Professor Eduardo Alonso
Contact details
About
Overview
I am the Director of the Artificial Intelligence Research Centre (CitAI) where we specialise in the intersection between the development of novel AI techniques, Explainable AI (XAI) and Artificial General Intelligence (AGI). I am a member of City St George's Senate, Co-Chair of the university's AI Accelerator and Change (AICC) Research Strand and the University Acacemic Liaison with the Alan Turing Institute.
Research interests
- Reinforcement learning and creativity
- Category theory and representational learning
- AI ethical, legal and socio-economic impact
- Philosophy and history of AI
I have published over 150 papers in high impact journals and in proceedings of first-class conferences in Artificial Intelligence. I have also contributed to The Cambridge Handbook of Artificial Intelligence (Cambridge University Press), the Handbook of Reinforcement Learning and Approximate Dynamic Programming for Feedback Control (Wiley-IEEE), and NFTs, Creativity and the Law (Routledge). One of my papers in the IEEE Transactions on Neural Networks and Learning Systems was spotlighted by the IEEE Computational Intelligence Society as one of the best papers on neural networks and learning systems. I have won the First Prize of the European Institute of Innovation and Technology (EIT) ICT Labs Idea Challenge on Smart Energy Systems.
During my career, I have secured funding from UK Research Councils, the European Commission, industry, and charities. I also collaborate with our American partners in various NSF projects. In the last years, I have gained over £5M from, among others, Innovate UK (in collaboration with companies in the creative industries, DeepReel and Prime Focus Technologies), EU EIT-Digital (in partnership with companies in the automotive and financial sectors, Bosch ASS and Lumera, respectively), the European Commission, the Alan Turing Institute, DSTL and MBDA.
Currently, I have supervised over 20 PhD students.
PhD students welcome!
Please contact me if you are interested in doing a PhD in the areas above.
Requirements: Good programming skills (preferably but not limited to Python) and expertise in two of the following areas: deep learning, control and optimisation, dynamic systems, neuroscience. Applicants would also need to have a strong mathematical background and … be creative!
Qualifications
- BSc, MSc, PhD, University of the Basque Country, Spain
Memberships of committees
- Engineering and Physical Sciences Research Council, January 2009 - December 2013
Research students
Ben Opperman
Attendance: October 2025 - present, full-time
Thesis title: Improvements to the Reinforcement Learning Foundation
Role: 1st Supervisor
Victor Abia Alonso
Attendance: October 2024 - present, full-time
Thesis title: Learning Value Systems in Ethical AI and their Impact in Policymaking
Role: 1st Supervisor
Further information: With Marc Serramia-Amoros
Azad Deihim
Attendance: October 2021 - present, full-time
Thesis title: Advancements in Deep Learning for Application in Electrical Power Systems: From Time Series Forecasting to Reinforcement Learning
Role: 1st Supervisor
Further information: With Dr Marc Serramia Amoros and Dr Dimitra Apostolopoulou
Corina Catarau-Cotutiu
Attendance: October 2020 - present, full-time
Thesis title: Free Energy Principle for Adaptive Cognitive Architectures
Role: External Supervisor
Further information: With Dr Esther Mondragon and Dr Michael-Garcia Ortiz
Sami Saadaoui
Attendance: October 2020 - present, full-time
Thesis title: Using Ai Analytics to Close the Advice Gap for Life, Pension & Investments (LP&I)
Role: 1st Supervisor
Further information: With Dr Aram Ter-Sarkisov (sponsored by EIT-Digital & Ai London)
Alex McCaffrey
Attendance: October 2020 - present, full-time
Thesis title: Free Energy Principle as Drive for Adaptive Cognitive Architectures
Role: 1st Supervisor
Further information: With Dr Michael Garcia-Ortiz and Dr Esther Mondragon (sponsored by DSTL)
Abdul Basit Hafeez
Attendance: February 2020 - present, full-time
Thesis title: Algorithms for Predictive Maintenance of Vehicles in a Connected Environment
Role: 1st Supervisor
Further information: With Dr Michael Garcia-Ortiz (sponsored by EIT-Digital & Bosch)
Esther Mulwa
Attendance: June 2019 - present, full-time
Thesis title: Building an Associative Model Using Deep Learning
Role: 1st Supervisor
Further information: With Dr. Esther Mondragon
Ananda Ananda
Attendance: October 2018 - present, full-time
Thesis title: Wrist fractures analysis in uncertainty pattern on x-ray imaging
Role: 2nd Supervisor
Further information: With Dr. Constantino Reyes Aldasoro
Mauricio Ortega Ruiz
Attendance: February 2018 - January 2025, part-time
Thesis title: Breast Cancer Tumour Cellularity Analysis in Immunostained Pathological Slices
Role: 2nd Supervisor
Further information: With Dr Constantino Reyes Aldasoro
Fatemeh Najibi
Attendance: 2018 - present, full-time
Thesis title: Optimal Operation of Microgrids in the Presence of Renewable Generations such as Photovoltaic
Role: 1st Supervisor
Further information: With Dr. Dimitra Apostolopoulou
Johann Bauer
Attendance: 2017 - present, full-time
Thesis title: The Modelling of Network Topologies under Evolutionary Dynamics
Role: 2nd Supervisor
Further information: With Prof. Mark Broom
Atif Riaz
Attendance: 2015 - present, full-time
Thesis title: Machine Learning for Functional Connectivity Analysis of Neurological Disorders Using Magnetic Resonance Imaging
Role: 1st Supervisor
Further information: With Dr. Greg Slabaugh
Andre Luzardo
Attendance: 2014 - 2017
Thesis title: A Model for Timing and Learning
Further information: With Dr. Esther Mondragon
Niklas Kokkola
Attendance: 2013 - 2017
Thesis title: Computational Models of Learning and Behaviour
Further information: With Dr. Esther Mondragon
Konstantin Pozdniakov
Attendance: 2013 - 2018
Thesis title: Unsupervised Machine Learning in Cyber-Security
Further information: With Prof. Kevin Jones and Dr. Vladimir Stankovic
Remilekun Basaru
Attendance: 2013 - 2018
Thesis title: Robust Hand-pose Recognition from Egocentric Stereovision
Further information: With Dr. Greg Slabaugh and Dr. Chris Child
Jan Teichmann
Attendance: 2012 - 2015
Thesis title: Modelling the Co-evolution of Defence and Signalling in Biological Populations with Aversive Learning
Further information: With Prof. Mark Broom
Michael Fairbank
Attendance: 2011 - 2014
Thesis title: Value-Gradient Learning
Tshiamo Motshegwa
Attendance: 2005 - 2009
Thesis title: Distributed Termination Detection for Multiagent Protocols
Further information: With Prof. Michael Schroeder
Rodrigo Agerri
Attendance: 2003 - 2006
Thesis title: Motivational Attitudes and Norms in a unified Agent Communication Language for open Multi-Agent Systems: A Pragmatic Approach
Jack Gomoluch
Attendance: 2001 - 2005
Thesis title: Market-based Resource Allocation for Distributed Information Processing Applications
Further information: With Prof. Michael Schroeder
Marcus Pearce
Attendance: 2001 - 2005
Thesis title: Construction and Evaluation of Computational Models of Music Perception and Cognition
Further information: With Prof. Geraint Wiggins
Penny Noy
Attendance: 2001 - 2005
Thesis title: Enhancing Comprehension of Complex Data Visualizations: Framework and Techniques Based on Signature Exploration
Further information: With Prof. Michael Schroeder
Riad Ibadulla
Thesis title: High-Resolution Capabilities of Free-space Optical Neural Networks
Role: 2nd Supervisor
Further information: With Dr Constantino Reyes Aldasoro
Publications
Publications by category
Book
- Alonso, E. and Mondragon, E. (2010). Computational Neuroscience for Advancing Artificial Intelligence: Models, Methods and Applications. Hershey, PA: IGI Global. ISBN 9781609600211.
Chapters (10)
- Reyes-Aldasoro, C.C. and Alonso, E. (2026). AI History and Basics: From Symbolism to Neural Networks. Artificial Intelligence for Radiographers (pp. 1-8). Springer Nature Switzerland. ISBN 9783032050793.
- Alonso, E. and Mondragon, E. (2024). NFTs 101: Non-Fungible Tokens and the blockchain may not be what you think. In Bonadio, E. and Sganga, C. (Eds.), NFTs, Creativity and the Law: Within and Beyond Copyright. (pp. 1-19). Milton Park, UK: Routledge. ISBN 9781032497402.
- Alonso, E. (2014). Actions and Agents. In Frankish, K. and Ramsey, W. (Eds.), The Cambridge Handbook of Artificial Intelligence (pp. 232-246). Cambridge, UK: Cambridge University Press. ISBN 9780521871426.
- Fairbank, M., Prokhorov, D. and Alonso, E. (2013). Approximating Optimal Control with Value Gradient Learning. In Lewis, F. and Liu, D. (Eds.), Reinforcement Learning and Approximate Dynamic Programming for Feedback Control (pp. 142-161). Hoboken, NJ: Wiley-IEEE Press. ISBN 9781118104200.
- Fairbank, M., Prokhorov, D. and Alonso, E. (2012). Approximating Optimal Control with Value Gradient Learning. (pp. 142-161). Wiley. ISBN 9781118104200.
- Alonso, E. and Mondragón, E. (2011). Computational Models of Learning and Beyond. Computational Neuroscience for Advancing Artificial Intelligence (pp. 316-332). IGI Global. ISBN 9781609600211.
- Jennings, D.J., Alonso, E., Mondragón, E. and Bonardi, C. (2011). Temporal Uncertainty During Overshadowing. Computational Neuroscience for Advancing Artificial Intelligence (pp. 46-55). IGI Global. ISBN 9781609600211.
- Kudenko, D., Kazakov, D. and Alonso, E. (2008). Machine Learning for Agents and Multi-Agent Systems. Intelligent Information Technologies (pp. 403-420). IGI Global. ISBN 9781599049410.
- Alonso, E. and Mondragon, E. (2007). Associative learning and behaviour: An algebraic search for psychological symmetries. In Aurnague, M., Korta, K. and Larrazabal, J.M. (Eds.), Language, Representation and Reasoning (pp. 35-35). Leioa, Spain: UPV-EHU Press.
- Alonso, E. and Mondragon, E. (2004). Agency, Learning and Animal-Based Reinforcement Learning. In Nickles, M., Rovatsos, M. and Weiss, G. (Eds.), Agents and Computational Autonomy: Potential, Risks, and Solutions (pp. 1-1). Berlin: Springer-Verlag.
Conference papers and proceedings (53)
- Opperman, B., Alonso, E. and Mondragon, E. (2026). Groupoid-Based Internal State Representations for Reinforcement Learning with Local Symmetries. The 25th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2026), Workshop on Adaptive and Learning Agents (ALA) 25-29 May, Paphos, Cyprus.
- Abia Alonso, V., Serramia, M. and Alonso, E. (2025). Finding Our Moral Values: Guidelines for Value System Aggregation. The 6th International Workshop on Democracy & AI, IJCAI25 16-22 August, Montreal, Canada.
- Hafeez, A.B., Alonso, E. and Riaz, A. (2025). Diagnostic Trouble Codes prediction with DTC-GOAT and Ensembles. The 6th International Conference on Deep Learning Theory and Applications (DELTA 2025) 12-13 June, Bilbao.
- McCaffrey, A., Alonso, E. and Mondragón, E. (2025). Predictive Improvement through Latent Space Optimisation. The 24th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2025) 19-23 May, Detroit, USA.doi:10.5555/3709347.3743973
- Alonso, E., Cortes, U., Bugarin, A. and Barrue, C. (2024). The Early Days of the AISB. 27th European Conference on Artificial Intelligence (ECAI 2024) 19-24 October, Santiago de Compostela.doi:10.5281/ZENODO.13898066
- Deihim, A., Apostolopoulou, D. and Alonso, E. (2024). Initial Estimate of AC Optimal Power Flow with Graph Neural Networks. Power Systems Computation Conference (PSCC'24) 4-7 June, Paris.
- Haffez, A.B., Alonso, E. and Riaz, A. (2024). DTC-TranGru: Improving the performance of the next-DTC Prediction Model with Transformer and GRU. The 39th ACM/SIGAPP Symposium on Applied Computing (SAC 2024) 8-12 April, Ávila, Spain.doi:10.1145/3605098.3635962
- Mir, A., Alonso, E. and Mondragón, E. (2024). DiT-Head: High Resolution Talking Head Synthesis Using Diffusion Transformers. 16th International Conference on Agents and Artificial Intelligence 24-26 February.doi:10.5220/0012312200003636
- Cătărău-Cotuțiu, C., Mondragón, E., Alonso, E., Moniz, N., Vale, Z., Cascalho, J.... Sebastiao, R. (2023). AIGenC: AI Generalisation via Creativity. EPIA Conference on Artificial Intelligence 5-8 September, Azores, Portugal.doi:10.1007/978-3-031-49011-8_4
- Suen, C.-.H. and Alonso, E. (2023). Switchable Lightweight Anti-symmetric Processing (SLAP) with CNN Outspeeds Data Augmentation by Smaller Sample -- Application in Gomoku Reinforcement Learning. Proceedings of the AISB Convention (AISB-2023) 13-14 April, Swansea, UK.
- Hafeez, A.B., Alonso, E. and Riaz, A. (2022). DTCEncoder: A Swiss Army Knife Architecture for DTC Exploration, Prediction, Search and Model Interpretation. 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) 12-14 December.doi:10.1109/icmla55696.2022.00085
- Hafeez, A.B., Alonso, E. and Ter-Sarkisov, A. (2021). Towards Sequential Multivariate Fault Prediction for Vehicular Predictive Maintenance. 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA) 13-16 December.doi:10.1109/icmla52953.2021.00167
- Ter-Sarkisov, A. and Alonso, E. (2021). Logo Generation Using Regional Features: A Faster R-CNN Approach to Generative Adversarial Networks. EAI ArtsIT 2021 – 10th EAI International Conference: ArtsIT, Interactivity & Game Creation 2-4 December, Karlsruhe, Germany (virtual).
- Lewis, D., Zugarini, A. and Alonso, E. (2021). Syllable Neural Language Models for English Poem Generation. 12th International Conference on Computational Creativity (ICCC'21) 14-18 September, Mexico City, Mexico.
- Najibi, F., Apostolopoulou, D. and Alonso, E. (2021). Clustering Sensitivity Analysis for Gaussian Process Regression Based Solar Output Forecast. 2021 IEEE Madrid PowerTech 28 June-2 July.doi:10.1109/powertech46648.2021.9495007
- Ikram, K., Mondragon, E., Alonso, E. and Garcia-Ortiz, M. (2021). HexaJungle: a MARL Simulator to Study the Emergence of Language. Conference on Computer Vision and Pattern Recognition (CVPR 2021), Embodied AI Workshop 20-25 June, Nashville, Tennessee.
- Jankovics, V., Garcia-Ortiz, M. and Alonso, E. (2021). HetSAGE: Heterogenous Graph Neural Network for Relational Learning. Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21) 2-9 February, virtual conference.
- Ananda, , Karabag, C., Ter-Sarkisov, A., Alonso, E. and Reyes-Aldasoro, C.C. (2020). Radiography Classification: A Comparison between Eleven Convolutional Neural Networks. 2020 Fourth International Conference on Multimedia Computing, Networking and Applications (MCNA) 19-22 October.doi:10.1109/mcna50957.2020.9264285
- Rozada, S., Apostolopoulou, D. and Alonso, E. (2020). Load Frequency Control: A Deep Multi-Agent Reinforcement Learning Approach. 2020 IEEE Power & Energy Society General Meeting (PESGM) 2-6 August.doi:10.1109/pesgm41954.2020.9281614
- Pozdniakov, K., Alonso, E., Stankovic, V., Tam, K. and Jones, K. (2020). Smart Computer Security Audit: Reinforcement Learning with a Deep Neural Network Approximator. Cyber2020, 135-143 15-19 June, Dublin.
- de A. F. Mello, F.R., Apostolopoulou, D. and Alonso, E. Cost Efficient Distributed Load Frequency Control in Power Systems. .doi:10.1016/j.ifacol.2020.12.2236
- Olliverre, N.J., Yang, G., Slabaugh, G.G., Reyes-Aldasoro, C.C. and Alonso, E. (2018). Generating Magnetic Resonance Spectroscopy Imaging Data of Brain Tumours from Linear, Non-Linear and Deep Learning Models. SASHIMI 2018: Simulation and Synthesis in Medical Imaging, LNCS 11037, 130-138 16-22 September, Granada, Spain.doi:10.1007/978-3-030-00536-8_14
- Najibi, F., Alonso, E. and Apostolopoulou, D. (2018). Optimal Dispatch of Pumped Storage Hydro Cascade under Uncertainty. Control 2018 – 12th International UKACC Conference on Control, 187-192 5-7 September, Sheffield, UK.
- Riaz, A., Asad, M., Al-Arif, S.M.M.R., Alonso, E., Dima, D., Corr, P.... Slabaugh, G. (2018). DeepFMRI: And End-to-End Deep Network for Classification of FRMI Data. 15th IEEE International Symposium on Biomedical Imaging, 1419-1422 April-, Washington DC, USA.
- Basaru, R., Child, C., Alonso, E. and Slabaugh, G.G. Conditional Regressive Random Forest Stereo-based Hand Depth Recovery. .doi:10.1109/ICCVW.2017.78
- Basaru, R., Child, C., Alonso, E. and Slabaugh, G.G. (2017). Hand Pose Estimation Using Deep Stereovision and Markov-chain Monte Carlo. International Conference on Computer Vision, Workshop on Observing and Understanding Hands in Action, 595-603 October-, Venice, Italy.
- Teichmann, J., Alonso, E. and Broom, M. (2017). Reinforcement Learning as a Model of Aposematism. 13th International Conference on Artificial Evolution, 217-230 October-, Paris, France.
- Teichmann, J., Alonso, E. and Broom, M. (2017). Reinforcement Learning is an Effective Strategy to Create Phenotypic Variation and a Potential Mechanism for the Initial Evolution of Learning. 13th International Conference on Artificial Evolution, 246-253 October-, Paris, France.
- Riaz, A., Asad, M., Al-Arid, S.M.M.R., Alonso, E., Dima, D., Corr, P.... Slabaugh, G. (2017). FCNet: A Convolutional Neural Network for Calculating Functional Connectivity from functional MRI. 1st International Workshop on Connectomics in NeuroImaging (CNI), LNCS 10511, 70-78 September-, Quebec City, QC, Canada.doi:10.1007/978-3-319-67159-8_9
- Li, S., Fu, X., Alonso, E., Fairbank, M. and Wunsch, D.C. (2016). Neural-network based vector control of VSC-HVDC transmission systems. Proceedings of the 4th International Conference on Renewable Energy Research and Applications (ICRERA), 173-180 November-, Palermo, Italy.doi:10.1109/ICRERA.2015.7418673
- Riaz, A., Alonso, E. and Slabaugh, G. (2016). Phenotypic Integrated Framework for Classification of ADHD using fMRI. Proc. of the International Conference on Image Analysis and Recognition (ICIAR 2016), 217-225 July-, Póvoa de Varzim, Portugal.doi:10.1007/978-3-319-41501-7_25
- Busquets, J.G., Alonso, E. and Evans, A. (2016). Predicting Aggregate Air Itinerary Shares Using Discrete Choice Modeling. 16th AIAA Aviation Technology, Integration, and Operations Conference, Vol. 3, 1537-1552 June-, Washington, D.C.doi:10.2514/6.2016-4076
- Basaru, R.R., Slabaugh, G., Child, C. and Alonso, E. (2016). HandyDepth: Example-based Stereoscopic Hand Depth Estimation using Eigen Leaf Node Features. Proceedings of the International Conference on Systems, Signals and Image Processing (IWSSIP 2016), 33-36 May-, Bratislava, Slovakia.doi:10.1109/IWSSIP.2016.7502698
- Li, S., Fu, X., Jaithwa, I., Alonso, E., Fairbank, M. and C. Wunsch, D. (2015). Control of Three-Phase Grid-Connected Microgrids using Artificial Neural Networks. 7th International Conference on Neural Computation Theory and Applications 12-14 November.doi:10.5220/0005607900580069
- Teichmann, J., Alonso, E. and Broom, M. (2015). A reward-driven model of Darwinian fitness. Proceedings of the 7th International Joint Conference on Computational Intelligence (IJCCI 2015) - Volume 1: ECTA, 174-179 November-, Lisbon, Portugal.doi:10.5220/0005591501740179
- Busquets, J.G., Alonso, E. and Evans, A. (2015). Application of Data Mining in Air Traffic Forecasting. 15th AIAA Aviation Technology, Integration, and Operations Conference October-, Dallas, TX.doi:10.2514/6.2015-2732
- Li, S., Alonso, E., Fairbank, M., Jaithwa, I. and Wunsch, D.C. (2015). Hardware Validation for Control of Three-Phase Grid-Connected Microgrids Using Artificial Neural Networks. 12th International Conference on Applied Computing 2015 24-10 January, Maynooth, Ireland.
- Karcanias, N., Hessami, A.G. and Alonso, E. (2014). Complexity of Multi-Modal Transportation and Systems of Systems. 47th Annual Universities’ Transport Study Group Conference (UTSG 2015) 24 December 2014-5 January 2015, London, UK.
- Basaru, R.R., Child, C., Alonso, E. and Slabaugh, G. (2014). Quantized Census for Stereoscopic Image Matching. Second International Conference on 3D Vision (3DV 2014), 22-29 December-, Tokyo, Japan.doi:10.1109/3DV.2014.83
- Weller, P., Fernandez, A. and Alonso, E. (2014). Towards a Personalised Health System. 7th International Conference on Health Informatics (HEALTHINF 2014), pp. 256-261 3 June-6 March, Angers, France.doi:10.5220/0004749702560261
- Alonso, E. and Mondragon, E. (2014). Quantum Probability and Operant Conditioning: Behavioral Uncertainty in Reinforcement Learning. 6th International Conference on Agents and Artificial Intelligence (ICAART 2014), 2448-251 3-6 March, Angers, France.doi:10.5220/0004903205480551
- Alonso, E., Sahota, P. and Mondragon, E. (2014). Computational Models of Classical Conditioning: A Qualitative Evaluation and Comparison. 6th International Conference on Agents and Artificial Intelligence (ICAART 2014), 2445-247 3-6 March, Angers, France.doi:10.5220/0004903105440547
- Alonso, E. and Fairbank, M. (2013). Emergent and Adaptive Systems of Systems. IEEE International Conference on Systems, Man, and Cybernetics (IEEE SMC 2013), 1721-1725 October-, Manchester, UK.doi:10.1109/SMC.2013.296
- Li, S., Fairbank, M., Fu, X., Wunsch, D. and Alonso, E. (2013). Nested-Loop Neural Network Vector Control of Permanent Magnet Synchronous Motors. IEEE International Joint Conference on Neural Networks (IEEE IJCNN 2013), 2999-3006 August-, Dallas, TX.doi:10.1109/IJCNN.2013.6707124
- Alonso, E., Karcanias, N. and Hessami, A. (2013). Symmetries, groups and groupoids for Systems of Systems. IEEE International Systems Conference (SysCon 2013), 244-250 April-, Orlando, FL.doi:10.1109/SysCon.2013.6549889
- Alonso, E., Karcanias, N. and Hessami, A. (2013). Multi-Agent Systems: A new paradigm for Systems of Systems. Eighth International Conference on Systems (ICONS 2013), 8-12 January-, Seville, Spain.
- Fairbank, M. and Alonso, E. (2012). The divergence of reinforcement learning algorithms with value-iteration and function approximation. IEEE International Joint Conference on Neural Networks (IEEE IJCNN 2012), 3070-3077 June-, Brisbane, Australia.doi:10.1109/IJCNN.2012.6252792
- Fairbank, M. and Alonso, E. (2012). Value-Gradient Learning. IEEE International Joint Conference on Neural Networks (IEEE IJCNN 2012), 3062-3069 June-, Brisbane, Australia.doi:10.1109/IJCNN.2012.6252791
- Fairbank, M. and Alonso, E. (2012). A Comparison of Learning Speed and Ability to Cope Without Exploration between DHP and TD(0). IEEE International Joint Conference on Neural Networks (IEEE IJCNN 2012), 1478-1485 June-, Brisbane, Australia.doi:10.1109/IJCNN.2012.6252569
- Li, S., Fairbank, M., Wunsch, D. and Alonso, E. (2012). Vector Control of a Grid-Connected Rectifier/Inverter Using an Artificial Neural Network. IEEE International Joint Conference on Neural Networks (IEEE IJCNN 2012), 1783-1789 June-, Brisbane, Australia.doi:10.1109/IJCNN.2012.6252614
- Alonso, E., Fairbank, M. and Mondragon, E. (2012). Conditioning for Least Action. 11th International Conference on Cognitive Modeling (ICCM-12), 234-239 April-, Berlin, Germany.
- Alonso, E. and Mondragon, E. (2012). Uses, Abuses and Misuses of Computational Models in Classical Conditioning. 11th International Conference on Cognitive Modeling (ICCM-12), 96-100 April-, Berlin, Germany.
- Jankovics, V., Garcia Ortiz, M. and Alonso, E. HetSAGE: Heterogenous Graph Neural Network for Relational Learning (Student Abstract). .doi:10.1609/aaai.v35i18.17898
Journal articles (45)
- Cătărău-Cotutiu, C., Mondragón, E. and Alonso, E. (2026). A representational framework for learning and encoding structurally enriched trajectories in complex agent environments. Neural Networks, 200, pp. 108868-108868. doi:10.1016/j.neunet.2026.108868
- Bauer, J., West, S., Alonso, E. and Broom, M. (2026). Mutation-bias learning: an evolutionary game dynamics approach to convergence analysis in multi-agent reinforcement learning. Proceedings of the Royal Society A Mathematical Physical and Engineering Science, 482(2329). doi:10.1098/rspa.2025.0449
- Dean, A., Alonso, E. and Mondragon, E. (2025). Algebras of Actions in an Agent’s Representations of the World. Artificial Intelligence, 348. doi:10.1016/j.artint.2025.104403
- Saadaoui, S. and Alonso, E. (2025). Coordinated LLM Multi-Agent Systems for Collaborative Question-Answer Generation. Knowledge-Based Systems, 330(Part B). doi:10.1016/j.knosys.2025.114627
- Alonso, E. and Lucchi, N. (2025). AI and Copyright ‘Hallucinations’: Does the Text and Data Mining Exception Really Supporting Generative AI Training? European Intellectual Property Review, 47(9), pp. 515-526
- Stogiannos, N., Gillan, C., Precht, H., Reis, C.S.D., Kumar, A., O'Regan, T.... Malamateniou, C. (2024). A multidisciplinary team and multiagency approach for AI implementation: A commentary for medical imaging and radiotherapy key stakeholders. Journal of Medical Imaging and Radiation Sciences, 55(4), pp. 101717-101717. doi:10.1016/j.jmir.2024.101717
- Deihim, A., Apostolopoulou, D. and Alonso, E. (2024). Initial estimate of AC optimal power flow with graph neural networks. Electric Power Systems Research, 234, pp. 110782-110782. doi:10.1016/j.epsr.2024.110782
- Shering, T., Alonso, E. and Apostolopoulou, D. (2024). Investigation of Load, Solar and Wind Generation as Target Variables in LSTM Time Series Forecasting, Using Exogenous Weather Variables. Energies, 17(8), pp. 1827-1827. doi:10.3390/en17081827
- Fu, X., Sturtz, J., Alonso, E., Challoo, R. and Qingge, L. (2024). Parallel Trajectory Training of Recurrent Neural Network Controllers With Levenberg–Marquardt and Forward Accumulation Through Time in Closed-Loop Control Systems. IEEE Transactions on Sustainable Computing, 9(2), pp. 222-229. doi:10.1109/tsusc.2023.3330573
- Deihim, A., Alonso, E. and Apostolopoulou, D. (2023). STTRE: A Spatio-Temporal Transformer with Relative Embeddings for multivariate time series forecasting. Neural Networks, 168, pp. 549-559. doi:10.1016/j.neunet.2023.09.039
- Fu, X., Li, S., Wunsch, D.C. and Alonso, E. (2023). Local Stability and Convergence Analysis of Neural Network Controllers With Error Integral Inputs. IEEE Transactions on Neural Networks and Learning Systems, 34(7), pp. 3751-3763. doi:10.1109/tnnls.2021.3116189
- Najibi, F., Apostolopoulou, D. and Alonso, E. (2021). Enhanced performance Gaussian process regression for probabilistic short-term solar output forecast. International Journal of Electrical Power & Energy Systems, 130, pp. 106916-106916. doi:10.1016/j.ijepes.2021.106916
- Rozada, S., Apostolopoulou, D. and Alonso, E. (2021). Deep multi‐agent Reinforcement Learning for cost‐efficient distributed load frequency control. IET Energy Systems Integration, 3(3), pp. 327-343. doi:10.1049/esi2.12030
- Ananda, A., Ngan, K.H., Karabağ, C., Ter-Sarkisov, A., Alonso, E. and Reyes-Aldasoro, C.C. (2021). Classification and Visualisation of Normal and Abnormal Radiographs; A Comparison between Eleven Convolutional Neural Network Architectures. Sensors, 21(16), pp. 5381-5381. doi:10.3390/s21165381
- Najibi, F., Apostolopoulou, D. and Alonso, E. (2021). TSO-DSO Coordination Schemes to Facilitate Distributed Resources Integration. Sustainability, 13(14), pp. 7832-7832. doi:10.3390/su13147832
- Mondragon, E., Alonso, E. and Kokkola, K. (2020). Associative Learning Should Go Deep. Trends in Cognitive Sciences, 21(11), pp. 822-825. doi:10.1016/j.tics.2017.06.001
- Lambrechts, A., Cook, J., Ludvig, E., Alonso, E., Anns, S., Taylor, M.... Gaigg, S. (2020). Reward devaluation in autistic children and adolescents with complex needs: a feasibility study. Autism Research, 13(11), pp. 1915-1928-1915-1928. doi:10.1002/aur.2388
- Li, S., Won, H., Fu, X., Fairbank, M., Wunsch, D. and Alonso, E. (2020). Neural-Network Vector Controller for Permanent-Magnet Synchronous Motor Drives: Simulated and Hardware-Validated Results. IEEE Transactions on Cybernetics, 50(7), pp. 3218-3230. doi:10.1109/TCYB.2019.2897653
- Bauer, J., Broom, M. and Alonso, E. (2019). The stabilization of equilibria in evolutionary game dynamics through mutation: mutation limits in evolutionary games. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 475(2231), pp. 20190355-20190355. doi:10.1098/rspa.2019.0355
- Carrera, Á., Alonso, E. and Iglesias, C.A. (2019). A Bayesian Argumentation Framework for Distributed Fault Diagnosis in Telecommunication Networks. Sensors, 19(15). doi:10.3390/s19153408
- Kokkola, N., Mondragon, E. and Alonso, E. (2019). A Double Error Dynamic Asymptote Model of Associative Learning. Psychological Review, 126(4), pp. 506-549. doi:10.1037/rev0000147
- Riaz, A., Asad, M., Alonso, E. and Slabaugh, G.G. (2018). Fusion of fMRI and Non-Imaging Data for ADHD Classification. Computerized Medical Imaging and Graphics, 65, pp. 115-128. doi:10.1016/j.compmedimag.2017.10.002
- Basaru, R.R., Child, C., Alonso, E. and Slabaugh, G. (2018). Data-driven Recovery of Hand Depth using Conditional Regressive Random Forest on Stereo Images. IET Computer Vision. doi:10.1049/iet-cvi.2017.0227
- Luzardo, A., Rivest, F., Alonso, E. and Ludvig, E. (2017). A Drift-Diffusion Model of Interval Timing in the Peak Procedure. Journal of Mathematical Psychology, 77, pp. 111-123. doi:10.1016/j.jmp.2016.10.002
- Albrecht, T., Slabaugh, G., Alonso, E. and Al-Arif, M.R. (2017). Deep Learning for Single-Molecule Science. Nanotechnology, 28(42), pp. 423001-423001. doi:10.1088/1361-6528/aa8334
- Luzardo, A., Alonso, E. and Mondragon, E. (2017). A Rescorla-Wagner Drift-Diffusion Model of Conditioning and Timing. PLoS Computational Biology, 13(11). doi:10.1371/journal.pcbi.1005796
- Guimera Busquets, J., Alonso, E. and Evans, A. (2017). Air Itinerary Shares Estimation Using Multinomial Logit Models. Transportation Planning and Technology, 41(1), pp. 3-16. doi:10.1080/03081060.2018.1402742
- Fu, X., Li, S., Fairbank, M., Wunsch, D. and Alonso, E. (2015). Training Recurrent Neural Networks with the Levenberg–Marquardt Algorithm for Optimal Control of a Grid-Connected Converter. IEEE Transactions on Neural Networks and Learning Systems, 26(9), pp. 1900-1912. doi:10.1109/TNNLS.2014.2361267
- Alonso, E., Fairbank, M. and Mondragon, E. (2015). Back to Optimality: A Formal Framework to Express the Dynamics of Learning Optimal Behavior. Adaptive Behavior, 23(4), pp. 206-215. doi:10.1177/1059712315589355
- Li, S., Fairbank, M., Johnson, C., Wunsch, D.C., Alonso, E. and Proano, J.L. (2014). Artificial Neural Networks for Control of a Grid-Connected Rectifier/Inverter under Disturbance, Dynamic and High Frequency Switching Conditions. IEEE Transactions on Neural Networks and Learning Systems, 25(4), pp. 738-750. doi:10.1109/TNNLS.2013.2280906
- Fairbank, M., Prokhorov, D. and Alonso, E. (2014). Clipping in Neurocontrol by Adaptive Dynamic Programming. IEEE Transactions on Neural Networks and Learning Systems, 25(10), pp. 1909-1920. doi:10.1109/TNNLS.2014.2297991
- Alonso, E. and Mondragón, E. (2014). What Have Computational Models Ever Done for Us? International Journal of Artificial Life Research, 4(1), pp. 1-12. doi:10.4018/ijalr.2014010101
- Fairbank, M., Li, S., Fu, X., Alonso, E. and Wunsch, D. (2014). An Adaptive Recurrent Neural-Network Controller using a Stabilization Matrix and Predictive Inputs to Solve the Tracking Problem under Disturbances. Neural Networks, 49, pp. 74-86. doi:10.1016/j.neunet.2013.09.010
- Teichmann, J., Broom, M. and Alonso, E. (2014). The Evolutionarily Dynamics of Aposematism: a Numerical Analysis of Co-Evolution in Finite Populations. Mathematical Modelling of Natural Phenomena (MMNP), 9(3), pp. 148-164. doi:10.1051/mmnp/20149310
- Mondragon, E., Gray, J., Alonso, E., Bonardi, C. and Jennings, D. (2014). SSCC TD: A Serial and Simultaneous Configural-Cue Compound Stimuli Representation for Temporal Difference Learning. PLoS ONE, 9(7): e102469, pp. 1-1. doi:10.1371/journal.pone.0102469
- Teichmann, J., Broom, M. and Alonso, E. (2013). The Application of Temporal Difference Learning in Optimal Diet Models. Journal of Theoretical Biology, 340(7), pp. 11-16. doi:10.1016/j.jtbi.2013.08.036
- Jennings, D., Alonso, E., Mondragon, E., Frassen, M. and Bonardi, C. (2013). The Effect of Stimulus Distribution Form on the Acquisition and Rate of Conditioned Responding: Implications for Theory. Journal of Experimental Psychology: Animal Behavior Processes, 39(3), pp. 233-248. doi:10.1037/a0032151
- Mondragon, E., Alonso, E., Fernandez, A. and Gray, J. (2013). An Extension of the Rescorla and Wagner simulator for Context Conditioning. Computer Methods and Programs in Biomedicine, 110(2), pp. 226-230. doi:10.1016/j.cmpb.2013.01.016
- Fairbank, M., Alonso, E. and Prokhorov, D. (2013). An Equivalence Between Adaptive Dynamic Programming With a Critic and Backpropagation Through Time. IEEE Transactions on Neural Networks and Learning Systems, 24(12), pp. 2088-2100. doi:10.1109/TNNLS.2013.2271778
- Mondragon, E., Gray, J. and Alonso, E. (2013). A Complete Serial Compound Temporal Difference Simulator for Compound stimuli, Configural cues and Context representation. NeuroInformatics, 11(2), pp. 259-261. doi:10.1007/s12021-012-9172-z
- Alonso, E., Mondragon, E. and Fernandez, A. (2012). A Java simulator of Rescorla and Wagner's prediction error model and configural cue extensions. Computer Methods and Programs in Biomedicine, 108(1), pp. 346-355. doi:10.1016/j.cmpb.2012.02.004
- Fairbank, M., Alonso, E. and Prokhorov, D. (2012). Simple and Fast Calculation of the Second Order Gradients for Globalized Dual Heuristic Programming in Neural Networks. IEEE Transactions on Neural Networks and Learning Systems, 23(10), pp. 1671-1676. doi:10.1109/TNNLS.2012.2205268
- Fairbank, M. and Alonso, E. (2012). Efficient Calculation of the Gauss-Newton Approximation of the Hessian Matrix in Neural Networks. Neural Computation, 24(3), pp. 607-610. doi:10.1162/NECO_a_00248
- Alonso, E. and Schmajuk, N. (2012). Computational Models of Classical Conditioning guest editors’ introduction. Learning and Behavior, 40(3), pp. 231-240. doi:10.3758/s13420-012-0081-7
- (2003). Adaptive Agents and Multi-Agent Systems. . doi:10.1007/3-540-44826-8
Report
- Mondragon, E. and Alonso, E. Hall and Rodríguez model as a particular case of the Pearce and Hall model: A formal analysis. St Albans, UK: Centre for Computational and Animal Learning Research.
Software (11)
- Chung, B., Mondragon, E. and Alonso, E. (2018). Rescorla & Wagner Simulator+ © Ver. 5. St. Albans, UK: Centre for Computational and Animal Learning Research (CAL-R).
- Kokkola, N., Mondragon, E. and Alonso, E. (2018). DOUBLE ERROR DYNAMIC ASYMPTOTE MODEL SIMULATOR. St. Albans, UK: Centre for Computational and Animal Learning Research (CAL-R).
- Byers, J., Mondragon, E. and Alonso, E. (2017). SOP Model Simulator. St. Albans, UK: Centre for Computational and Animal Learning Research (CAL-R).
- Ghorashi, A., Mondragon, E. and Alonso, E. (2017). REPLACED ELEMENTS MODEL - REM SIMULATOR v.1. St. Albans, UK: Centre for Computational and Animal Learning Research (CAL-R).
- Gheorghescu, A., Mondragon, E. and Alonso, E. (2017). PEARCE MODEL SIMULATOR ver. 1. St. Albans, UK: Centre for Computational and Animal Learning Research (CAL-R).
- Anandasivathas, T., Mondragon, E. and Alonso, E. (2017). Harris Model Simulator. St. Albans, UK: Centre for Computational and Animal Learning Research (CAL-R).
- Grikietis, R., Mondragon, E. and Alonso, E. (2016). PEARCE & HALL MODEL SIMULATOR. St. Albans, UK: Centre for Computational and Animal Learning Research (CAL-R).
- Alexandrakis, D., Mondragon, E. and Alonso, E. (2015). RESCORLA & WAGNER SIMULATOR FOR ANDROID V.1. St. Albans, UK: Centre for Computational and Animal Learning Research (CAL-R).
- Gray, J., Mondragon, E. and Alonso, E. (2013). SSCC TD MODEL SIMULATOR ver. 1.0. St. Albans, UK: Centre for Computational and Animal Learning Research (CAL-R).
- Mondragon, E., Gray, J. and Alonso, E. (2012). TEMPORAL DIFFERENCE MODEL SIMULATOR ver. 1.0. St. Albans, UK: Centre for Computational and Animal Learning Research (CAL-R).
- Mondragon, E., Alonso, E., Fernandez, A. and Gray, J. (2012). RESCORLA & WAGNER MODEL SIMULATOR. St. Albans, UK: Centre for Computational and Animal Learning Research (CAL-R).