Neural-symbolic computing seeks to benefit from the integration of symbolic AI and neural computation. In a neural-symbolic system, neural networks offer the machinery for efficient learning and computation, while symbolic knowledge representation and reasoning enables the use of prior knowledge, transfer learning and extrapolation, and explainable AI. Neural-symbolic computing has found application in many areas including software systems specification, training and assessment in simulators, the prediction of harm in gambling for consumer protection. In this talk, I will introduce the principles of neural-symbolic computing and will exemplify its use with an emphasis on the combination of deep learning and first-order logic as used by Logic Tensor Networks. I will identify applications where the neural-symbolic approach has been successful and will conclude by discussing the main challenges of the research and development of neural-symbolic AI.
About the speaker
Professor Artur d’Avila Garcez, FBCS, is the Director of the Research Centre for Machine Learning at City, University of London, and the Chair of The City Data Science Institute. He holds a Ph.D. in Computer Science (2000) from Imperial College London. He co-authored two books: Neural-Symbolic Cognitive Reasoning (Springer, 2009) and Neural-Symbolic Learning Systems (Springer, 2002), and has more than 150 peer-reviewed publications in the areas of Artificial Intelligence, Machine Learning, Neural Computing and Neural-Symbolic AI. Garcez is president of the Neural-Symbolic Learning and Reasoning Association, associate editor of the Journal of Logic and Computation and the IEEE Transactions on Neural Networks and Learning Systems, and editor of the Machine Learning Journal special track on learning and reasoning. He has served on the programme committees of all the major conferences in machine learning, artificial intelligence and neural computation, including IJCAI, NeurIPS, ECAI, ICML, AAAI, AAMAS and IJCNN. His research has received funding from the Nuffield foundation, the EU, the Daiwa Foundation, the Royal Society, Innovate UK, ESRC and EPSRC UK, CAPES-Brazil, and from industry.