Publications

Theodore Tranos, Piyabhum Chaysri, Christos Spatharis and Konstantinos Blekas. A multi-task reinforcement learning framework for resilient autonomous USV navigation. Ocean Engineering 357, 125549, 2026.

SC Katsoudas, TI Aravanis, KD Blekas, EE Tzirtzilakis and MA Xenos. Artificial neural networks for predicting pulsatile hemodynamics in stenotic arteries. Meccanica 61 (4), 93, 2026.

Christos Spatharis, Konstantinos Blekas, Georgios Santipantakis and George A. Vouros. Modular and Multimodal Generative Adversarial Imitation Learning for Modeling Flight Trajectories. Journal of Air Transportation, 2025.

Piyabhum Chaysri, Theodore Tranos, George Papadopoulos, George A. Vouros and Konstantinos Blekas. Efficient Autonomous Marine Vessel Navigation with Safe Deep Reinforcement Learning. 2025 Symposium on Maritime Informatics and Robotics (MARIS), pp. 1–8, 2025.

George Vouros, Ioannis Ioannidis, Georgios Santipantakis, Theodore Tranos, Konstantinos Blekas, Marc Melgosa and Xavier Prats. Machine-Learning Methods Estimating Flights’ Hidden Parameters for the Prediction of KPIs. Aerospace, 11(11), 937, 2024.

Alexandros Troupiotis-Kapeliaris, Nikolaos Sapountzis, Giannis Spiliopoulos, Thomas Kogias, Piyabhum Chaysri, Bernardo António Correia Gabriel, Miguel Filipe Santos Silva, Elias Xidias, Konstantinos Blekas, João Tasso de Figueiredo Borges de Sousa and Dimitris Zissis. Collection: The Aegean Ro-Boat Race 2023. IEEE Data Descriptions, 1, pp. 87–94, 2024.

Theodore Tranos, Piyabhum Chaysri, Christos Spatharis and Konstantinos Blekas. An Advanced Deep Reinforcement Learning Framework for Docking Unmanned Surface Vessels in Variable Environmental Conditions and Amid Moving Ships. Proceedings of the 13th Hellenic Conference on Artificial Intelligence (SETN 2024), 2024.

Theodore Tranos, Christos Spatharis, Konstantinos Blekas and Andreas-Giorgios Stafylopatis. Large-Scale Urban Traffic Management Using Zero-Shot Knowledge Transfer in Multi-Agent Reinforcement Learning for Intersection Patterns. Robotics, 13(7), 109, 2024.

Christos Spatharis, Konstantinos Blekas and George A. Vouros. Modelling Flight Trajectories with Multi-Modal Generative Adversarial Imitation Learning. Applied Intelligence, 54, pp. 7118–7134, 2024.

Piyabhum Chaysri, Christos Spatharis, Kostas Vlachos and Konstantinos Blekas. Design and Implementation of a Low-Cost Intelligent Unmanned Surface Vehicle. Sensors, 24(10), 3254, 2024.

Christos Spatharis and Konstantinos Blekas. Multiagent reinforcement learning for autonomous driving in traffic zones with unsignalized intersections. Journal of Intelligent Transportation Systems, 28(1), pp. 103–119, 2024.

Piyabhum Chaysri, Christos Spatharis, Konstantinos Blekas and Kostas Vlachos. Unmanned surface vehicle navigation through generative adversarial imitation learning. Ocean Engineering, 282, 114989, 2023.

Christos Spatharis, Alevizos Bastas, Theocharis Kravaris, Konstantinos Blekas, George A. Vouros and Jose Manuel Cordero Garcia. Hierarchical multiagent reinforcement learning schemes for air traffic management. Neural Computing and Applications, 35(1), pp. 147–159, 2023.

Bin Liu, Konstantinos Blekas and Grigorios Tsoumakas. Multi-label sampling based on local label imbalance. Pattern Recognition, 122, 108294, 2022.

K. Blekas and I.L. Lagaris. Functionally Weighted Neural Networks: Frugal Models with High Accuracy. Springer Nature Applied Science, vol. 2 (12), pp. 1-12, 2020.

P. Chayrsi, K. Blekas and K. Vlachos. Multiple mini-robots navigation using a collaborative multiagent reinforcement learning framework. Journal of Advanced Robotics (Special Issue on Robot Learning), vol. 34 (13), pp. 902-916, 2020.

V. Oikonomou, K. Blekas and L. Astrakas. Identification of Brain Functional Networks Using a Model-Based Approach. International Journal of Pattern Recognition and Artificial Intelligence (IJPRAI), vol. 34 (8), 2020.

DL Patiris, K Blekas, KG Ioannides, The expansion of TRIAC to TRIACII code for track measurements from SSNT detectors HNPS Proceedings 15, 180-187, 2020.

C. Spatharis and K. Blekas. Double deep multiagent reinforcement learning for autonomous driving in traffic maps with road segments and unsignaled intersections. 23rd IEEE International Conference on Intelligent Transportation Systems (ITSC) 2020.

C. Spatharis. K. Blekas and G. Vouros. Apprenticeship learning of flight trajectories prediction with inverse reinforcement learning. 11th Hellenic Conference on Artificial Intelligence (SETN) 2020.

C Spatharis, K Blekas, A Bastas, T Kravaris, GA Vouros. Collaborative multiagent reinforcement learning schemes for air traffic management. 10th IEEE International Conference on Information, Intelligence, Systems and Applications (IISA), Patras, 2019

P Chaysri, K Blekas, K Vlachos. Navigation of inertial forces driven mini-robots using reinforcement learning. 10th IEEE International Conference on Information, Intelligence, Systems and Applications (IISA), Patras, 2019.

DL Patiris, K Blekas, KG Ioannides. A MATLAB code for recognition and counting of track images. HNPS Proceedings 14, 119-124. 2019.

T Kravaris, C Spatharis, A Bastas, GA Vouros, K Blekas, G Andrienko. Resolving Congestions in the Air Traffic Management Domain via Multiagent Reinforcement Learning Methods. arXiv:1912. 06860, 2019

K. Blekas and K. Vlachos. RL-based Path Planning for an Over-actuated Floating Vehicle under Disturbances, Robotics and Autonomous Systems, vol. 101, pp. 93-102, 2018

K. Tziortziotis, N. Tziortziotis, K. Vlachos and K. Blekas. Motion Planning with Energy Reduction for a Floating Robotic Platform under Disturbances and Measurement Noise using Reinforcement Learning International Journal on Artificial Intelligence Tools (IJAIT), vol. 27 (4), 2018

M. Koubarakis, G. Vouros, G. Chalkiadakis, V. Plagianakos, C. Tjortjis, E. Kavallieratou, D. Vrakas, N. Mavridis, G. Petasis, K. Blekas, A. Krithara. AI in Greece: The Case of Research on Linked Geospatial Data, AI Magazine 39 (2), 91-96, 2018.

C. Spatharis, T. Kravaris, G. A. Vouros, K. Blekas, G. Chalkiadakis, J. M. Cordero Garcia, E. C. Fernandez: Multiagent Reinforcement Learning Methods to Resolve Demand Capacity Balance Problems. Proceedings of the 10th Hellenic Conference on Artificial Intelligence (SETN 2018). (best student paper award).

C. Spatharis, T. Kravaris, K. Blekas and G. A. Vouros. Multiagent Reinforcement Learning Methods for Resolving Demand - Capacity Imbalances. 37th AIAA/IEEE Digital Avionics Systems Conference (DASC), Sept. 2018. (best paper award).

V. Oikonomou, K. Blekas and L. Astrakas. Functional Connectivity in Parkinson Disease Through Mixture Modelling. IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), pages 1-5, 2018.

K. Blekas and I.E. Lagaris. Artificial Neural Networks with an infinite number of nodes, Journal of Physics: Conf. Series, 915, 2017.

T. Kavaris, G. A. Vouros, C. Spatharis, K. Blekas and J.M.C. Carcia. Learning Policies for Resolving Demand-Capacity Imbalances During Pre- tactical Air Traffic Management, German Conference on Multiagent System Technologies, 2017.

N. Tziortziotis, G. Papagiannis and K. Blekas. A Bayesian Ensemble Regression Framework on the Angry Birds Game, IEEE Trans. on Computational Intelligence and AI in Games, vol. 8 (2), pp. 104-115, 2016.

K. Tziortziotis, N. Tziortziotis, K, Vlachos and K. Blekas. Autonomous navigation of an over-actuated marine platform using reinforcement learning, 9 th Hellenic Conference on Artificial Intelligence (SETN 2016), 2016. (best student paper award).

K. Tziortziotis, K. Vlachos and K. Blekas. Reinforcement learning-based motion planning of a triangular floating platform under environmental disturbances, Mediterranean Conference on Control and Automation (MED), 2016.

V. Oikonomou and K. Blekas. Regression Mixture Modeling for fMRI Data Analysis,Frontiers of Medical Imaging, C.H. Chen editor, World Scientific Publishing, pp. 167-190, 2015.

K. Pandremmenou, N. Tziortziotis, S. Paluri, W. Zhang, K. Blekas, L.P. Kondi, S. Kumar. Quality Optimization of H.264AVC Video Transmission over Noisy Environments Using a Sparse Regression Framework, in Visual Information Processing and Communication VI, Proceedings of SPIE - IS&T Electronic Imaging, San Francisco, CA, February 2015.

N. Tziortziotis, G. Papagiannis and K. Blekas. A Bayesian Ensemble Regression Framework on the Angry Birds Game. ECAI 2014 Symposium on AI in Angry Birds, Prague, 2014. (2 nd winner of the AIBIRDS 2014 competition)

N. Tziortziotis, C. Dimitrakakis and K. Blekas. Cover tree Bayesian reinforcement learning. Journal of Machine Learning Research (JMLR), vol. 15, pp. 2313-2335, 2014

K. Blekas and A. Likas. Sparse regression mixture modeling with the multi-kernel relevance vector machine. Knowledge and Information Systems (KAIS), vol. 39(2), pp. 241-264, 2014

N. Tziortziotis, K. Tziortziotis and K. Blekas. Play Ms. Pac-Man using an advanced reinforcement learning agent, 8 th Hellenic Conference on Artificial Intelligence (SETN 2014), Ioannina, Greece, Lecture notes in

Artificial Intelligence 8445, pp.71-83, 2014.

V. Oikonomou, K. Blekas and L. Astrakas. Resting State fMRI analysis using a spatial regression mixture model. BIBE 2013, Chania, Greece, Nov. 2013.

S. Karavarsamis, N. Ntarmos, K. Blekas and I. Pitas, Detecting pornographic images by localizing skin ROIs. International Journal of Digital Crime and Forensics (IJDCF), 5 (1), pp. 39-53, 2013.

V. Oikonomou and K. Blekas. An adaptive regression mixture model for fMRI cluster analysis. IEEE Transactions on Medical Imaging, vol. 32 (4), pp.649-659, 2013.

N. Tziortziotis, C. Dimitrakakis and K. Blekas. Linear Bayesian Reinforcement Learning. International Joint Conference on Artificial Intelligence IJCAI 2013

K. Blekas and I. Lagaris. A spectral clustering approach based on Newton’s equations of motion. International Journal of Intelligent Systems, vol 28 (4), pp. 394–410, 2013

K. Blekas and A. Lykas. The mixture of multi-kernel relevance vector machines model. IEEE International Conference on Data Mining (ICDM), pp. 111-120, Brussels 2012

N. Tziortziotis and K. Blekas. Model-based Reinforcement learning using online clustering. IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2012, pp. 712-718, Greece. 2012

V. Karavasilis, K. Blekas and C. Nikou. Motion segmentation by a model-based clustering approach of incomplete trajectories. Computer Vision and Image Understanding, vol. 116, pp. 1135-1148, 2012

N. Tziortziotis and K. Blekas. An online kernel-based clustering approach for value function approximation. 7th Hell. Conf. on Artificial Intelligence, (SETN 2012), LNAI 7297, pp. 182-189, Greece, 2012

V.P. Oikonomou, K. Blekas and L. Astrakas. A sparse and spatially constrained generative regression model for fMRI data analysis. IEEE Trans. on Biomedical Engineering, vol. 59 (1), pp. 58-67, 2012.

N. Tziortziotis and K. Blekas. A Bayesian Reinforcement Learning framework Using Relevant Vector Machines. AAAI 2011, San Francisco, pp. 1820-1821, 2011.

N. Tziortziotis and K. Blekas. Value Function Approximation through Sparse Bayesian Modeling, European Workshop on Reinforcement Learning (EWRL 2011), Athens, Lecture notes in Computer Science 7188, pp. 128-139, 2011

V. Karavasilis, K. Blekas and C. Nikou. Motion segmentation by a model-based clustering approach of incomplete trajectories. Proceedings of the ECML PKDD 2011, Athens, pp. 146-161, 2011.

S. Karavarsamis, N. Ntarmos and K. Blekas. InFeRno - an Intelligent Framework for Recognizing Pornographic Web Pages. Proceedings of the ECML PKDD 2011, Athens, pp. 638-641, 2011.

L. Astrakas, K. Blekas, C. Constantinou, et. al. Combining magnetic resonance spectroscopy and molecular genomics offers better accuracy in brain tumor typing and prediction of survival than either methodology alone. Intern. Journal of Oncol ogy, 38(4):1113-27, 2011.

V.P. Oikonomou and K. Blekas. A sparse spatial linear regression model for fMRI data analysis. 6th Hellenic Conference in Artificial Intelligence (SETN-2010) Athens. Lecture Notes in Artificial Intelligence 6040, pp. 203-212, 2010.

K. Blekas, K. Christodoulidou, I.E. Lagaris. Newtonian Spectral Clustering. Inter. Conf. on Artificial. Neural Networks (ICANN 2009) Lecture Notes in Computer Science 5769 LNCS, pp. 145- 154, 2009.

O.C. Andronesi, K. Blekas, D. Mitzopoulos, L. Astrakas, P.M. Black and A. Tzika. Molecular classification of brain tumor biopsies using solid-state magic angle spinning proton magnetic resonance spectroscopy and robust classifiers. Intern. Journal on Oncology, 33(5), pp.1017-1025, 2009.

K. Blekas, C. Nikou, N. Galatsanos and N.V. Tsekos. A regression mixture model with spatial constraints for clustering spatiotemporal data. Intern. Journal on Artificial Intelligence Tools, 17(5), pp.1023-1041, 2008.

K. Blekas, N.P. Galatsanos and A. Likas. A Sparse Regression Mixture Model for Clustering Time-Series. 5th Hellenic Conf. in Artificial Intelligence (SETN-2008) Syros, Greece, 2008, Lecture Notes in Artificial Intelligence, pp. 64-72, 2008.

E. Voudigari and K. Blekas. A marginal mixture model for discovering motifs in sequences. Workshop on ‘Bioinformatics, Genomics and Proteomics on Artificial Intelligence Approach ”, ECAI 2008.

K. Blekas and I.E. Lagaris. Newtonian Clustering: An Approach based on Molecular Dynamics and Global Optimization. Pattern Recognition, vol. 40 (6), pp.1734-1744, 2007.

D. Saougos, G. Manis, K. Blekas and A. Zarras. Revisiting Java Bytecode Compression for Embedded and Mobile Computing Environments. IEEE Trans. on Software Engineering, 33(7), pp. 478-496, 2007.

D.L. Patiris, K. Blekas and K.G. Ioannides. TRIAC II. A MatLab code for track measurements from SSNT detectors. Computer Physics Communications, 177(3), pp. 329-338, 2007.

P. Margariti, K. Blekas, F. Katzioti, A. Zikou, M. Tzoufi and M. Argyropoulou. Magnetization transfer ratio and volumetric analysis of the brain in macrocephalic patients with neurofibromatosis type 1. Journal of European Radiology, vol. 17 (2), pp.433-438, 2007.

M. Argyropoulou, A. Zikou, I. Tzovara, A. Nikas, K. Blekas, P. Margariti, N. Galatsanos, and I. Asproudis. Non- arteritic anterior ischemic optic neuropathy: evaluation of the brain and optic pathway by conventional MRI and magnetization transfer imaging. Journal of European Radiology, 17(7), pp. 1669-1674, 2007.

A. Tzika, L. Astrakas, H. Cao, D. Mintzopoulos, O.C. Andronesi, M. Mindrinos, J. Zhang, L. Rahme, K. Blekas, A. Likas, N.P. Galatsanos and P.M. Black. Combination of high-resolution magic angle spinning proton magnetic resonance spectroscopy and microscale genomics to type brain tumor biopsies. Intern. Journal of Molecular Medicine, vol. 20 (2), pp.199-208, 2007.

K. Blekas and I. E. Lagaris. Split-Merge Incremental Learning (SMILE) of Mixture Models. Inter. Conference on Artificial Neural Networks (ICANN), Porto 2007, Lecture Notes on Artificial Neural Networks, vol.4669, pp.291-300, 2007.

K. Blekas, C. Nikou, N. Galatsanos and N. Tsekos. Curve clustering with spatial constraints for analysis of spatiotemporal data. Intern. Conf. on Tools with Artificial Intelligence (ICTAI) Patras, 29-31 Oct. 2007

D.L. Patiris, K. Blekas and K.G. Ioannides. TRIAC: A code for track measurements using image analysis tools. Nuclear Instruments and Methods in Physics Research, Section B, Vol. 244(2), pp. 392-396, 2006.

A. Kakoliris and K. Blekas. Incremental training of Markov mixture models. ECML 2006 International Workshop on Knowledge Discovery from Data Streams (IWKDDS), pp. 47-56, Berlin, Sep. 2006.

K. Blekas. A mixture model based Markov random fields for discovering probabilistic patterns in sequences. Panhellenic Conference in Artificial Intelligence (SETN-2006) Heraclion, Greece, May 2006, Lecture Notes in Artificial Intelligence, vol. 3955, pp. 25-34, 2006.

C. Nikou, N, Galatsanos, A. Likas and K. Blekas. Image segmentation with a class-adaptive spatially constrained mixture model. EUSIPCO 2006.

K. Blekas, D. Fotiadis and A. Likas. Motif-based Protein Sequence Classification using Neural Networks. Journal of Computational Biology, vol. 12(1), pp. 64-82, 2005.

K. Blekas, A. Likas, N. Galatsanos and I. E. Lagaris. A Spatially-Constrained Mixture Model for Image Segmentation. IEEE Transactions on Neural Networks, Vol. 16(2), pp. 494-498, 2005.

K. Blekas, N. Galatsanos, A. Likas and I. E. Lagaris. Mixture Model Analysis of DNA Microarray Images. IEEE Transactions on Medical Imaging, Vol. 24(7), pp. 901-909, 2005.

K. Blekas, D. Fotiadis and A. Likas. A Sequential Method for Discovering Probabilistic Motifs in Proteins. Methods of Information in Medicine, vol. 43(1), pp. 9-12, 2004.

K. Blekas and A. Likas. Incremental Mixture Learning for Clustering Discrete Data. Proceedings of the 3th Hellenic Conference on Artificial Intelligence (SETN'04) Samos, Greece, May 2004, Lecture Notes in Artificial Intelligence, vol. 3025, pp. 210-219, 2004.

K. Blekas, A. Likas, N. P. Galatsanos and I. E. Lagaris. Mixture model based image segmentation with spatial constraints. Proceedings of the 12th European Signal Processing Conference (EUSIPCO 2004), Vienna, Sep. 2004.

K. Blekas, D. Fotiadis and A. Likas. Greedy Mixture Learning for Multiple Motif Discover in Biological Sequences. Bioinformatics vol. 19(5), pp. 607-617, 2003.

K. Blekas, D. Fotiadis and A. Likas. Protein Sequence Classification using Probabilistic Motifs and Neural Networks. Proc. of the 13th International Conference on Artificial Neural Networks (ICANN 2003), pp. 702-709, Istanbul, 2003.

K. Blekas, N. Galatsanos and I. Georgiou. An Unsupervised Artifact Correction Approach for the Analysis of DNA Microarray Images. IEEE International Conference on Image Processing (ICIP 2003), Vol.2, pp. 165-168, Barcelona, Sep. 2003.

K. Blekas, D. Fotiadis and A. Likas. A Sequential Method for Discovering Probabilistic Motifs in Proteins. Proc. of the 4th International Workshop on Biosignal Interpretation (BSI 2002), pp. 11-14, Como, Italy, June 2002.

K. Blekas, D. Fotiadis, A. Likas and D. Nanou. A Web-based Intelligent System for Personal Health Assistance. Proc.EUNITE Woskshop: Intelligent Systems in Patient Care, pp. 59-65, Vienna, 2001.

S. Pavlopoulos, E. Kyriacoy, D. Koutsouris, K. Blekas, A. Stafylopatis and P. Zoumpoulis. Fuzzy Neural Network Based Characterization of Diffused Liver Diseases Using Image Texture Techniques on Ultrasonic Images. IEEE Engineering in Medicine and Biology Magazine, vol 19(1), pp. 39-47, 2000.

A. Stafylopatis and K. Blekas. Autonomous Vehicle Navigation Using Evolutionary Reinforcement Learning. European Journal of Operational Research, 108(2), pp. 306-318, 1998.

N. Vlassis, K. Blekas, A. Stafylopatis, G. Papakonstantinou, A Vector Quantization Schema for Non-Stationary Signal Distributions Based on ML Estimation of Mixture Densities. Proc. EUSIPCO'98, IX European Signal Processing Conference, Rhodes, Greece, Sep 1998.

K. Blekas, A. Stafylopatis, D. Kontoravdis, A. Likas and P. Karakitsos. Cytological Diagnosis Based on Fuzzy Neural Networks. Journal of Intelligent Systems, vol. 8(1/2), pp. 55-79, 1998.

E. Kyriakoy, S. Pavlopoulos, D. Koutsouris, K. Blekas, A. Stafylopatis and P. Zoumpoulis. Fuzzy Neural Network Based Characterization of Diffused Liver Diseases Using Image Texture Techniques on Ultrasonic Images. VIII Mediterranean Conference on Biological Engineering and Computing (MEDICON'98), pp. 180-184, Lemesos, Cyprus, June 1998.

K. Blekas, A. Likas and A. Stafylopatis. A Fuzzy Neural Network Approach to Classification Based on Proximity Characteristics of Patterns. 9th IEEE International Confenrence on Tools with Artificial Intelligence (ICTAI'97), pp.323-330, California, USA, Nov. 1997.

K. Blekas, G. Papageorgiou and A. Stafylopatis. Continuous Optimization Schemes for Fuzzy Classification. Proc. 13th Int. Conference on Digital Signal Processing, (DSP'97), pp. 265-268, Santorini, Greece, Jul. 1997.

A. Likas, K. Blekas and A. Stafylopatis. Parallel Recombinative Reinforcement Learning: A Genetic Approach. Journal of Intelligent Systems, vol. 6(2), pp. 145-177, 1996.

K. Blekas and A. Stafylopatis. Real-coded Genetic Optimization for Fuzzy Clustering. European Congress on Intelligence Techniques and Soft Computing, EUFIT- 96, pp. 461-465, Aachen, Germany, Sep. 1996.

A. Stafylopatis and K. Blekas. Autonomous Vehicle Navigation Using Evolutionary Reinforcement Learning. Biologically Inspired Autonomous Systems - Computation, Cognition and Action, Durham, North Carolina, Mar. 1996.

A. Likas and K. Blekas. A Reinforcement Learning Approach Based on the Fuzzy Min-Max Neural network. Neural Processing Letters, vol. 4(3), pp. 167-172, 1996.

K. Blekas, A. Likas and A. Stafylopatis. Fuzzy Neural Network Approach Based on Dirichlet Tesselations for Nearest Neighbor Classification of Patterns. Proc. IEEE Int. Workshop on Neural Networks for Signal Processing (NNSP `95), pp. 153-161, Boston, MA, Aug. 1995.

A. Likas, K. Blekas and A. Stafylopatis. Parallel Recombinative Reinforcement Learning.Proc. Machine Learning: ECML- 95, Heraclion, Crete, Greece, Apr. 1995. (Lecture notes in Artificial Intelligence, vol. 912, pp. 311-314, Springer-Verlag, 1995).

A. Likas, K. Blekas and A. Stafylopatis. Application of the Fuzzy Min-Max Neural Network classifier to problems with continuous and discrete attributes. IEEE International Workshop on Neural Networks for Signal Processing (NNSP `94), pp. 163-170, Ermioni, Greece, Sep. 1994.

D. Kontoravdis, A Likas, K. Blekas and A. Stafylopatis. A Fuzzy Neural Network Approach to Autonomous Vehicle Navigation. Proc. European Robotics and Intelligent Systems Conference (EURISCON `94), pp. 243-252, Malaga, Spain, Aug. 1994.