Neural-symbolic computing seeks to benefit from the integration of symbolic AI and neural computation. In a neurosymbolic 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 explainability. Neural-symbolic computing has found application in many areas including software systems specification, investment decision support, training and assessment in simulators, the prediction of harm in gambling for consumer protection. In this talk, I will introduce the principles of neurosymbolic AI and will exemplify its use in applications where the neural-symbolic approach has been successful and will conclude by discussing the main challenges for the research and development of neurosymbolic AI in the next decade.
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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 Neurosymbolic AI. Garcez is president of the Neural-Symbolic Learning and Reasoning Association (www.neural-symbolic.org), 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, CNPq Brazil, the Royal Society, Innovate UK, ESRC and EPSRC UK, and from industry.