Seminar: Pat LANGLEY

13 October 2016

15 – 17

Sala Lauree

Dipartimento di Lingue e Letterature Straniere e Culture Moderne

Palazzo Badini

via Verdi 10, Torino



Pat LANGLEY (University of Auckland)


Computational discovery of scientific models: Guiding search with knowledge and data


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 review the history of research on computational systems that discover scientific knowledge. The general framework 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. Next I turn to 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 illustrate the operation of implemented systems on data sets from ecology and environmental science, showing they produce accurate and interpretable models. I also report an improved framework that, by adopting a few simplifying assumptions, reliably produces more accurate fits and scales far better to complex models, along with recent work on adapting models to altered settings. In closing, I discuss challenges for research on scientific discovery and their role in the e-science movement, which uses computational methods to understand and support the scientific enterprise.


(This talk describes joint work with Kevin Arrigo, Adam Arvay, Stuart Borrett, Will Bridewell, Ljupco Todorovski, and others. Papers are available at


The seminar is organized in collaboration with the Herbert Simon Society.


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 actively to artificial intelligence and cognitive science for over 35 years, he was founding executive editor of Machine Learning, and he is currently editor for Advances in Cognitive Systems. His ongoing research focuses on induction of explanatory scientific models and on architectures for intelligent agents.