When: Tuesday, June 08, 3:30 p.m.

Where: 5409 Wean Hall

Pat Langley, Head, Computational Learning Laboratory
Director, Institute for the Study of Learning and Expertise
Center for the Study of Language and Information
Stanford University

AI Seminar

Abstract:
The growing amount of scientific data has led to the increased use of computational discovery methods to understand and interpret them. However, most work has relied on knowledge-lean techniques like clustering and classification learning, which produce descriptive rather than explanatory models, and it has utilized formalisms developed in AI or statistics, so that results seldom make contact with current theories or scientific notations. In this talk, I present an approach to computational discovery that encodes explanatory scientific models as sets of quantitative processes, simulates these models' behavior over time, incorporates background knowledge to constrain model construction, and induces these models from time-series data in a robust manner. I illustrate this framework on data and models from Earth science and microbiology, two domains in which explanatory process accounts occur frequently. In closing, I describe our progress toward an interactive software environment for the construction, evaluation, and revision of such explanatory scientific models.

This talk describes joint work with Kevin Arrigo, Nima Asgharbeygi, Stephen Bay, Andrew Pohorille, and Jeff Shrager.

BIO
Dr. Pat Langley's research focuses on machine learning and knowledge discovery. He has published over 100 papers on this topic and related aspects of AI, he has edited or authored five books in the area, including the textbook, Elements of Machine Learning, and he was the founding editor of the journal Machine Learning. Dr. Langley's work has contributed to methods for rule induction, probabilistic learning, and case-based reasoning, and he has applied these techniques to a variety of problem areas. His current research emphasizes adaptive user interfaces, which invoke machine learning to construct user models based on interaction with their users. Dr. Langley received his PhD from Carnegie Mellon University in 1979, and he has worked in academia, in government, and in industry. He currently serves as Director of the Institute for the Study of Learning and Expertise, as Head of the Adaptive Systems Group at the Daimler-Benz Research & Technology Center, and as a Consulting Professor at Stanford University.

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