An Empirical Analysis of Terminological Representation Systems. - Jochen Heinsohn; Daniel Kudenko; Bernhard Nebel; Hans-Jürgen Profitlich (Artif. Intell., 1994)

Representing Sequences in Description Logics Using Suffix Trees. - Daniel Kudenko; Haym Hirsh (Description Logics, 1996)

Transferring and Retraining Learned Information Filters. - William W. Cohen; Daniel Kudenko (AAAI/IAAI, 1997)

Representing Sequences in Description Logics. - Haym Hirsh; Daniel Kudenko (AAAI/IAAI, 1997)

Feature Generation for Sequence Categorization. - Daniel Kudenko; Haym Hirsh (AAAI/IAAI, 1998)

Feature-Based Learners for Description Logics. - Daniel Kudenko; Haym Hirsh (Description Logics, 1999)

Machine Learning for Logic-Based Multi-agent Systems. - Eduardo Alonso; Daniel Kudenko (FAABS, 2000)

Machine Learning and Inductive Logic Programming for Multi-agent Systems. - Dimitar Kazakov; Daniel Kudenko (EASSS, 2001)

Sistemas Logicos de Multiples Agentes: Arquitectura e Implementacion en Simuladores de Conflictos. - Eduardo Alonso; Daniel Kudenko (Inteligencia Artif., 2001)

Learning in multi-agent systems. - Eduardo Alonso; Mark d'Inverno; Daniel Kudenko; Michael Luck; Jason Noble (Knowl. Eng. Rev., 2001)

Reinforcement Learning of Coordination in Cooperative Multi-Agent Systems. - Spiros Kapetanakis; Daniel Kudenko (AAAI/IAAI, 2002)

Reinforcement Learning Approaches to Coordination in Cooperative Multi-agent Systems. - Spiros Kapetanakis; Daniel Kudenko; Malcolm J. A. Strens (Adaptive Agents and Multi-Agents Systems, 2002)

MIAU - Supporting Group Decisions in E-Commerce Applications. - Stephan Baldes; Mathias Bauer; Dietmar Dengler; Daniel Kudenko; Gabriele Paul; Thomas Rist; Christian Schmitt (HCI (4), 2003)

Group Decision Making through Mediated Discussions. - Daniel Kudenko; Mathias Bauer; Dietmar Dengler (User Modeling, 2003)

Algorithms for Distributed Exploration. - Thomas Walker; Daniel Kudenko; Malcolm J. A. Strens (ECAI, 2004)

Baselines for Joint-Action Reinforcement Learning of Coordination in Cooperative Multi-agent Systems. - Martin Carpenter; Daniel Kudenko (Adaptive Agents and Multi-Agent Systems, 2005)

Reinforcement Learning of Coordination in Heterogeneous Cooperative Multi-agent Systems. - Spiros Kapetanakis; Daniel Kudenko (Adaptive Agents and Multi-Agent Systems, 2005)

Learning to Coordinate Using Commitment Sequences in Cooperative Multi-agent Systems. - Spiros Kapetanakis; Daniel Kudenko; Malcolm J. A. Strens (Adaptive Agents and Multi-Agent Systems, 2005)

A Pattern Restore Method for Restoring Missing Patterns in Server Side Clickstream Data. - I-Hsien Ting; Chris Kimble; Daniel Kudenko (APWeb, 2005)

Moving Nature-Inspired Algorithms to Parallel, Asynchronous and Decentralised Environments. - Enda Ridge; Daniel Kudenko; Dimitar Kazakov; Edward Curry (SOAS, 2005)

UBB Mining: Finding Unexpected Browsing Behaviour in Clickstream Data to Improve a Web Site's Design. - I-Hsien Ting; Chris Kimble; Daniel Kudenko (Web Intelligence, 2005)

Interactions Between Stereotypes. - Zoë P. Lock; Daniel Kudenko (AH, 2006)

A Study of Concurrency in the Ant Colony System Algorithm. - Enda Ridge; Daniel Kudenko; Dimitar Kazakov (IEEE Congress on Evolutionary Computation, 2006)

Combining ethnographic and clickstream data to identify user Web browsing strategies. - Lillian Clark; I-Hsien Ting; Chris Kimble; Peter C. Wright; Daniel Kudenko (Inf. Res., 2006)

Parallel Reinforcement Learning with Linear Function Approximation. - Matthew Jon Grounds; Daniel Kudenko (Adaptive Agents and Multi-Agents Systems, 2007)

Combining Reinforcement Learning with Symbolic Planning. - Matthew Jon Grounds; Daniel Kudenko (Adaptive Agents and Multi-Agents Systems, 2007)

Multi-agent Reinforcement Learning for Intrusion Detection. - Arturo Servin; Daniel Kudenko (Adaptive Agents and Multi-Agents Systems, 2007)

Dynamic Generation of Dilemma-based Interactive Narratives. - Heather Barber; Daniel Kudenko (AIIDE, 2007)

An Analysis of Problem Difficulty for a Class of Optimisation Heuristics. - Enda Ridge; Daniel Kudenko (EvoCOP, 2007)

Analyzing heuristic performance with response surface models: prediction, optimization and robustness. - Enda Ridge; Daniel Kudenko (GECCO, 2007)

Screening the parameters affecting heuristic performance. - Enda Ridge; Daniel Kudenko (GECCO, 2007)

Applying Web Usage Mining Techniques to Discover Potential Browsing Problems of Users. - I-Hsien Ting; Chris Kimble; Daniel Kudenko (ICALT, 2007)

APD-A Tool for Identifying Behavioural Patterns Automatically from Clickstream Data. - I-Hsien Ting; Lillian Clark; Chris Kimble; Daniel Kudenko; Peter C. Wright (KES (2), 2007)

Tuning the Performance of the MMAS Heuristic. - Enda Ridge; Daniel Kudenko (SLS, 2007)

Special issue on Nature-inspired systems for parallel, asynchronous and decentralised environments. - Enda Ridge; Edward Curry; Daniel Kudenko; Dimitar Kazakov (Multiagent Grid Syst., 2007)

Adaptive Generation of Dilemma-based Interactive Narratives. - Heather Barber; Daniel Kudenko (Advanced Intelligent Paradigms in Computer Games, 2007)

Robustness Analysis of SARSA(lambda): Different Models of Reward and Initialisation. - Marek Grzes; Daniel Kudenko (AIMSA, 2008)

Multi-Agent Reinforcement Learning for Intrusion Detection: A case study and evaluation. - Arturo Servin; Daniel Kudenko (ECAI, 2008)

An Empirical Analysis of the Impact of Prioritised Sweeping on the DynaQ's Performance. - Marek Grzes; Daniel Kudenko (ICAISC, 2008)

Multigrid Reinforcement Learning with Reward Shaping. - Marek Grzes; Daniel Kudenko (ICANN (1), 2008)

Schemas in Directed Emergent Drama. - María Arinbjarnar; Daniel Kudenko (ICIDS, 2008)

Generation of Dilemma-Based Narratives: Method and Turing Test Evaluation. - Heather Barber; Daniel Kudenko (ICIDS, 2008)

Generation of dilemma-based interactive narratives with a changeable story goal. - Heather Barber; Daniel Kudenko (INTETAIN, 2008)

Determining Whether a Problem Characteristic Affects Heuristic Performance. - Enda Ridge; Daniel Kudenko (Recent Advances in Evolutionary Computation for Combinatorial Optimization, 2008)

Knowledge-Based Reinforcement Learning for Data Mining. - Daniel Kudenko; Marek Grzes (ADMI, 2009)

Educational Narrative and Student Modeling for Ill-Defined Domains. - Rania A. Hodhod; Daniel Kudenko; Paul A. Cairns (AIED, 2009)

Reinforcement Learning in RoboCup KeepAway with Partial Observability. - Sam Devlin; Marek Grzes; Daniel Kudenko (IAT, 2009)

Improving Optimistic Exploration in Model-Free Reinforcement Learning. - Marek Grzes; Daniel Kudenko (ICANNGA, 2009)

Theoretical and Empirical Analysis of Reward Shaping in Reinforcement Learning. - Marek Grzes; Daniel Kudenko (ICMLA, 2009)

Duality of Actor and Character Goals in Virtual Drama. - María Arinbjarnar; Daniel Kudenko (IVA, 2009)

Reinforcement Learning with Reward Shaping and Mixed Resolution Function Approximation. - Marek Grzes; Daniel Kudenko (Int. J. Agent Technol. Syst., 2009)

Finding Unexpected Navigation Behaviour in Clickstream Data for Website Design Improvement. - I-Hsien Ting; Chris Kimble; Daniel Kudenko (J. Web Eng., 2009)

Generation of Adaptive Dilemma-Based Interactive Narratives. - Heather Barber; Daniel Kudenko (IEEE Trans. Comput. Intell. AI Games, 2009)

Tuning an Algorithm Using Design of Experiments. - Enda Ridge; Daniel Kudenko (Experimental Methods for the Analysis of Optimization Algorithms, 2010)

PAC-MDP learning with knowledge-based admissible models. - Marek Grzes; Daniel Kudenko (AAMAS, 2010)

Bayesian networks: Real-time applicable decision mechanisms for intelligent agents in interactive drama. - María Arinbjarnar; Daniel Kudenko (CIG, 2010)

Character Education Using Pedagogical Agents and Socratic Voice. - Rania A. Hodhod; Daniel Kudenko; Paul A. Cairns (FLAIRS, 2010)

Adaptive Interactive Narrative Model to Teach Ethics. - Rania A. Hodhod; Daniel Kudenko; Paul A. Cairns (Int. J. Gaming Comput. Mediat. Simulations, 2010)

Automated Event Recognition for Football Commentary Generation. - Maliang Zheng; Daniel Kudenko (Int. J. Gaming Comput. Mediat. Simulations, 2010)

Online learning of shaping rewards in reinforcement learning. - Marek Grzes; Daniel Kudenko (Neural Networks, 2010)

Multi-agent, reward shaping for RoboCup KeepAway. - Sam Devlin; Marek Grzes; Daniel Kudenko (AAMAS, 2011)

Theoretical considerations of potential-based reward shaping for multi-agent systems. - Sam Devlin; Daniel Kudenko (AAMAS, 2011)

An Empirical Study of Potential-Based Reward Shaping and Advice in Complex, Multi-Agent Systems. - Sam Devlin; Daniel Kudenko; Marek Grzes (Adv. Complex Syst., 2011)

Innovative Integrated Architecture for Educational Games: Challenges and Merits. - Rania A. Hodhod; Paul A. Cairns; Daniel Kudenko (Trans. Edutainment, 2011)

Actor Bots. - María Arinbjarnar; Daniel Kudenko (Believable Bots, 2012)

Dynamic potential-based reward shaping. - Sam Devlin; Daniel Kudenko (AAMAS, 2012)

Potential-based reward shaping for POMDPs. - Adam Eck; Leen-Kiat Soh; Sam Devlin; Daniel Kudenko (AAMAS, 2013)

Overcoming erroneous domain knowledge in plan-based reward shaping. - Kyriakos Efthymiadis; Sam Devlin; Daniel Kudenko (AAMAS, 2013)

Using plan-based reward shaping to learn strategies in StarCraft: Broodwar. - Kyriakos Efthymiadis; Daniel Kudenko (CIG, 2013)

Multiagent Router Throttling: Decentralized Coordinated Response Against DDoS Attacks. - Kleanthis Malialis; Daniel Kudenko (IAAI, 2013)

Combining Multiple Correlated Reward and Shaping Signals by Measuring Confidence. - Tim Brys; Ann Nowé; Daniel Kudenko; Matthew E. Taylor (AAAI, 2014)

Potential-based difference rewards for multiagent reinforcement learning. - Sam Devlin; Logan Michael Yliniemi; Daniel Kudenko; Kagan Tumer (AAMAS, 2014)

Knowledge revision for reinforcement learning with abstract MDPs. - Kyriakos Efthymiadis; Sam Devlin; Daniel Kudenko (AAMAS, 2014)

Game intelligence. - Sam Devlin; Peter I. Cowling; Daniel Kudenko; Nikolaos Goumagias; Alberto Nucciarelli; Ignazio Cabras; Kiran Jude Fernandes 001; Feng Li 021 (CIG, 2014)

Coordinated Team Learning and Difference Rewards for Distributed Intrusion Response. - Kleanthis Malialis; Sam Devlin; Daniel Kudenko (ECAI, 2014)

Predicting Player Disengagement in Online Games. - Hanting Xie; Daniel Kudenko; Sam Devlin; Peter I. Cowling (CGW@ECAI, 2014)

Improving Robustness of Gaussian Process-Based Inferential Control System Using Kernel Principle Component Analysis. - Ali Abusnina; Daniel Kudenko; Rolf Roth (ICMLA, 2014)

A Phylogenetic Classification of the Video-Game Industry's Business Model Ecosystem. - Nikolaos Goumagias; Ignazio Cabras; Kiran Jude Fernandes 001; Feng Li 021; Alberto Nucciarelli; Peter I. Cowling; Sam Devlin; Daniel Kudenko (PRO-VE, 2014)

Multi-objectivization of reinforcement learning problems by reward shaping. - Tim Brys; Anna Harutyunyan; Peter Vrancx; Matthew E. Taylor; Daniel Kudenko; Ann Nowé (IJCNN, 2014)

Gaussian Process-Based Inferential Control System. - Ali Abusnina; Daniel Kudenko; Rolf Roth (SOCO-CISIS-ICEUTE, 2014)

A comparison of plan-based and abstract MDP reward shaping. - Kyriakos Efthymiadis; Daniel Kudenko (Connect. Sci., 2014)

Special Issue on Transfer Learning. - Daniel Kudenko (Künstliche Intell., 2014)

Interview with Peter Stone and Matthew E. Taylor. - Daniel Kudenko (Künstliche Intell., 2014)

Knowledge Revision for Reinforcement Learning with Abstract MDPs. - Kyriakos Efthymiadis; Daniel Kudenko (AAMAS, 2015)

Higher Graphical Fidelity Decreases Players' Access to Aggressive Concepts in Violent Video Games. - David Zendle; Paul A. Cairns; Daniel Kudenko (CHI PLAY, 2015)

Predicting player disengagement and first purchase with event-frequency based data representation. - Hanting Xie; Sam Devlin; Daniel Kudenko; Peter I. Cowling (CIG, 2015)

Distributed reinforcement learning for adaptive and robust network intrusion response. - Kleanthis Malialis; Sam Devlin; Daniel Kudenko (Connect. Sci., 2015)

Distributed response to network intrusions using multiagent reinforcement learning. - Kleanthis Malialis; Daniel Kudenko (Eng. Appl. Artif. Intell., 2015)

Resource Abstraction for Reinforcement Learning in Multiagent Congestion Problems. - Kleanthis Malialis; Sam Devlin; Daniel Kudenko (AAMAS, 2016)

Potential-based reward shaping for finite horizon online POMDP planning. - Adam Eck; Leen-Kiat Soh; Sam Devlin; Daniel Kudenko (Auton. Agents Multi Agent Syst., 2016)

Plan-based reward shaping for multi-agent reinforcement learning. - Sam Devlin; Daniel Kudenko (Knowl. Eng. Rev., 2016)

Overcoming incorrect knowledge in plan-based reward shaping. - Kyriakos Efthymiadis; Sam Devlin; Daniel Kudenko (Knowl. Eng. Rev., 2016)

Context-sensitive reward shaping for sparse interaction multi-agent systems. - Yann-Michaël De Hauwere; Sam Devlin; Daniel Kudenko; Ann Nowé (Knowl. Eng. Rev., 2016)

A Reinforcement Learning Based Workflow Application Scheduling Approach in Dynamic Cloud Environment. - Yi Wei; Daniel Kudenko; Shijun Liu; Li Pan 001; Lei Wu 002; Xiangxu Meng (CollaborateCom, 2017)

Distribution Data Across Multiple Cloud Storage using Reinforcement Learning Method. - Abdullah Fayez H. Algarni; Daniel Kudenko (ICAART (2), 2017)

Assured Reinforcement Learning with Formally Verified Abstract Policies. - George Mason; Radu Calinescu; Daniel Kudenko; Alec Banks (ICAART (2), 2017)

Reinforcement Learning for Argumentation: Describing a PhD Research. - Sultan Alahmari; Tommy Yuan; Daniel Kudenko (CMNA@ICAIL, 2017)

Deep Learning of Cell Classification Using Microscope Images of Intracellular Microtubule Networks. - Aleksei Shpilman; Dmitry Boikiy; Marina Polyakova; Daniel Kudenko; Anton Burakov; Elena Nadezhdina (ICMLA, 2017)

Automated Refactoring of Object-Oriented Code Using Clustering Ensembles. - Timofey Bryksin; Alexey Shpilman; Daniel Kudenko (AAAI Workshops, 2018)

Introspective Reinforcement Learning and Learning from Demonstration. - Mao Li; Tim Brys; Daniel Kudenko (AAMAS, 2018)

Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data. - Aleksandra Malysheva; Daniel Kudenko; Aleksei Shpilman (ICARCV, 2018)

Continuous Gesture Recognition from sEMG Sensor Data with Recurrent Neural Networks and Adversarial Domain Adaptation. - Ivan Sosin; Daniel Kudenko; Aleksei Shpilman (ICARCV, 2018)

A Comparative Evaluation of Machine Learning Methods for Robot Navigation Through Human Crowds. - Anastasia Gaydashenko; Daniel Kudenko; Aleksei Shpilman (ICMLA, 2018)

Applying Cartesian Genetic Programming to Evolve Rules for Intrusion Detection System. - Hasanen Alyasiri; John A. Clark; Daniel Kudenko (IJCCI, 2018)

Evaluating Stereotype and Non-Stereotype Recommender Systems. - Nourah A. ALRossais; Daniel Kudenko (KaRS@RecSys, 2018)

Evolutionary Computation Algorithms for Detecting Known and Unknown Attacks. - Hasanen Alyasiri; John A. Clark; Daniel Kudenko (SecITC, 2018)

iSynchronizer: A Tool for Extracting, Integration and Analysis of MovieLens and IMDb Datasets. - Nourah A. ALRossais; Daniel Kudenko (UMAP (Adjunct Publication), 2018)

No priming in video games. - David Zendle; Paul A. Cairns; Daniel Kudenko (Comput. Hum. Behav., 2018)

Deep Multi-Agent Reinforcement Learning with Relevance Graphs. - Aleksandra Malysheva; Tegg Tae Kyong Sung; Chae-Bong Sohn; Daniel Kudenko; Aleksei Shpilman (CoRR, 2018)

Behavioural realism and the activation of aggressive concepts in violent video games. - David Zendle; Daniel Kudenko; Paul A. Cairns (Entertain. Comput., 2018)

Generating Stereotypes Automatically For Complex Categorical Features. - Nourah A. ALRossais; Daniel Kudenko (KaRS@CIKM, 2019)

Reinforcement Learning for Dialogue Game Based Argumentation. - Sultan Alahmari; Tommy Yuan; Daniel Kudenko (CMNA@PERSUASIVE, 2019)

Reinforcement Learning of Dialogue Coherence and Relevance. - Sultan Alahmari; Tommy Yuan; Daniel Kudenko (CMNA@PERSUASIVE, 2019)

MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning. - Aleksandra Malysheva; Daniel Kudenko; Aleksei Shpilman (REDUNDANCY, 2019)

Guest Editorial: Special Issue on Intelligent Robotics and Multi-Agent Systems. - Rui Paulo Rocha; Daniel Kudenko (Cybern. Syst., 2019)

Artificial Intelligence for Prosthetics - challenge solutions. - Lukasz Kidzinski; Carmichael F. Ong; Sharada Prasanna Mohanty; Jennifer L. Hicks; Sean F. Carroll; Bo Zhou; Hong-cheng Zeng; Fan Wang 021; Rongzhong Lian; Hao Tian 005; Wojciech Jaskowski; Garrett Andersen; et al. (CoRR, 2019)

Introspective Q-learning and learning from demonstration. - Mao Li; Tim Brys; Daniel Kudenko (Knowl. Eng. Rev., 2019)

Two-level Q-learning: learning from conflict demonstrations. - Mao Li; Yi Wei; Daniel Kudenko (Knowl. Eng. Rev., 2019)

Uniform State Abstraction for Reinforcement Learning. - John Burden; Daniel Kudenko (ECAI, 2020)

Player Style Clustering without Game Variables. - Mark Ferguson; Sam Devlin; Daniel Kudenko; James Alfred Walker (FDG, 2020)

Automatic Similarity Detection in LEGO Ducks. - Mark Ferguson; Sebastian Deterding; Andreas Lieberoth; Marc Malmdorf Andersen; Sam Devlin; Daniel Kudenko; James Alfred Walker (ICCC, 2020)

Predictive scheduling of wet flue gas desulfurization system based on reinforcement learning. - Zhuang Shao; Fengqi Si; Daniel Kudenko; Peng Wang 139; Xiaozhong Tong (Comput. Chem. Eng., 2020)

Deep Reinforcement Learning with Graph-based State Representations. - Vikram Waradpande; Daniel Kudenko; Megha Khosla (CoRR, 2020)

Curriculum Learning with a Progression Function. - Andrea Bassich; Francesco Foglino; Matteo Leonetti; Daniel Kudenko (CoRR, 2020)

Reinforcement Learning with Quantitative Verification for Assured Multi-Agent Policies. - Joshua Riley; Radu Calinescu; Colin Paterson; Daniel Kudenko; Alec Banks (ICAART (2), 2021)

Assured Deep Multi-Agent Reinforcement Learning for Safe Robotic Systems. - Joshua Riley; Radu Calinescu; Colin Paterson; Daniel Kudenko; Alec Banks (ICAART (Revised Selected Papers), 2021)

Utilising Assured Multi-Agent Reinforcement Learning within Safety-Critical Scenarios. - Joshua Riley; Radu Calinescu; Colin Paterson; Daniel Kudenko; Alec Banks (KES, 2021)

An Online Learning Algorithm for Non-stationary Imbalanced Data by Extra-Charging Minority Class. - Sajjad Kamali Siahroudi; Daniel Kudenko (PAKDD (1), 2021)

Improving cold-start recommendations using item-based stereotypes. - Nourah A. ALRossais; Daniel Kudenko; Tommy Yuan (User Model. User Adapt. Interact., 2021)

Imitating Playstyle with Dynamic Time Warping Imitation. - Mark Ferguson; Sam Devlin; Daniel Kudenko; James Alfred Walker (FDG, 2022)

Assured Multi-agent Reinforcement Learning with Robust Agent-Interaction Adaptability. - Joshua Riley; Radu Calinescu; Colin Paterson; Daniel Kudenko; Alec Banks (KES-IDT, 2022)

A tool for the automation of efficient multi-robot choreography planning and execution. - Eric Wete; Joel Greenyer; Daniel Kudenko; Wolfgang Nejdl; Oliver Flegel; Dennes Eisner (MoDELS (Companion), 2022)

Data Valuation for Offline Reinforcement Learning. - Amir Abolfazli; Gregory Palmer; Daniel Kudenko (CoRR, 2022)

Knowledge Graph Injection for Reinforcement Learning. - Robert Wardenga; Liubov Kovriguina; Dmitrii Pliukhin; Daniil Radyush; Ivan SmoliakoV; Yuan Xue; Henrik Müller; Aleksei Pismerov; Dmitry Mouromtsev; Daniel Kudenko (DL4KG@ISWC, 2023)

MDE and Learning for flexible Planning and optimized Execution of Multi-Robot Choreographies. - Eric Wete; Joel Greenyer; Andreas Wortmann 001; Daniel Kudenko; Wolfgang Nejdl (ETFA, 2023)

Partial Multi-label Learning via Constraint Clustering. - Sajjad Kamali Siahroudi; Daniel Kudenko (ICONIP (11), 2023)

Inductive and transductive link prediction for criminal network analysis. - Zahra Ahmadi; Hoang H. Nguyen; Zijian Zhang; Dmytro Bozhkov; Daniel Kudenko; Maria Jofre; Francesco Calderoni; Noa Cohen; Yosef Solewicz (J. Comput. Sci., 2023)

An effective single-model learning for multi-label data. - Sajjad Kamali Siahroudi; Daniel Kudenko (Expert Syst. Appl., 2023)

Multi-Robot Motion and Task Planning in Automotive Production Using Controller-based Safe Reinforcement Learning. - Eric Wete; Joel Greenyer; Daniel Kudenko; Wolfgang Nejdl (AAMAS, 2024)

Effectively Capturing Label Correlation for Tabular Multi-Label Classification. - Sajjad Kamali Siahroudi; Zahra Ahmadi; Daniel Kudenko (CIKM, 2024)

Entity Matching Across Small Networks Using Node Attributes. - Zahra Ahmadi; Zijian Zhang; Hoang H. Nguyen; Sergio Burdisso; Srikanth R. Madikeri; Petr Motlícek; Erinç Dikici; Gerhard Backfried; Marek Kovác; Kvetoslav Malý; Daniel Kudenko (ECAI, 2024)

ROXSD: The ROXANNE Multimodal and Simulated Dataset for Advancing Criminal Investigations. - Petr Motlícek; Erinç Dikici; Srikanth R. Madikeri; Pradeep Rangappa; Miroslav Jánosík; Gerhard Backfried; Dorothea Thomas-Aniola; Maximilian Schürz; Johan Rohdin; Petr Schwarz; Marek Kovác; Kvetoslav Malý; et al. (Odyssey, 2024)

Reducing CO2 emissions in a peer-to-peer distributed payment network: Does geography matter in the lightning network? - Danila Valko; Daniel Kudenko (Comput. Networks, 2024)

How Real Is Real? A Human Evaluation Framework for Unrestricted Adversarial Examples. - Dren Fazlija; Arkadij Orlov; Johanna Schrader; Monty-Maximilian Zühlke; Michael Rohs; Daniel Kudenko (CoRR, 2024)

Sustainable broadcasting in Blockchain Network with Reinforcement Learning. - Danila Valko; Daniel Kudenko (CoRR, 2024)

Improving the Effectiveness of Potential-based Reward Shaping in Reinforcement Learning. - Henrik Müller; Daniel Kudenko (AAMAS, 2025)

Hybrid pathfinding optimization for the Lightning Network with Reinforcement Learning. - Danila Valko; Daniel Kudenko (Eng. Appl. Artif. Intell., 2025)

TCR: topologically consistent reweighting for XGBoost in regression tasks. - Monty-Maximilian Zühlke; Daniel Kudenko (Mach. Learn., 2025)

Adversarial Robustness of Neural Networks from the Perspective of Lipschitz Calculus: A Survey. - Monty-Maximilian Zühlke; Daniel Kudenko (ACM Comput. Surv., 2025)

SCOOTER: A Human Evaluation Framework for Unrestricted Adversarial Examples. - Dren Fazlija; Monty-Maximilian Zühlke; Johanna Schrader; Arkadij Orlov; Clara Stein; Iyiola E. Olatunji; Daniel Kudenko (CoRR, 2025)

Graph learning-based generation of abstractions for reinforcement learning. - Yuan Xue; Daniel Kudenko; Megha Khosla (Neural Comput. Appl., 2025)

Using incomplete and incorrect plans to shape reinforcement learning in long-sequence sparse-reward tasks. - Henrik Müller; Lukas Berg; Daniel Kudenko (Neural Comput. Appl., 2025)

Increasing energy efficiency of bitcoin infrastructure with reinforcement learning and one-shot path planning for the lightning network. - Danila Valko; Daniel Kudenko (Neural Comput. Appl., 2025)