Artificial Intelligence & Cognitive Engineering

University of Groningen > Faculty of Science and Engineering > Faculty Board FSE > FSE Research > Bernoulli Institute > Artificial Intelligence & Cognitive Engineering

  1. 2018
  2. Mohades Kasaei, H., Lopes, L. S., & Tomé, A. M. (2018). Coping with Context Change in Open-Ended Object Recognition without Explicit Context Information. Paper presented at 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems, Madrid, Spain. https://doi.org/10.1109/IROS.2018.8593922
  3. 2017
  4. Okafor, E., Pawara, P., Karaaba, M., Surinta, O., Codreanu, V., Schomaker, L., & Wiering, M. (2017). Comparative study between deep learning and bag of visual words for wild-animal recognition. In 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 (2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SSCI.2016.7850111
  5. 2016
  6. 2015
  7. Renardel de Lavalette, G., Ghosh, S. (Ed.), & Szymanik, J. (Ed.) (2015). Infinitary hybrid logic and the Lindelöf property. In The Facts Matter. Essays on Logic and Cognition in Honour of Rineke Verbrugge: Tributes (Vol. 25, pp. 113-120). College Publications.
  8. 2014
  9. Kruitbosch, H. T., Giotis, I., & Biehl, M. (2014). Segmented Shape-Symbolic Time Series Representation. In M. Verleysen (Ed.), Proceedings of the 22. European Symposium on Artificial Neural Networks ESANN d-side publishing.
  10. 2013
  11. Ghosh, S., & de Jongh, D. (2013). Comparing strengths of beliefs explicitly. Logic Journal of the IGPL, 21(3), 488-514.
  12. Kooi, B., & van Ditmarsch, H. (2013). Honderd gevangenen en een gloeilamp. (Epsilon uitgaven). Utrecht: Epsilon Uitgaven.
  13. van der Ree, M., & Wiering, M. (2013). Reinforcement Learning in the Game of Othello: Learning Against a Fixed Opponent and Learning from Self-Play. In Proceedings of IEEE International Symposium on Adaptive Dynamic Programming and Reinforcement Learning: ADPRL
  14. 2012
  15. Mostowski, M., & Szymanik, J. (2012). Semantic bounds for everyday language. Semiotica, 2012(188), 323-332. https://doi.org/10.1515/sem-2012-0022
  16. Nijboer, M., Taatgen, N. A., & van Rijn, H. (2012). Choices, Choices: Task Selection Preference During Concurrent Multitasking. In N. Russwinkel, U. Drewitz, & H. Van Rijn (Eds.), Proceedings of the 11th international Conference on Cognitive Modeling (pp. 241-242). Berlin: Universitätsverlag der TU Berlin.
  17. Van Rijn, H., & Nijboer, M. (2012). Optimaal feiten leren met ICT. 4W: Weten Wat Werkt en Waarom, 1, 6 - 11.
  18. Szymanik, J., & Robaldo, L. (2012). Pragmatic Identification of the Witness Sets. In N. Calzolari (Ed.), Proceedings of the 8th Conference on Language resources and Evaluation Istanbul: European Language Resources Association (ELRA).
  19. 2011
  20. van Rooij, I., Kwisthout, J., Blokpoel, M., Szymanik, J., Wareham, T., & Toni, I. (2011). Intentional communication: Computationally easy or difficult? Frontiers in Human Neuroscience, 5, [52]. https://doi.org/10.3389/fnhum.2011.00052
  21. Gierasimczuk, N., & Szymanik, J. (2011). A note on a generalization of the muddy children puzzle. In K. Apt (Ed.), Proceedings of the 13th Conference on Theoretical Aspects of Rationality and Knowledge (pp. 257-264). ACM Press Digital Library.
  22. Visser, T., Andringa, T., & Verbrugge, L. (2011). Affective agents bridge teh gap between life and mind. In T. Froese, M. Egbert, & X. Barandiaran (Eds.), Workshop on Artificial Autonomy ECAL 2011: Twenty Years of Practice of Autonomous Systems (pp. 7-12). Paris.
  23. Baltag, A., Gierasimczuk, N., & Smets, S. (2011). Belief revision as a truth tracking process. In K. Apt (Ed.), Proceedings of TARK (pp. 187-190). New York: ACM Press.
  24. Kontinen, J., & Szymanik, J. (2011). Characterizing definability of second-order generalized quantifiers. In L. D. Beklemishev, & R. de Quieroz (Eds.), Logic, Language, Information and Computation (Vol. 8652, pp. 187-200). (Lecture Notes on Computer Science; Vol. 8652). Berlin: Springer.
  25. Gierasimczuk, N., & Szymanik, J. (2011). Invariance properties of quantifiers and multiagent information exchange. In M. Kanazawa (Ed.), Proceedings of the 12th Meeting on Mathematics of Language (Vol. 6878, pp. 72-89). Berlin: Lecture Notes in Artificial Intelligence.
  26. Dégremont, C., Kurzen, L., & Szymanik, J. (2011). On the tractability of comparing informational structures. In J. van Eijck, & R. Verbrugge (Eds.), Proceedings of the Workshop on reasoning About Other Minds: Logical and Cognitive Perspectives (Vol. 751, pp. 50-64). CEUR Workshop Proceedings.
  27. Kooi, B., Ditmarsch, H. V., & Hoek, W. V. D. (2011). Reasoning about local properties in modal logic. In K. Tumer, P. Yolum, L. Sonenberg, & P. Stone (Eds.), Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems (pp. 711-718). Richland: IFAAMAS.
  28. Bhowmik, T. K., van Oosten, J-P., & Schomaker, L. (2011). Segmental K-Means Learning with Mixture Distribution for HMM Based Handwriting Recognition. In SO. Kuznetsov, DP. Mandal, MK. Kundu, & SK. Pal (Eds.), PATTERN RECOGNITION AND MACHINE INTELLIGENCE (pp. 432-439). (Lecture Notes in Computer Science; Vol. 6744). BERLIN: Springer.
  29. Dégremont, C., Lowe, B., & Witzel, A. (2011). The synchronicity of dynamic epistemic logic. In K. R. Apt (Ed.), TARK XIII: Proceedings of the 13th Conference on Theoretical Aspects of Rationality and Knowledge (pp. 145-152). ACM Press Digital Library.
  30. 2010
  31. Borst, J. P., Taatgen, N. A., & Van Rijn, D. H. (2010). Locating the neural correlates of the problem state resource: Analyzing fMRI data on the basis of a computational model. In G. Gunzelmann, & D. D. Salvucci (Eds.), Proceedings of ICCM - 2010- Tenth International Conference on Cognitive Modeling (pp. 287-288). Philadelphia: PA.
  32. Taatgen, N. A., & Van Rijn, D. H. (2010). Nice graphs, Good R2, but still a poor fit? How to be more sure your model explains your data. In D. D. Salvucci, & G. Gunzelmann (Eds.), Proceedings of ICCM - 2010- Tenth International Conference on Cognitive Modeling (pp. 247-252). Philadelphia, PA.
  33. van Valkenhoef, G., van der Vaart, E. E., & Verbrugge, L. (2010). OOPS: An S5n prover for educational settings. In Proceedings of the 6th Workshop on Methods for Modalities (M4M-6): Revised version in Electronic Notes in Theoretical Computer Science (Vol. 162, pp. 249-261) https://doi.org/10.1016/j.entcs.2010.04.018
  34. Andringa, T. (2010). Smart, general, and situation specific sensors. In Smarter sensors, easier processing: 11th International Conference on the Simulation of Adaptive behavior (SAB2010) Paris.
  35. Andringa, T. (2010). Soundscape and Core Affect Regualtion. In Interspeech 2010 Lisbon.
  36. Andringa, T., & Krijnders, J. (2010). IPC No. WO/2010/107. Texture Based Signal Analysis and Recognition. (Patent No. PVT/NL2010/050144).
  37. Meijering, B., Van Maanen, L., Van Rijn, H., & Verbrugge, R. (2010). The facilitative effect of context on second-order social reasoning. In S. Ohlsson, & R. Catrambone (Eds.), Proceedings of the 32nd Annual Conference of the Cognitive Science Society (pp. 1423-1428). Austin, TX: Cognitive Science Society.
  38. 2009
  39. Slingerland, S., Mulder, M., van der Vaart, E. E., & Verbrugge, L. (2009). A multi-agent systems approach to gossip and the evolution of language. In N. A. Taatgen, & R. van Rijn (Eds.), Proceedings of the 31st Annual Meeting of the Cognitive Science Society (pp. 1609-1614). Austin: Cognitive Science Society.
  40. Verhoef, T., Lisetti, C., Barreto, A., Ortega, F., van der Zant, C., & Cnossen, F. (2009). Bio-sensing for Emotional Characterization without Word Labels. In J. A. Jacko (Ed.), Proceedings of the 13th International Conference on Human Computer Interaction (Vol. 5612, pp. 693-702). (Lecture Notes in Computer Science). Springer.
  41. Niessen, M., Kootstra, G., de Jong, S., & Andringa, T. (2009). Expectancy-based robot navigation through context evaluation. In H. R. Arabnia, D. de la Fuente, & J. Angel Olivas (Eds.), Proceedings of the 2009 International Conference on Artificial Intelligence (pp. 371-377). Las Vegas.
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