kudenko⋆. (1997). Representing sequences in description logics. National Conference on Artificial Intelligence. 384–389. 4 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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