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AIC Seminar Series

Computational Discovery of Scientific Models: Guiding Search with Knowledge and Data

Pat LangleyInstitute for the Study of Learning and Expertise, and Department of Computer Science, University of Auckland[Home Page]

Notice:  Hosted by Karen Myers

Date:  Thursday, May 16th 2019 at 4:00pm

Location:  EK255 (SRI E building)  (Directions)

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Remote attendance is possible via VTC. Please contact for connection details if needed.

Scientific discovery was long viewed as a uniquely human creative activity, but digital computers can now reproduce many facets of this process. In this talk, I present my research on computational systems that discover scientific knowledge. This posits that discovery involves search through a space of hypotheses, laws, or models, and that this search is guided both by domain knowledge and by regularities in data. I focus on one paradigm – inductive process modeling – that encodes models as sets of processes incorporating differential equations, induces these models from observational data, and uses background knowledge to aid in their construction. I report results of this approach on data sets from ecology and environmental science, showing it produces both accurate and interpretable models.

I also report a new framework that, by adopting a few simplifying assumptions, produces more accurate fits and scales far better to complex models. In closing, I discuss open challenges for research on scientific discovery. In addition, I briefly review my contributions to other areas of AI, machine learning, and cognitive systems.

This talk describes joint work with Adam Arvay, Will Bridewell, Saso Dzeroski, Ljupco Todorovski, and many others.

Langley, P., & Arvay, A. (2015). Heuristic induction of rate-based process models. Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence (pp. 537-543). Austin, TX: AAAI Press.

Arvay, A., & Langley, P. (2016). Selective induction of rate-based process models. Proceedings of the Fourth Annual Conference on Cognitive Systems. Evanston, IL.

   Bio for Pat Langley

Dr. Pat Langley serves as Director of the Institute for the Study of Learning and Expertise and as Honorary Professor of Computer Science at the University of Auckland. He has contributed to artificial intelligence and cognitive science for more than 40 years, having published over 300 papers and five books on these topics. Dr. Langley developed some of the first computational approaches to scientific knowledge discovery, and he was an early champion of both experimental studies of machine learning and its application to real-world problems. He is the founding editor of two journals, Machine Learning in 1986 and Advances in Cognitive Systems in 2012, and he is a Fellow of both AAAI and the Cognitive Science Society. Dr. Langley’s current research focuses on architectures for intelligent agents, abductive methods for plan understanding, and induction of explanatory scientific models.

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