Searchable List of Research Output

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  • Alechina, N. A. (1995). Logic with Probabilistic Operators. In Proceedings ACCOLADE'94 (pp. 121-138) >>>
  • Alechina, N. A. (1995). For All Typical. In Symbolic and Quantitative Approach to Reasoning and Uncertainty. Proceedings ECSQARU'95 (pp. 1-8). Springer. >>>
  • Alexiadou, A., & Giannakidou, A. (1999). Specificational pseudoclefts as lists. In K. Shahin, S. Blake, & E.-U. Kim (Eds.), Proceedings of the West Coast Conference on Formal Linguistics (WCCFL) XVII (pp. 1-16). CSLI Publications. >>>
  • Alhama, R. G., Scha, R., & Zuidema, W. (2014). Rule Learning in Humans and Animals. In E. A. Cartmill, S. Roberts, H. Lyn, & H. Cornish (Eds.), The Evolution of Language: proceedings of the 10th International Conference (EVOLANG10), Vienna, Austria, 14-17 April 2014 (pp. 371-372). World Scientific. https://doi.org/10.1142/9789814603638_0049 >>>
  • Alhama, R. G., Scha, R., & Zuidema, W. (2015). How should we evaluate models of segmentation in artificial language learning? In N. A. Taatgen, M. K. van Vugt, J. P. Borst, & K. Mehlhorn (Eds.), Proceedings of ICCM 2015: 13th International Conference on Cognitive Modeling : April 9-11, Groningen, The Netherlands (pp. 172-173). University of Groningen. http://www.cognitive-modeling.com/proceedings/ICCM2015_proceedings.pdf >>>
  • Alhama, R. G., & Zuidema, W. (2016). Pre-Wiring and Pre-Training: What does a neural network need to learn truly general identity rules? In T. R. Besold, A. Bordes, A. d'Avila Garcez, & G. Wayne (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 Article 4 (CEUR Workshop Proceedings; Vol. 1773). CEUR-WS. http://ceur-ws.org/Vol-1773/CoCoNIPS_2016_paper4.pdf >>>
  • Alhama, R. G., & Zuidema, W. (2016). Generalization in Artificial Language Learning: Modelling the Propensity to Generalize. In A. Korhonen, A. Lenci, B. Murphy, T. Poibeau, & A. Villavicencio (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. https://doi.org/10.18653/v1/W16-19 >>>
  • Alhama, R. G., & Zuidema, W. (2017). Segmentation as Retention and Recognition: the R&R model. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. J. Davelaar (Eds.), CogSci 2017: proceedings of the 39th Annual Meeting of the Cognitive Science Society : London, UK : 26-29 July 2017 : Computational Foundations of Cognition (Vol. 2, pp. 1531-1536). Cognitive Science Society. https://cogsci.mindmodeling.org/2017/papers/0300/index.html >>>
  • 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, 61, 927-946. https://doi.org/10.1613/jair.1.11197 >>>
  • Aliseda-Llera, A. (1997). Seeking Explanations: Abduction in Logic, Philosophy of Science and Artificial Intelligence. [Thesis, fully external, Universiteit van Amsterdam]. Universiteit van Amsterdam. >>>
  • Aliseda-Llera, A. (1997). Abduction as a model for belief revision. In Proceedings of the Taller de Logica y Computacion, Primer Encuentro de Computacion (pp. 1-7). Universidad de Queretaro. >>>
  • Aljanaki, A., Bountouridis, D., Burgoyne, J. A., Van Balen, J., Wiering, F., Honing, H., & Veltkamp, R. (2014). Designing Games with a Purpose for Data Collection in Music Research. Emotify and Hooked: Two Case Studies. In A. De Gloria (Ed.), Games and Learning Alliance: Second International Conference, GALA 2013, Paris, France, October 23-25, 2013 : revised selected papers (pp. 29-40). (Lecture Notes in Computer Science; Vol. 8605). Springer. https://doi.org/10.1007/978-3-319-12157-4_3 >>>
  • Allcock, J., Buhrman, H., & Linden, N. (2009). Arbitrarily little knowledge can give a quantum advantage for nonlocal tasks. Physical Review A, 80(3), 032105. https://doi.org/10.1103/PhysRevA.80.032105 >>>
  • Allender, E., Buhrman, H., Friedman, L., & Loff, B. (2012). Reductions to the set of random strings: The resource-bounded case. In B. Rovan, V. Sassone, & P. Widmayer (Eds.), Mathematical Foundations of Computer Science 2012: 37th International Symposium, MFCS 2012, Bratislava, Slovakia, August 27-31, 2012 : proceedings (pp. 88-99). (Lecture Notes in Computer Science; Vol. 7464). Springer. https://doi.org/10.1007/978-3-642-32589-2_11 >>>
  • Allender, E., Buhrman, H., Friedman, L., & Loff, B. (2014). Reductions to the set of random strings: The resource-bounded case. Logical Methods in Computer Science, 10(3), Article 5. https://doi.org/10.2168/LMCS-10(3:5)2014 >>>
  • Allender, E., Buhrman, H. M., Kouck, M., van Melkebeek, D., & Ronneburger, D. (2002). Power from random strings. In Proceedings of the 43d annual IEEE conference on Foundations of Computer Science (pp. 669-678). IEEE Computer Society Press. >>>
  • Allender, E., Buhrman, H. M., Koucky, M., van Melkebeek, D., & Ronneburger, D. (2006). Power from random strings. SIAM Journal on Computing, 35(6), 1467-1493. https://doi.org/10.1137/050628994 >>>
  • Allender, E., Buhrman, H. M., & Koucky, M. (2006). What can be efficiently reduced to the kolmogorov-random strings? Annals of Pure and Applied Logic, 138(1-3), 2-19. https://doi.org/10.1016/j.apal.2005.06.003 >>>
  • Allo, P., Poggiolesi, F., & Smets, S. (2015). Dynamics in Logic: Introduction. Logique et Analyse, 58(230), 143-144. https://doi.org/10.2143/LEA.230.0.3141806 >>>
  • Allo, P. (Guest ed.), Poggiolesi, F. (Guest ed.), & Smets, S. (Guest ed.) (2015). Dynamics in Logic. Logique et Analyse, 58(230), 143-346. http://poj.peeters-leuven.be/content.php?url=issue&journal_code=LEA&issue=0&vol=230 >>>

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