Goodale, M. A. and Milner, A. D. (1992). Separate visual pathways for perception and action. Trends in Neurosciences, 15(1):20-25. 1
Gordon, D., Kembhavi, A., Rastegari, M., Redmon, J., Fox, D., and Farhadi, A. (2018). Iqa: Visual question answering in interactive environments. In Conference on Computer Vision and Pattern Recognition (CVPR), 3, 4, 6, 10
Hafner, D. (2021). Benchmarking the spectrum of agent capabilities. In International Conference on Learning Representations (ICLR). 3
Hanson, N. R. (1965). Patterns of discovery: An inquiry into the conceptual foundations of science. CUP Archive. 2
He, P., Gao, J., and Chen, W. (2022). Debertav3: Improving deberta using electra-style pre-training with gradient-disentangled embedding sharing. In International Conference on Learning Representations (ICLR). 7
Hessel, J., Hwang, J. D., Park, J. S., Zellers, R., Bhagavatula, C., Rohrbach, A., Saenko, K., and Choi, Y. (2022). The abduction of sherlock holmes: A dataset for visual abductive reasoning. In European Conference on Computer Vision (ECCV). 2, 3, 4
Hobbs, J. R., Stickel, M. E., Appelt, D. E., and Martin, P. (1993). Interpretation as abduction. Artificial intelligence, 63(1-2):69-142. 2
Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. *Neural Computation*, 9(8):1735-1780. A4
Johnson, J., Hariharan, B., Van Der Maaten, L., Fei-Fei, L., Lawrence Zitnick, C., and Girshick, R. (2017). Clevr: A diagnostic dataset for compositional language and elementary visual reasoning. In Conference on Computer Vision and Pattern Recognition (CVPR). 3, 4
Johnson, M., Hofmann, K., Hutton, T., and Bignell, D. (2016). The malmo platform for artificial intelligence experimentation. In International Joint Conference on Artificial Intelligence (IJCAI). 4
Josephson, J. R. and Josephson, S. G. (1996). Abductive inference: Computation, philosophy, technology. Cambridge University Press. 2, 3
Kakas, A. C., Kowalski, R. A., and Toni, F. (1992). Abductive logic programming. Journal of Logic and Computation, 2(6):719-770. 3
Kayser, M., Camburu, O.-M., Salewski, L., Emde, C., Do, V., Akata, Z., and Lukasiewicz, T. (2021). e-vil: A dataset and benchmark for natural language explanations in vision-language tasks. In Conference on Computer Vision and Pattern Recognition (CVPR). 2
Kenton, J. D. M.-W. C. and Toutanova, L. K. (2019). Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of NAACL-HLT. 7
Kocisky, T., Schwarz, J., Blunsom, P., Dyer, C., Hermann, K. M., Melis, G., and Grefenstette, E. (2018). The narrativeqa reading comprehension challenge. Transactions of the Association for Computational Linguistics (TACL), 6:317-328. 10
Lei, J., Yu, L., Bansal, M., and Berg, T. L. (2018). Tvqa: Localized, compositional video question answering. In Annual Conference on Empirical Methods in Natural Language Processing (EMNLP). 10, A5
Li, L., Chen, Y.-C., Cheng, Y., Gan, Z., Yu, L., and Liu, J. (2020). Hero: Hierarchical encoder for video+ language omni-representation pre-training. In Annual Conference on Empirical Methods in Natural Language Processing (EMNLP). A5
Li, Q., Huang, S., Hong, Y., Zhu, Y., Wu, Y. N., and Zhu, S.-C. (2023). A minimalist dataset for systematic generalization of perception, syntax, and semantics. In International Conference on Learning Representations (ICLR). 3
Li, Q., Zhu, Y., Liang, Y., Wu, Y. N., Zhu, S.-C., and Huang, S. (2022). Neural-symbolic recursive machine for systematic generalization. arXiv preprint arXiv:2210.01603. 3
Liang, C., Wang, W., Zhou, T., and Yang, Y. (2022). Visual abductive reasoning. In Conference on Computer Vision and Pattern Recognition (CVPR). 2, 3, 4
Magnani, L. (2011). magnani2011abduction. Springer Science & Business Media. 3