. . . Michail Maniadakis

Postdoctoral Researcher
Computational Vision and Robotics Laboratory, Institute of Computer Science,
Foundation fro Research and Technolohy, Hellas (FORTH)
GREECE

Who am I...

I received a BSc in Informatics from the University of Piraeus, Greece, in 1996 and an MSc in Systems Automation from the Technical University of Crete, Greece, in 1999.

From 2001 to 2006, I was a PhD student working in the area of Biologically Inspired RoboticSystems, in the Computer ScienceDepartment, University of Crete,Greece. My research work aimed at developing a computationalframework that supports the long-term process of developing complexbrain-like cognitive systems for robots. My work has been supervised by Prof. Panos Trahanias and it has been supported by the Computational Vision and Robotics Laboratory of FORTH-ICS.

My PhD Thesis is entitled: Design and Integration of Agent-Based Partial Brain Models for Robotic Systems by means of Hierarchical Cooperative CoEvolution (Awarded Distinction by University of Crete).

From April 2007 to March 2008, I worked in the Laboratory for Behavior and Dynamic Cognition, Brain Science Institute, RIKEN, Japan, under thesupervision of Jun Tani. I have been also a visiting researcher in the same lab, from April 2008 to March 2010.

Currenlty I am working as a PostDoctoral Researcher in the ComputationalVision and Robotics Laboratory, FORTH, Greece, and I continue collaborating with the Laboratory for Behavior and Dynamic Cognition, RIKEN, Japan, as a visiting researcher untill March 2011.

My News
- I will discuss the key role of time in developing artificial cognition, in the VBC PhD Symposium 2013, Vienna, Austria, November 7-8, 2013. This year, the symposium is dedicated to the topic "Biology of Time".
- I have been invited to coordinate the editorial of a forthcoming Frontiers Research Topic with title Towards embodied artificial cognition: TIME is on my side. The editorial board includes also Marc Wittmann and Sylvie Droit-Volet.
- I will present the Entimed Cognition concept in the 2013 Spring School on Developmental Robotics and Cognitive Bootstrapping (a joint ROBOTDOC/ POETICON++ school), Athens, Greece, Match 18-20, 2013.

Research Interests

The last years my research has mainly focused on the time perception and time processing capacities of robotic systems.This largely unexplored dimmension of artificial cognition, plays a key role in the development of autonomous and intelligent systems.

Time is involved in nearly all aspects of our daily life. In the way our experiences are stored, the way we learn new skills based on previous knowledge, the way we jointly recall past events with friends, the way we make long term plans and schedule our future acivities, and many more. Time is everywhere.

How about robots? Is the robot you are working with, aware about the flow of time? Is it aware that your life is time-structured? I assume... no. This might be the reason why robots can not sufficiently bridge self and societal past, present and future. High and low level robotic cognitive processes must be "entimed" in order to facilitate the seamless integration of artificial agents into the (inherently time-structured) dynamic world.

Besides time, I am also interested in high-level cognition. Overall, my research interests are listed below:

You may check early results of my ongoing work focused on time perception and particularly on Duration Comparison and Duration Reproduction. Currently I am investigating mechanisms that can jointly perform both Duration Comparison and Duration Reproduction. Hopefully, I will upload more results... soon.

Publications

JOURNALS
  1. M. Maniadakis, P. E. Trahanias, J. Tani Self-organizing high-order cognitive functions in artficial agents: implications for possible prefrontal cortex mechanisms, Neural Networks, Vol 33, 76–87, 2012.
  2. M. Maniadakis, P. E. Trahanias, Temporal cognition: a key ingredient of intelligent systems, Frontiers in Neurorobotics, vol 5, 2011.
  3. M. Maniadakis, P. E. Trahanias, J. Tani, Explorations on Artificial Time Perception, Neural Networks, vol 22(5-6), pp 509-517, 2009.
  4. M. Maniadakis, P. E. Trahanias, Agent-based Brain Modelling for Artificial Organisms by means of Hierarchical Cooperative CoEvolution, Artificial Life, vol 15(3), pp 293-336, 2009.
  5. M. Maniadakis, J. Tani, Acquiring Rules for Rules: Neuro-Dynamical Systems Account for Meta-Cognition, Adaptive Behavior, vol 17(1), pp.58-80, 2009.
  6. M. Maniadakis, P. E. Trahanias, Hierarchical Cooperative CoEvolution: Presentation and Assessment Study, International Journal of AI Tools, vol 18(1), pp 99-120, 2009.
  7. M. Maniadakis, P. E. Trahanias, Hierarchical CoEvolution of Cooperating Agents Acting in the Brain-Arena , Adaptive Behavior, vol 16, pp 221-245, 2008.
  8. P. Fattori, R. Breveglieri, N. Marzocchi, M. Maniadakis, C. Galletti, Brain area V6A: a cognitive model for an embodied artificial intelligence, Lecture Notes in Artificial Intelligence - 50 Years of Artificial Intelligence, vol. 4850, pp.206-220, 2007.
  9. M. Maniadakis, P. E. Trahanias, Modelling Robotic Cognitive Mechanisms by Hierarchical Cooperative CoEvolution, International Journal of AI Tools, vol. 16(6), pp. 935-966, 2007.
  10. M. Maniadakis, P. E. Trahanias, Modelling brain emergent behaviours through coevolution of neural agents, Neural Networks, 19(5), 705-720, 2006 .
  11. G.A. Rovithakis, M. Maniadakis, M. Zervakis, A Hybrid Neural Network/Genetic Algorithm Approach to Optimizing Feature Extraction for Signal Classification, IEEE Transactions on Systems, Man and Cybernetics, Part B, vol. 34(1), pages 695- 703, 2004.
  12. H. Surmann, M. Maniadakis, Learning feed-forward and recurrent fuzzy systems: a genetic approach, Journal of Systems Architecture, vol 47, no 7, pp. 649-662, 2001.
  13. G.A. Rovithakis, M. Maniadakis, M. Zervakis, G. Fillipidis, G. Zacharakis, A. Katsamouris, T. Papazoglou, “Artificial Neural Networks for Discriminating Pathologic from Normal Peripheral Vascular Tissue”, IEEE Trans. On Biomedical Engineering, vol 48, no 10, pp. 1088-1097, 2001.
CONFERENCES
  1. M. Maniadakis, P. Trahanias Is Time the Next Big Thing in Robot Cognition?, accepted for oral presentation in the Cognitive Neuroscience Robotics Workshop, IEEE/RSJ International Conference on Intelligent Robots and System 2012 (IROS), Vilamoura, Portugal, 2012.
  2. M. Maniadakis, P. Trahanias Time in robot cognition: an emerging research branch, accepted for oral presentation in the First International Conference on Time Perspective (ICTP), Coimbra, Portugal, 2012.
  3. M. Maniadakis, P. Trahanias, Experiencing and Processing Time with Neural Networks, accepted for presentation in the 4th International Conference on Advanced Cognitive Technologies and Applications (COGNITIVE), Nice, France, 2012.
  4. M. Maniadakis, J. Tani, P. Trahanias, Ego-centric and allo-centric abstraction in self-organized hierarchical neural networks, in Proc. combined IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EPIROB), 2011.
  5. M. Maniadakis, M. Wittmann, P. Trahanias, Time Experiencing by Robotic Agents, in Proc. European Symposium on Artificial Neural Networks (ESANN), 2011.
  6. M. Maniadakis, P. Trahanias, J. Tani, Self-Organized Executive Control Functions, in Proc. International Joint Conference On Neural Networks (IJCNN), pp. 3633-3640, 2010.
  7. M. Maniadakis, J. Tani, P. Trahanias, Time perception in shaping cognitive neurodynamics in artificial agents, in Proc. International Joint Conference On Neural Networks (IJCNN), pp. 1993-2000, 2009.
  8. M. Maniadakis, J. Tani, Dynamical Systems Account for Meta-Level Cognition, in Proc. of the 10th International Conference on the Simulation of Adaptive Behavior (SAB), pp. 311-320, 2008.
  9. M. Maniadakis, P. Trahanias, Assessing Hierarchical Cooperative CoEvolution, in Proc. 19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pp. 391-398, 2007.
  10. M. Maniadakis, E. Hourdakis, P. Trahanias, Modelling Overlapping Execution/Observation Pathways, in Proc. International Joint Conference On Neural Networks (IJCNN), pp.1255-1260, 2007.
  11. E. Hourdakis, M. Maniadakis, P. Trahanias, A biologically inspired approach for the control of the hand, in Proc. Congress on Evolutionary Computation (CEC), pp.1503-1510, 2007.
  12. M. Maniadakis, P. E. Trahanias, Hierarchical Cooperative CoEvolution Facilitates the Redesign of Agent-based Systems, in Proc. 9th International Conference on the Simulation of Adaptive Behavior (SAB), pp. 582-593, 2006.
  13. M. Maniadakis, P. E. Trahanias, Design and Integration of Partial Brain Models Using Hierarchical Cooperative CoEvolution, in Proc. International Conference on Cognitive Modelling (ICCM), pp. 196-201, 2006.
  14. M. Maniadakis, P. E. Trahanias, Modelling Robotic Cognitive Mechanisms by Hierarchical Cooperative CoEvolution, in Proc. 4th Hellenic Conference on Artificial Intelligence (SETN), pp. 224-234, 2006.
  15. M. Maniadakis, P. E. Trahanias, Distributed Brain Modelling by means of Hierarchical Collaborative CoEvolution, Proc. IEEE Congress on Evolutionary Computation, (CEC), pp. 2699-2706, 2005.
  16. M. Maniadakis, P. E. Trahanias, CoEvolutionary Incremental Modelling of Robotic Cognitive Mechanisms, Proc. VIIIth European Conference on Artificial Life (ECAL), pp.200-209, 2005.
  17. M. Maniadakis, P. E. Trahanias, A Hierarchical Coevolutionary Method to Support brain-Lesion Modelling, Proc. International Joint Conference on Neural Networks (IJCNN), pp. 434-439, 2005.
  18. M. Maniadakis, P. E. Trahanias, Evolution Tunes Coevolution: Modelling Robot Cognition Mechanisms, Proc. GECCO 2004, pp. 640-641, 2004.
  19. M. Maniadakis, P. E. Trahanias, A Computational Model of Neocortical-Hippocampal Cooperation and Its Application to Self-Localization, Proc. VIIth European Conference on Artificial Life (ECAL), pp. 183-190, 2003.
  20. G. Filippidis, G. Zacharakis, A. Katsamouris, G. A. Rovithakis, M. Maniadakis, M. Zervakis, T. G. Papazoglou, Artificial neural networks analysis of laser-induced fluorescence spectra for charcterization of peripheral vascular tissue, Proc. SPIE Vol. 4158, Biomonitoring and Endoscopy Technologies, Israel Gannot; Yuri V. Gulyaev; Theodore G. Papazoglou; Christiaan F. van Swol; Eds., pp. 199-208, 2001.
  21. G.A. Rovithakis, M. Maniadakis, and M. Zervakis, A genetically optimized artificial neural network structure for feature extraction and classification of vascular tissue luorescence spectrums, International Workshop on Computer Architectures for Machine Perception, CAMP 2000
  22. G. Filippidis, G. Zacharakis, A. Katsamouris, G.A. Rovithakis, M. Maniadakis, M. Zervakis, and T.G. Papazoglou, Artificial Neural Network analysis of laser-induced fluorescence spectra for characterization of peripheral vascular tissue, EOS/SPIE European Biomedical Optics (EBIOS), 2000.
  23. G. Rovithakis, M. Maniadakis, M. Zervakis, Artificial Neural Networks for Feature Extraction and Classification of Vascular Tissue Fluorescence Spectrums, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2000.
  24. G.A. Rovithakis, M. Maniadakis, M. Zervakis, G. Fillipidis, G. Zacharakis, T. Papazoglou Optimization of a ANN Diagnostic System using GAs, Second Panhellinic Conference on Biomedical Technology 1999.
  25. G.A. Rovithakis, M. Maniadakis, M. Zervakis, G. Fillipidis, G. Zacharakis, T. Papazoglou, Vascular Tissue Characterization using Laser Fluorescence Spectrums and ANNs, Second Panhellinic Conference on Biomedical Technology, 1999.
  26. M. Maniadakis, H. Surmann A Genetic Algorithm for Structural and Parametrical Tuning of Fuzzy Systems, European Symposium on Intelligent Techniques (ESIT), 1999.

Invited Talks

  • M. Maniadakis, Do Robots Sense the Flow of Time? EUROCOGSCI 2011, Symposium Current advances on Time perception: Psychophysical, Neuronal, and Applied Perspectives, 21-24 May, 2011.
  • M. Maniadakis, Time Perception as a Key Ingredient of Robotic Intelligence, 1st International Workshop on the Multidisciplinary Aspects of Time Perception, 7-8 October, 2010.
  • M. Maniadakis, Panos Trahanias and Jun Tani, Self-organizing neural mechanisms for higher-order cognitive functions in human prefrontal cortex, Brain Lunch Seminar, RIKEN, 31 July 2009.

    • Other

  • M. Maniadakis, P. Trahanias, Executive Control in Artificial Agents, ERCIM News, 84, 32-33, 2011
  • M. Maniadakis, P. Trahanias, High Level Cognition for Artificial Agents in FORTH Retreat, Loutra Kyllinis, Greece, October 2009.
  • M. Maniadakis, J. Tani, Executive Control Dynamics, in RIKEN-BSI Retreat, 2008.
  • M. Maniadakis, J. Tani, Investigating Mental Shifting Mechanisms in Dynamical Systems, in RIKEN-BSI Retreat, 2007.
  • M. Maniadakis, Hierarchical Cooperative CoEvolution: A New Tool to Assist Brain Modeling Efforts, RIKEN BSI, December 14, 2006.
  • M. Maniadakis, P. Trahanias, Mechanisms for Cognition Development Inspired by the Mammalian Paradigm, ERCIM News, 55, 12-13, 2003.

    • Free Time

    In my free time, I enjoy cooking for friends and myself (when nobody is around).
    I love greek traditional food with gourmet touches, but I am also fun of asian and particularly japanese food!
    I am now trying to train my 18-month-old daughter in liking gourmet dishes. This is a top secret, you know, but my wife doesn't. So please say nothing if you meet her around. ;-)

    In addition to cooking, I have co-founded the www.foodadvisor.gr that is dedicated in reviewing restaurants around Greece. Try it!
    I very much enjoy visiting tavernas and restaurants around Greece in order to taste new disches and share my experiences with friends in the foodadvisor.gr community.