Knowledge Representation and Reasoning is about how to represent knowledge in a form that is suitable for automated reasoning on computer. Current research focuses on reasoning under uncertainty using Bayesian networks, representing and reasoning about actions in dynamic domains, and representing and reasoning about strategic information in planning. Logic Programming concerns the use of logic as a programming language and explores computational models based on controlled deduction. Current research interests rest in incorporating functional and object-oriented programming paradigms into the framework and developing tools for specifying distributed systems. Machine Learning aims to develop computational models of learning, a crucial feature in simulating natural intelligence.
Current research focuses on learning in various statistical models.
Natural Language Processing concerns computational techniques for understanding and manipulating human language. Current research focuses on statistical, neural network, and machine learning techniques to improve the accuracy and coverage of interpretation, translation, and cognitive language models, and their applications to Chinese information processing. Neurocomputing is based on massively parallel networks of simple computing elements, inspired by biological nervous systems. One research focus is the theory and applications of the self-modifying capabilities of artificial neural networks. Another focus is modelling biological neural networks as complex systems that exhibit sophisticated emergent behaviour.