Profil

VLASSIS Nikos

Main Referenced Co-authors
Kröse, B. (9)
Motomura, Y. (8)
Spaan, Matthijs T. J. (8)
Verbeek, J. J. (8)
WILMES, Paul  (7)
Main Referenced Keywords
EM algorithm (4); computational complexity (2); expectation-maximization (EM) algorithm (2); Machine learning (2); machine learning (2);
Main Referenced Unit & Research Centers
Luxembourg Centre for Systems Biomedicine (LCSB): Machine Learning (Vlassis Group) (17)
Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) (5)
Luxembourg Centre for Systems Biomedicine (LCSB): Eco-Systems Biology (Wilmes Group) (5)
Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group) (3)
Luxembourg Centre for Systems Biomedicine (LCSB): Experimental Neurobiology (Balling Group) (3)
Main Referenced Disciplines
Computer science (80)
Biochemistry, biophysics & molecular biology (7)
Microbiology (7)
Environmental sciences & ecology (3)
Electrical & electronics engineering (2)

Publications (total 94)

The most downloaded
4389 downloads
Ballereau, S., Glaab, E., Kolodkin, A., Chaiboonchoe, A., Biryukov, M., Vlassis, N., Ahmed, H., Pellet, J., Baliga, N., Hood, L., Schneider, R., Balling, R., & Auffray, C. (2013). Functional Genomics, Proteomics, Metabolomics and Bioinformatics for Systems Biology. In A. Prokop & B. Csukás (Eds.), Systems Biology: Integrative Biology and Simulation Tools. Springer. doi:10.1007/978-94-007-6803-1_1 https://hdl.handle.net/10993/1247

The most cited

2519 citations (Scopus®)

Likas, A., Vlassis, N., & Verbeek, J. J. (2003). The global k-means clustering algorithm. Pattern Recognition, 36 (2), 451-461. doi:10.1016/S0031-3203(02)00060-2 https://hdl.handle.net/10993/11064

Bernard, F., Vlassis, N., Gemmar, P., Husch, A., Thunberg, J., Goncalves, J., & Hertel, F. (2016). Fast Correspondences for Statistical Shape Models of Brain Structures. In SPIE Medical Imaging. doi:10.1117/12.2206024
Peer reviewed

Salamanca Mino, L., Vlassis, N., Diederich, N., Bernard, F., & Skupin, A. (2015). Improved Parkinson’s disease classification from diffusion MRI data by Fisher vector descriptors. In Improved Parkinson’s disease classification from diffusion MRI data by Fisher vector descriptors (pp. 119-126).
Peer reviewed

Colombo, N., & Vlassis, N. (2015). FastMotif: Spectral Sequence Motif Discovery. Bioinformatics. doi:10.1093/bioinformatics/btv208
Peer reviewed

Laczny, C. C., Sternal, T., Plugaru, V., Gawron, P., Atashpendar, A., Margossian, H. H., Coronado, S., van der Maaten, L., Vlassis, N., & Wilmes, P. (2015). VizBin - an application for reference-independent visualization and human-augmented binning of metagenomic data. Microbiome. doi:10.1186/s40168-014-0066-1
Peer Reviewed verified by ORBi

Vlassis, N., & Glaab, E. (2015). GenePEN: analysis of network activity alterations in complex diseases via the pairwise elastic net. Statistical Applications in Genetics and Molecular Biology, 14 (2), 221-224. doi:10.1515/sagmb-2014-0045
Peer Reviewed verified by ORBi

Muller, E., Pinel, N., Laczny, C. C., Hoopmann, M., Narayanasamy, S., Lebrun, L., Roume, H., Lin, J., May, P., Hicks, N., Buschart, A., Wampach, L., Liu, C., Price, L., Gillece, J., Guignard, C., Schupp, J., Vlassis, N., Baliga, ... Wilmes, P. (2014). Community-integrated omics links dominance of a microbial generalist to fine-tuned resource usage. Nature Communications. doi:10.1038/ncomms6603
Peer Reviewed verified by ORBi

Vlassis, N., & Jungers, R. (May 2014). Polytopic uncertainty for linear systems: New and old complexity results. Systems and Control Letters, 67, 9-13. doi:10.1016/j.sysconle.2014.02.001
Peer Reviewed verified by ORBi

Laczny, C. C., Pinel, N., Vlassis, N., & Wilmes, P. (2014). Alignment-free Visualization of Metagenomic Data by Nonlinear Dimension Reduction. Scientific Reports. doi:10.1038/srep04516
Peer Reviewed verified by ORBi

Laczny, C. C., May, P., Vlassis, N., & Wilmes, P. (March 2014). Identification of condition-specific microbial populations from human metagenomic data [Poster presentation]. VizBi - Visualizing biological data 2014, Heidelberg, Germany.

Colombo, N., & Vlassis, N. (2014). Spectral Sequence Motif Discovery. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/21010.

Thiele, I., Vlassis, N., & Fleming, R. M. (2014). FASTGAPFILL: Efficient gap filling in metabolic networks. Bioinformatics, 30 (17), 2529-2531. doi:10.1093/bioinformatics/btu321
Peer reviewed

Muller, E., Pinel, N., Laczny, C. C., Hoopmann, M., Lebrun, L., Roume, H., May, P., Hicks, N., Liu, C., Price, L., Gillece, J., Guignard, C., Schupp, J., Vlassis, N., Moritz, R., Baliga, N., Keim, P., & Wilmes, P. (2014). Community integrated omics links the dominance of a microbial generalist to fine-tuned resource usage [Paper presentation]. 15th International Symposium on Microbial Ecology.

Muller, E., Pinel, N., Laczny, C. C., Hoopmann, M., Lebrun, L., Roume, H., May, P., Hicks, N., Liu, C., Price, L., Gillece, J., Guignard, C., Schupp, J., Vlassis, N., Moritz, R., Baliga, N., Keim, P., & Wilmes, P. (2014). Community integrated omics links the dominance of a microbial generalist to fine-tuned resource usage [Poster presentation]. Phenotypic heterogeneity and sociobiology of bacterial populations.

Vlassis, N., Pacheco, M., & Sauter, T. (January 2014). Fast reconstruction of compact context-specific metabolic network models. PLoS Computational Biology, 10 (1), 1003424. doi:10.1371/journal.pcbi.1003424
Peer Reviewed verified by ORBi

Vlassis, N., & Jungers, R. M. (2013). Polytopic uncertainty for linear systems: New and old complexity results. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/11098.

Vlassis, N., & Glaab, E. (2013). Network deregulation analysis in complex diseases via the pairwise elastic net. In Proc 8th BeNeLux Bioinformatics Conference. doi:10.1515/sagmb-2014-0045
Peer reviewed

Muller, E., Glaab, E., May, P., Vlassis, N., & Wilmes, P. (2013). Condensing the omics fog of microbial communities. Trends in Microbiology, 21 (7), 325–333. doi:10.1016/j.tim.2013.04.009
Peer reviewed

Blazewicz, J., Kasprzak, M., & Vlassis, N. (2013). Ties between graph theory and biology. In J. L. Gross, J. Yellen, ... P. Zhang (Eds.), Handbook of Graph Theory (2nd). Chapman and Hall/CRC.
Peer reviewed

Ballereau, S., Glaab, E., Kolodkin, A., Chaiboonchoe, A., Biryukov, M., Vlassis, N., Ahmed, H., Pellet, J., Baliga, N., Hood, L., Schneider, R., Balling, R., & Auffray, C. (2013). Functional Genomics, Proteomics, Metabolomics and Bioinformatics for Systems Biology. In A. Prokop & B. Csukás (Eds.), Systems Biology: Integrative Biology and Simulation Tools. Springer. doi:10.1007/978-94-007-6803-1_1
Peer reviewed

Vlassis, N., Pacheco, M., & Sauter, T. (2013). Fast reconstruction of compact context-specific metabolic network models. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/11095.

Vlassis, N., Pacheco, M., & Sauter, T. (2013). Fastcore: An algorithm for fast reconstruction of context-specific metabolic network models. In Proc. 8th BeNeLux Bioinformatics Conference.
Peer reviewed

Vlassis, N., Littman, M. L., & Barber, D. (2012). On the computational complexity of stochastic controller optimization in POMDPs. ACM Transactions on Computation Theory, 4 (4), 1-9. doi:10.1145/2382559.2382563
Peer Reviewed verified by ORBi

Antony, P., Balling, R., & Vlassis, N. (2012). From Systems Biology to Systems Biomedicine. Current Opinion in Biotechnology, 23 (4), 604-8. doi:10.1016/j.copbio.2011.11.009
Peer Reviewed verified by ORBi

Vlassis, N., Littman, M. L., & Barber, D. (2012). Stochastic POMDP controllers: How easy to optimize? [Paper presentation]. 10th European Workshop on Reinforcement Learning, Edinburgh, United Kingdom.

Vlassis, N., Ghavamzadeh, M., Mannor, S., & Poupart, P. (2012). Bayesian Reinforcement Learning. In M. Wiering & M. van Otterlo (Eds.), Reinforcement Learning: State of the Art (pp. 359-386). Springer. doi:10.1007/978-3-642-27645-3_11
Peer reviewed

Vlassis, N., Littman, M. L., & Barber, D. (2011). On the computational complexity of stochastic controller optimization in POMDPs. ORBilu-University of Luxembourg. https://orbilu.uni.lu/handle/10993/3388.

Sifniotis, M., Jackson, B. J. C., Mania, K., Vlassis, N., Watten, P. L., & White, M. (2010). 3D visualization of archaeological uncertainty. In Proc. ACM Symposium on Applied Perception (pp. 162).
Peer reviewed

Vlassis, N., Toussaint, M., Kontes, G., & Piperidis, S. (2009). Learning model-free robot control by a Monte Carlo EM algorithm. Autonomous Robots, 27 (2), 123-130. doi:10.1007/s10514-009-9132-0
Peer reviewed

Vlassis, N., & Toussaint, M. (2009). Model-free reinforcement learning as mixture learning. In Proceedings of the 26th International Conference on Machine Learning (pp. 1081-1088).
Peer reviewed

Oliehoek, F. A., Spaan, M. T. J., & Vlassis, N. (2008). Optimal and approximate Q-value functions for decentralized POMDPs. Journal of Artificial Intelligence Research, 32, 289-353. doi:10.1613/jair.2447
Peer Reviewed verified by ORBi

Kuyer, L., Whiteson, S., Bakker, B., & Vlassis, N. (2008). Multiagent Reinforcement Learning for Urban Traffic Control Using Coordination Graphs. In Proceedings of 19th European Conference on Machine Learning (pp. 656-671).
Peer reviewed

Oliehoek, F. A., Spaan, M. T. J., Vlassis, N., & Whiteson, S. (2008). Exploiting locality of interaction in factored Dec-POMDPs. In Int. Joint Conf. on Autonomous Agents and Multi-Agent Systems (pp. 517-524).
Peer reviewed

Spaan, M. T. J., Oliehoek, F. A., & Vlassis, N. (2008). Multiagent Planning under Uncertainty with Stochastic Communication Delays. In 338 Proceedings of the Eighteenth International Conference on Automated Planning and Scheduling (ICAPS 2008) (pp. 338-345).
Peer reviewed

Poupart, P., & Vlassis, N. (2008). Model-based Bayesian reinforcement learning in partially observable domains. In Proc Int. Symp. on Artificial Intelligence and Mathematics (pp. 1-2).
Peer reviewed

Oliehoek, F. A., Kooij, J. F. P., & Vlassis, N. (2008). The cross-entropy method for policy search in decentralized POMDPs. Informatica, 32 (4), 341-357.
Peer reviewed

Vlassis, N. (2007). Distributed Decision Making for Robot Teams. In Proc. 1st Int. Symp. on Intelligent and Distributed Computing.
Peer reviewed

Oliehoek, F. A., & Vlassis, N. (2007). Q-value Heuristics for Approximate Solutions of Dec-POMDPs. In Proc. AAAI Spring Symp. on Game Theoretic and Decision Theoretic Agents.
Peer reviewed

Oliehoek, F. A., & Vlassis, N. (2007). Q-value functions for decentralized POMDPs. In Proc Int. Joint Conf. on Autonomous Agents and Multi-Agent Systems (pp. 833-840).
Peer reviewed

Kurihara, K., Welling, M., & Vlassis, N. (2007). Accelerated variational dirichlet process mixtures. In Advances in Neural Information Processing Systems 19 (pp. 761-768). MIT Press.
Peer reviewed

Diplaros, A., Vlassis, N., & Gevers, T. (2007). A spatially constrained generative model and an EM algorithm for image segmentation. IEEE Transactions on Neural Networks, 18 (3), 798-808. doi:10.1109/TNN.2007.891190
Peer Reviewed verified by ORBi

Vlassis, N. (2007). A Concise Introduction to Multiagent Systems and Distributed Artificial Intelligence. San Rafael, United States - California: Morgan & Claypool.

Oliehoek, F. A., de Jong, E. D., & Vlassis, N. (2006). The Parallel Nash Memory for Asymmetric Games. In Proc. Genetic and Evolutionary Computation Conference, Seattle, WA (pp. 337-344).
Peer reviewed

Poupart, P., Vlassis, N., Hoey, J., & Regan, K. (2006). An analytic solution to discrete Bayesian reinforcement learning. In Proc Int. Conf. on Machine Learning, Pittsburgh, USA (pp. 697-704).
Peer reviewed

James, M. R., Wessling, T., & Vlassis, N. (2006). Improving Approximate Value Iteration Using Memories and Predictive State Representations. In Proc. National Conf. on Artificial Intelligence.
Peer reviewed

Verbeek, J. J., & Vlassis, N. (2006). Gaussian fields for semi-supervised regression and correspondence learning. Pattern Recognition, 39 (10), 1864-1875. doi:10.1016/j.patcog.2006.04.011
Peer Reviewed verified by ORBi

Kok, J. R., & Vlassis, N. (2006). Collaborative multiagent reinforcement learning by payoff propagation. Journal of Machine Learning Research, 7, 1789-1828.
Peer Reviewed verified by ORBi

Porta, J. M., Vlassis, N., Spaan, M. T. J., & Poupart, P. (2006). Point-Based Value Iteration for Continuous POMDPs. Journal of Machine Learning Research, 7, 2329-2367.
Peer Reviewed verified by ORBi

Verbeek, J. J., Nunnink, J. R. J., & Vlassis, N. (2006). Accelerated EM-based clustering of large data sets. Data Mining & Knowledge Discovery, 13 (3), 291-307. doi:10.1007/s10618-005-0033-3
Peer reviewed

Spaan, M. T. J., Gordon, G. J., & Vlassis, N. (2006). Decentralized planning under uncertainty for teams of communicating agents. In Proc. Int. Joint Conf. on Autonomous Agents and Multiagent Systems, Hakodate, Japan (pp. 249-256).
Peer reviewed

Kok, J. R., & Vlassis, N. (2006). Using the max-plus algorithm for multiagent decision making in coordination graphs. In Proc. RoboCup Int. Symposium, Osaka, Japan (pp. 1-12).
Peer reviewed

Spaan, M. T. J., & Vlassis, N. (2005). Planning with Continuous Actions in Partially Observable Environments. In Proc. IEEE Int. Conf. on Robotics and Automation (pp. 3458-3463).
Peer reviewed

Spaan, M. T. J., & Vlassis, N. (2005). Perseus: Randomized point-based value iteration for POMDPs. Journal of Artificial Intelligence Research, 24, 195-220. doi:10.1613/jair.1659
Peer Reviewed verified by ORBi

Verbeek, J. J., Vlassis, N., & Krose, B. J. A. (2005). Self-organizing mixture models. Neurocomputing, 63, 99-123. doi:10.1016/j.neucom.2004.04.008
Peer Reviewed verified by ORBi

Porta, J. M., Spaan, M. T. J., & Vlassis, N. (2005). Robot planning in partially observable continuous domains. In Proc. Robotics: Science and Systems (pp. 217-224).
Peer reviewed

Kok, J. R., Hoen, E. J., Bakker, B., & Vlassis, N. (2005). Utile coordination: Learning interdependencies among cooperative agents. In EEE Symp. on Computational Intelligence and Games, Colchester, Essex (pp. 29-36).
Peer reviewed

Kowalczyk, W., & Vlassis, N. (2005). Newscast EM. In Advances in Neural Information Processing Systems 17 (pp. 713-720). MIT Press.
Peer reviewed

Vlassis, N., Sfakianakis, Y., & Kowalczyk, W. (2005). Gossip-based greedy Gaussian mixture learning. In Lecture Notes in Computer Science (pp. 349-359). Springer-Verlag. doi:10.1007/11573036_33
Peer reviewed

Kok, J. R., Spaan, M. T. J., & Vlassis, N. (2005). Non-communicative multi-robot coordination in dynamic environments. Robotics and Autonomous Systems, 50 (2-3), 99-114. doi:10.1016/j.robot.2004.08.003
Peer Reviewed verified by ORBi

Spaan, M. T. J., & Vlassis, N. (2004). A point-based POMDP algorithm for robot planning. In Proc. IEEE Int. Conf. on Robotics and Automation, New Orleans, Louisiana (pp. 2399-2404).
Peer reviewed

Vlassis, N., Elhorst, R., & Kok, J. R. (2004). Anytime Algorithms for Multiagent Decision Making Using Coordination Graphs. In Proceedings of the International Conference on Systems, Man and Cybernetics (pp. 953-957). New York, United States - New York: Institute of Electronics Engineers.

Diplaros, A., Gevers, T., & Vlassis, N. (2004). Skin detection using the EM algorithm with spatial constraints. In Proc. Int. Conf. on Systems, Man and Cybernetics.
Peer reviewed

Kok, J. R., & Vlassis, N. (2004). Sparse Cooperative Q-learning. In Proc. 21st Int. Conf. on Machine Learning, Banff, Canada (pp. 481-488).
Peer reviewed

Verbeek, J. J., Roweis, S. T., & Vlassis, N. (2004). Non-linear CCA and PCA by Alignment of Local Models. In Advances in Neural Information Processing Systems 16 (pp. 297-304). San Mateo, United States - California: Morgan Kaufmann Publishers.
Peer reviewed

Kröse, B., Bunschoten, R., ten Hagen, S., Terwijn, B., & Vlassis, N. (2004). Household robots look and learn. IEEE Robotics and Automation Magazine, 11 (4), 45-52. doi:10.1109/MRA.2004.1371608
Peer Reviewed verified by ORBi

Kok, J. R., Spaan, M. T. J., & Vlassis, N. (2003). Multi-Robot Decision Making Using Coordination Graphs. In Proceedings of the International Conference on Advanced Robotics (ICAR) (pp. 1124-1129).
Peer reviewed

Verbeek, J. J., Vlassis, N., & Kröse, B. J. A. (2003). Self-Organization by Optimizing Free-Energy. In Proc. of European Symposium on Artificial Neural Networks (pp. 125-130).
Peer reviewed

Verbeek, J. J., Vlassis, N., & Kröse, B. (2003). Efficient Greedy Learning of Gaussian Mixture Models. Neural Computation, 15 (2), 469-485. doi:10.1162/089976603762553004
Peer Reviewed verified by ORBi

Likas, A., Vlassis, N., & Verbeek, J. J. (2003). The global k-means clustering algorithm. Pattern Recognition, 36 (2), 451-461. doi:10.1016/S0031-3203(02)00060-2
Peer Reviewed verified by ORBi

Verbeek, J. J., Vlassis, N., & Kröse, B. (2002). A k-segments algorithm for finding principal curves. Pattern Recognition Letters, 23 (8), 1009-1017. doi:10.1016/S0167-8655(02)00032-6
Peer Reviewed verified by ORBi

Verbeek, J. J., Vlassis, N., & Kröse, B. (2002). Fast Nonlinear Dimensionality Reduction With Topology Preserving Networks. In Proceedings of the Tenth European Symposium on Artificial Neural Networks (pp. 193-198).
Peer reviewed

Verbeek, J. J., Vlassis, N., & Kröse, B. (2002). Fast nonlinear dimensionality reduction with topology representing networks. In Proc. Europ. Symp. on Artificial Neural Networks.
Peer reviewed

Vlassis, N., & Likas, A. (2002). A greedy EM algorithm for Gaussian mixture learning. Neural Processing Letters, 15 (1), 77-87. doi:10.1023/A:1013844811137
Peer reviewed

Vlassis, N., Terwijn, B., & Krose, B. (2002). Auxiliary particle filter robot localization from high-dimensional sensor observations. In IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, VOLS I-IV, PROCEEDINGS (pp. 7-12).
Peer reviewed

Verbeek, J. J., Vlassis, N., & Kröse, B. J. A. (2002). Coordinating Principal Component Analyzers. In Proc. Int. Conf. on Artificial Neural Networks, Madrid, Spain (pp. 914-919). Berlin, Germany: Springer.
Peer reviewed

Vlassis, N., Motomura, Y., & Kröse, B. (2002). Supervised dimension reduction of intrinsically low-dimensional data. Neural Computation, 14 (1), 191 - 215. doi:10.1162/089976602753284491
Peer Reviewed verified by ORBi

Vlassis, N., & Motomura, Y. (2001). Efficient source adaptivity in independent component analysis. IEEE Transactions on Neural Networks, 12 (3), 559-566. doi:10.1109/72.925558
Peer Reviewed verified by ORBi

Vlassis, N., Bunschoten, R., & Kröse, B. (2001). Learning Task-Relevant Features From Robot Data. In IEEE International Conference on Robotics and Automation, 2001. Proceedings 2001 ICRA (pp. 499 - 504).
Peer reviewed

Verbeek, J. J., Vlassis, N., & Krose, B. (2001). A soft k-segments algorithm for principal curves. In ARTIFICIAL NEURAL NETWORKS-ICANN 2001, PROCEEDINGS (pp. 450-456).
Peer reviewed

Asoh, H., Vlassis, N., Motomura, Y., Asano, F., Hara, I., Hayamizu, S., Itou, K., Kurita, T., Matsui, T., Bunschoten, R., & Kröse, B. (2001). Jijo-2: An office robot that communicates and learns. IEEE Intelligent Systems, 16 (5), 46-55. doi:10.1109/5254.956081
Peer Reviewed verified by ORBi

Vlassis, N., Motomura, Y., Hara, I., Asoh, H., & Matsui, T. (2001). Edge-based features from omnidirectional images for robot localization. In Proc. IEEE Int. Conf. on Robotics and Automation.
Peer reviewed

Vlassis, N. (2001). Fast score function estimation with application in ICA. In Proc. Int. Conf. on Artificial Neural Networks.

Krose, B. J. A., Vlassis, N., Bunschoten, R., & Motomura, Y. (2001). A probabilistic model for appearance-based robot localization. Image and Vision Computing, 19 (6), 381-391. doi:10.1016/S0262-8856(00)00086-X
Peer Reviewed verified by ORBi

Vlassis, N., Motomura, Y., & Krose, B. (2000). Supervised linear feature extraction for mobile robot localization. In Proc. IEEE Int. Conf. on Robotics and Automation (pp. 2979 - 2984).
Peer reviewed

Vlassis, N., & Krose, B. (1999). Robot environment modeling via principal component regression. In Proc. of Intelligent Robots and Systems, International Conference on (pp. 677 - 682).
Peer reviewed

Vlassis, N., & Kröse, B. (1999). Mixture Conditional Density Estimation with the EM Algorithm. In Proc. 9th Int. Conf. on Artificial Neural Networks.
Peer reviewed

Vlassis, N., Papakonstantinou, G., & Tsanakas, P. (1999). Mixture density estimation based on Maximum Likelihood and test statistics. Neural Processing Letters, 9 (1), 63-76. doi:10.1023/A:1018624029058
Peer reviewed

Vlassis, N., Motomura, Y., & Kröse, B. (1999). An information-theoretic localization criterion for robot map building. In Proc. ACAI'99, Int. Conf. on Machine Learning and Applications.
Peer reviewed

Kröse, B., Bunschoten, R., Vlassis, N., & Motomura, Y. (1999). Appearance-Based Robot Localization. In Proc. IJCAI'99, 16th Int. Joint Conf. on Artificial Intelligence, ROB-2 Workshop.
Peer reviewed

Vlassis, N., & Likas, A. (1999). A kurtosis-based dynamic approach to Gaussian mixture modeling. IEEE Transactions on Systems, Man and Cybernetics. Part A, Systems and Humans, 29 (4), 393-399. doi:10.1109/3468.769758
Peer reviewed

Vlassis, N., Papakonstantinou, G., & Tsanakas, P. (1998). Dynamic Sensory Probabilistic Maps for Mobile Robot Localization. In Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems.
Peer reviewed

Vlassis, N., & Tsanakas, P. (1998). A Sensory Uncertainty Field Model for Unknown and Non-stationary Mobile Robot Environments. In Proc. IEEE Int. Conf. on Robotics and Automation.
Peer reviewed

Vlassis, N., Dimopoulos, A., & Papakonstantinou, G. (1997). The Probabilistic Growing Cell Structures Algorithm. In Proc. Int. Conf. on Artificial Neural Networks.
Peer reviewed

Vlassis, N., Sgouros, N. M., Efthivoulidis, G., Papakonstantinou, G., & Tsanakas, P. (1996). Global Path Planning for Autonomous Qualitative Navigation. In Proc. 8th IEEE Int. Conf. on Tools with AI.
Peer reviewed

Efthivoulidis, G., Vlassis, N., Tsanakas, P., & Papakonstantinou, G. (1996). An Experiment for Truly Parallel Logic Programming. Journal of Intelligent and Robotic Systems, 16 (2), 169-184. doi:10.1007/BF00449704
Peer reviewed

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