Current A.I. research in our Department includes natural language understanding and dialog modeling, automated reasoning, planning, statistical models and more powerful programming languages for A.I. and other systems development. Research in neural networks and genetic algorithms is done by joint faculty in the Departments of Physics and Electrical and Computer Engineering. The group has a strong analytical focus, but has also implemented important leading-edge systems in each of the areas mentioned above. We also have experience in components of other A.I. subfields such as program synthesis, robotics, machine learning, expert systems, search analysis, and game playing. The faculty in the A.I. group are listed below.
More detailed information appears on subsequent pages.
Computational linguistics, automatic programming, learning and inference. In the area of natural language processing systems, we seek to develop a class of task-oriented natural language processors that enable a human and machine to collaborate efficiently in problem solving. Our work primarily concentrates on the development of theories of semantics appropriate for such systems with particular emphasis on mechanisms for handling complex noun-phrase resolution, focusing mechanisms, dialogue comprehension, and user models. In order to test theories, we continuously build natural language interactive systems and study their performance in typical problem-solving situations with human subjects. This experimental work provides examples of human-machine dialogue for study and yields data related to the achieved level of efficiency.