Researcher

Weiss, G.B.

Full professor - Key domain chair, Professor

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  1. 2019
  2. Published
  3. 2018
  4. Published
    Weiss, G., & Chen, S. (2018). Automated negotiation: An efficient approach to interaction among agents. In Interactions in Multiagent Systems (pp. 149-177). World Scientific Publishing Company.
  5. Published
  6. Published
    Ghaleb, E., Popa, M., Hortal, E., Asteriadis, S., & Weiss, G. (2018). Towards Affect Recognition through Interactions with Learning Materials. In 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)
  7. 2017
  8. Published
  9. Published
    Vrancken, T., Tenbrock, D., Reick, S., Bozhinovski, D., Weiss, G., & Spanakis, G. (2017). Multi-Agent Parking Place Simulation. In Y. Demazeau, P. Davidsson, J. Bajo, & Z. Vale (Eds.), Advances in Practical Applications of Cyber-Physical Multi-Agent Systems: The PAAMS Collection: 15th International Conference, PAAMS 2017, Porto, Portugal, June 21-23, 2017, Proceedings (pp. 272-283). Springer. https://doi.org/10.1007/978-3-319-59930-4_22
  10. Published
    Sorger, B., Tumanov, K., Benitez Andonegui, A., Lührs, M., Boeijkens, H., Weiss, G., ... Möckel, R. (2017). An fNIRS-based brain-machine interface for remote robot control. Paper presented at Real-Time Functional Imaging and Neurofeedback Conference, Nara, Japan.
  11. Published
    Spanakis, G., & Weiss, G. (2017). Enhancing Visual Clustering Using Adaptive Moving Self-Organizing Maps (AMSOM). In Agents and Artificial Intelligence (pp. 189-211). (Lecture Notes in Computer Science; Vol. 10162). https://doi.org/10.1007/978-3-319-53354-4_11
  12. Published
    Chen, S., Hao, J., Zhou, S., & Weiss, G. (2017). Negotiating with unknown opponents: Toward multi-lateral agreement in real-time domains. In K. Fuijta (Ed.), Modern Approaches to Agent-based Complex Automated Negotiation (pp. 219-229). (Studies in Computational Intelligence; Vol. 674). Springer Verlag.
  13. 2016
  14. Published
  15. Published
    Spanakis, G., Weiss, G., & Roefs, A. (2016). Enhancing Classification of Ecological Momentary Assessment Data Using Bagging and Boosting. In IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI 2016) (pp. 388-395). IEEE. https://doi.org/10.1109/ICTAI.2016.0066
  16. Published
  17. Published
  18. Published
  19. Published
    Spanakis, G., Weiss, G., & Roefs, A. (2016). Bagged Boosted Trees for Classification of Ecological Momentary Assessment Data. In ECAI 2016 - 22nd European Conference on Artificial Intelligence, 29 August-2 September 2016, The Hague, The Netherlands - Including Prestigious Applications of Artificial Intelligence (PAIS 2016) (pp. 1612-1613) https://doi.org/10.3233/978-1-61499-672-9-1612
  20. Published
  21. Published
    Chen, S., Weiss, G., & Zhou, S. (2016). Solving negotiation problems against unknown opponents with wisdom of crowds. In Advances in Artificial Intelligence (pp. 126-133). (Lecture Notes in Computer Science; Vol. 9904). Springer Verlag.
  22. Published
    Chen, S., Zhou, S., Weiss, G., & Tuyls, K. (2016). Using transfer learning to model unknown opponents in automated negotiations. In N. Fukuta, T. Ito, M. Zhang, K. Fujita, & V. Robu (Eds.), Recent advances in agent-based complex automated negotiation (pp. 175-192). (Studies in Computational Intelligence). Springer.
  23. Published
  24. 2015
  25. Published
  26. Published
    Wittlinger, C., Spanakis, G., & Weiss, G. (2015). Flexible Deep Neural Network structure with application to Natural Language Processing. In BNAIC 2015 THE 27TH BENELUX CONFERENCE ON ARTIFICIAL INTELLIGENCE (Vol. 1)
  27. Published
  28. Published
    Ranjbar-Sahraei, B., Tuyls, K., Caliskanelli, I., Broeker, B., Claes, D., Alers, S., & Weiss, G. (2015). Bio-inspired multi-robot systems. In T. D. Ngo (Ed.), Biomimetic technologies. Principles and applications. (pp. 273-299). Woodhead Publishing Ltd.
  29. Published
  30. Published
    Efremova, J., Ranjbar-Sahraei, B., Rahmani, H., Oliehoek, F., Calders, T., Tuyls, K., & Weiss, G. (2015). Multi-source entity resolution for genealogical data. In G. Boothooft, P. Christen, K. Mandemakers, & M. Schraagen (Eds.), Population reconstruction (pp. 129-154). Springer Verlag.
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