AIM Distinguished Lecture Series: Leslie Kaelbling

The classical approach to AI was to design systems that were rational at run-time: they had explicit representations of beliefs, goals, and plans and ran inference algorithms, online, to select actions. The rational approach was criticized (by the behaviorists) and modified (by the probabilists) but persisted in some form. Now the overwhelming success of the connectionist approach in so many areas presents evidence that the rational view may no longer have a role to play in AI. I will examine this question from several perspectives, including whether the rationality is present at design-time and/or at run-time, and whether systems with run-time rationality might be useful from the perspectives of computational efficiency, cognitive modeling and safety. I will present some current research focused on understanding the roles of learning in runtime-rational systems with the ultimate aim of constructing general-purpose human-level intelligent robots.

Leslie is a Professor at MIT. She has an undergraduate degree in Philosophy and a PhD in Computer Science from Stanford, and was previously on the faculty at Brown University. She was the founding editor-in-chief of the Journal of Machine Learning Research. Her research agenda is to make intelligent robots using methods including estimation, learning, planning, and reasoning.