Neuro-Symbolic Artificial Intelligence: A Brief Tutorial


The field of artificial intelligence (AI) has seen several proposals for modeling intelligent computation. Two of the most popular ones are (1) neural – which is inspired by the structure of our brain and consists of millions of nodes resembling neurons connected in a network, and (2) symbolic – which uses the formalism of logic to make inferences from known facts. While deep neural models have revolutionized the field of AI in modern times, an emerging body of work combines neural models with symbolic computation to achieve the best of both worlds. In this introductory tutorial, we briefly present some of this literature in the context of (1) augmenting neural models by incorporating additional symbolic knowledge, (2) designing neural models for solving symbolic reasoning problems, and, (3) neuro-symbolic architectures for solving perceptual-reasoning tasks.


Mausam is the founding head of School of Artificial Intelligence, along with being a Professor of Computer Science at IIT Delhi. He is also an affiliate professor at University of Washington, Seattle. With a twenty year research experience in artificial intelligence, he has, over time, contributed to many research areas such as large scale information extraction over the Web, AI approaches for optimizing crowdsourced workflows, and probabilistic planning algorithms. More recently, his research is exploring neuro-symbolic machine learning, computer vision for radiology, NLP for robotics, multilingual NLP, and several threads in intelligent information systems that include information extraction, knowledge base completion, question answering, summarization and dialogue systems. He has over 100 archival papers to his credit, along with a book, and two best paper awards. Mausam was awarded the AAAI Senior Member status in 2015 for his long-term participation in AAAI and distinction in the field of artificial intelligence. He has had the privilege of being a program chair for two top conferences, AAAI 2021, and ICAPS 2017. He was ranked the 65th most influential AI scholar and 71st most influential NLP scholar for the last decade by ArnetMiner. He received his PhD from University of Washington in 2007 and a B.Tech. from IIT Delhi in 2001.

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