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Institute for Logic, Language and Computation

Searchable List of Research Output

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  • Alechina, N.A., van Lambalgen, M. (1995) Generalized quantification as substructural logic.
    technical Report. onbekend (FdL).
    Report | UvA-DARE
  • Alechina, N.A., van Lambalgen, M. (1996) Generalized quantification as substructural logic.
    Journal of Symbolic Logic, Vol. 61 (pp 1006-1044)
  • Alechina, N.A. (1995) On a decidable generalized quantifier logic corresponding to a decidable fragment of first-order logic.
    Journal of Logic, Language and Information, Vol. 4 (pp 177-189)
    Article | UvA-DARE
  • Alechina, N.A. (1995) Modal quantifiers.
    Institute for Logic, Language and Computation.
    Thesis, fully internal | UvA-DARE
  • Alechina, N.A. (1995) Logic with Probabilistic Operators.
    In Proceedings ACCOLADE'94 (pp 121-138)
    Chapter | UvA-DARE
  • Alechina, N.A. (1995) For All Typical.
    In Symbolic and Quantitative Approach to reasoning and Uncertainty. Proceedings ECSQARU'95 (pp 1-8). Springer.
    Chapter | UvA-DARE
  • Alechina, N.A. (1995) Logic with Probabilistic Operators.
    In Proceedings ACCOLADE'94 (pp 121-138)
    Chapter | UvA-DARE
  • Alechina, N.A. (1995) For All Typical.
    In Symbolic and Quantitative Approach to Reasoning and Uncertainty. Proceedings ECSQARU'95 (pp 1-8). Springer.
    Chapter | UvA-DARE
  • Alexiadou, A., Giannakidou, A. (1999) Specificational pseudoclefts as lists.
    In Shahin, K. Blake, S. Kim, E.-U. (Eds.), Proceedings of the West Coast Conference on Formal Linguistics (WCCFL) XVII (pp 1-16). CSLI Publications.
    Conference contribution | UvA-DARE
  • Alhama, R.G., Scha, R.J.H., Zuidema, W. (2014) Rule Learning in Humans and Animals.
    In Cartmill, E.A. Roberts, S. Lyn, H. Cornish, H. (Eds.), The Evolution of Language: proceedings of the 10th International Conference (EVOLANG10), Vienna, Austria, 14-17 April 2014 (pp 371-372). World Scientific.
  • Alhama, R.G., Scha, R.J.H., Zuidema, W. (2014) Rule Learning in Humans.
  • Alhama, R.G., Scha, R.J.H., Zuidema, W. (2015) How should we evaluate models of segmentation in artificial language learning?.
    In Taatgen, N.A. van Vugt, M.K. Borst, J.P. Mehlhorn, K. (Eds.), Proceedings of ICCM 2015: 13th International Conference on Cognitive Modeling : April 9-11, Groningen, The Netherlands (pp 172-173). University of Groningen.
  • Alhama, R.G., Scha, R.J.H., Zuidema, W. (2015) How should we evaluate models of segmentation in artificial language learning?.
    Poster | UvA-DARE
  • Alhama, R.G., Zuidema, W. (2016) Pre-Wiring and Pre-Training: What does a neural network need to learn truly general identity rules?.
    In Besold, T.R. Bordes, A. d'Avila Garcez, A. Wayne, G. (Eds.), Proceedings of the Workshop on Cognitive Computation: Integrating neural and symbolic approaches 2016: co-located with the 30th Annual Conference on Neural Information Processing Systems (NIPS 2016) : Barcelona, Spain, December 9, 2016 (CEUR Workshop Proceedings, Vol. 1773). CEUR-WS.
  • Alhama, R.G., Zuidema, W. (2016) Generalization in Artificial Language Learning: Modelling the Propensity to Generalize.
    In Korhonen, A. Lenci, A. Murphy, B. Poibeau, T. Villavicencio, A. (Eds.), The 54th Annual Meeting of the Association for Computational Linguistics: proceedings of the 7th Workshop on Cognitive Aspects of Computational Language Learning: August 11, 2016, Berlin, Germany (pp 64-72). Association for Computational Linguistics.
    Conference contribution | https://doi.org/10.18653/v1/W16-19 | UvA-DARE
  • Alhama, R.G., Zuidema, W. (2017) Segmentation as Retention and Recognition: the R&R model.
    In Gunzelmann, G. Howes, A. Tenbrink, T. Davelaar, E.J. (Eds.), CogSci 2017: proceedings of the 39th Annual Meeting of the Cognitive Science Society : London, UK : 26-29 July 2017 : Computational Foundations of Cognition (pp 1531-1536). Cognitive Science Society.
  • Alhama, R.G., Zuidema, W. (2018) Pre-wiring and pre-training: What does a neural network need to learn truly general identity rules?.
    Journal of Artificial Intelligence Research, Vol. 61 (pp 927-946)
  • Alhama, R.G., Zuidema, W. (2019) A review of computational models of basic rule learning: The neural-symbolic debate and beyond.
    Psychonomic Bulletin and Review, Vol. 26 (pp 1174-1194)
  • Alhama, R.G. (2017) Computational modelling of Artificial Language Learning: Retention, Recognition & Recurrence.
    Thesis, fully internal | UvA-DARE
  • Aliseda-Llera, A. (1997) Seeking Explanations: Abduction in Logic, Philosophy of Science and Artificial Intelligence.
    Universiteit van Amsterdam.
    Thesis, fully external | UvA-DARE
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