Crowdsourcing Insights into Problem Structure for Scientific Discovery

Abstract.  In recent years, there has been tremendous progress in solving large-scale reasoning and optimization problems. Central to this progress has been the ability to automatically uncover hidden problem structure. Nevertheless, for the very hardest computational tasks, human ingenuity still appears indispensable. We show that automated reasoning strategies and human insights can effectively complement each other, leading to hybrid human-computer solution strategies that outperform other methods by orders of magnitude. We illustrate our approach with challenges in scientific discovery in the areas of finite mathematics and materials science.

Bio.  Bart Selman is a Professor of Computer Science at Cornell University. His research interests include computational sustainability, efficient inference procedures, planning, knowledge representation, and connections between computer science and statistical physics. He has (co-)authored over 150 publications, including six best paper awards. His papers have appeared in venues spanning Nature, Science, Proc. Natl. Acad. of Sci., and a variety of conferences and journals in AI and Computer Science. He has received the Cornell Stephen Miles Excellence in Teaching Award, the Cornell Outstanding Educator Award, an NSF Career Award, and an Alfred P. Sloan Research Fellowship. He is a Fellow of the American Association for Artificial Intelligence, a Fellow of the American Association for the Advancement of Science, and a Fellow of the ACM.