Welcome to the class on neuro-symbolic modeling. This lecture lays the groundwork for the rest of the semester. We will see a broad introduction to the idea of neuro-symbolic modeling which seeks to integrate the predictive capacity of neural networks with the expressiveness and reasoning capability of declaratively stated knowledge.
This lecture will also go over policies and administrivia for this semester’s class.
Lecture and Resources
Readings
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Kautz, Henry. 2022. “The Third AI Summer: AAAI Robert S. Engelmore Memorial Lecture.” AI Magazine 43 (1): 105–25. https://doi.org/10.1002/aaai.12036.
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Darwiche, Adnan. 2020. “Three Modern Roles for Logic in AI.” In Proceedings of the 39th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems, 229–43. PODS’20. New York, NY, USA: Association for Computing Machinery. https://doi.org/10.1145/3375395.3389131.
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Marra, Giuseppe, Sebastijan Dumančić, Robin Manhaeve, and Luc De Raedt. 2021. “From Statistical Relational to Neurosymbolic Artificial Intelligence: A Survey.” arXiv.Org. August 25, 2021. https://arxiv.org/abs/2108.11451v4.
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Kambhampati, Subbarao, Sarath Sreedharan, Mudit Verma, Yantian Zha, and Lin Guan. 2022. “Symbols as a Lingua Franca for Bridging Human-AI Chasm for Explainable and Advisable AI Systems.” In Proceedings of the 36th AAAI Conference On Artificial Intelligence. https://aaai-2022.virtualchair.net/poster_blue153.