"Bayesian models of human learning and reasoning"   Young Stars Program

03/03/06: 10:30 AM, webcast
11th Floor Large Conference Room
Host: Patrick Pantel and Jafar Adibi, schedule

Abstract: In the last decade, Bayesian methods have revolutionized major areas of artificial intelligence, machine learning, and natural language processing. In contrast, Bayesian methods have not yet achieved nearly the same success among cognitive scientists trying to explain how humans learn, reason and communicate. In this talk I will sketch some of the challenges and prospects for Bayesian models in cognitive science, and also draw some lessons for advancing the state of the art in probabilistic approaches to artificial intelligence. I will focus on everyday reasoning tasks where people can routinely draw successful generalizations from very limited evidence. These generalizations can be modeled as Bayesian inferences constrained by people's intuitive theories about the causal structure of the world. I will present several case studies drawn from task domains such as diagnostic reasoning, predicting the duration of events, inferring the properties of biological species, and learning physical laws. Time permitting, I will also talk about some recent work on how people might learn their abstract theories about the structure of these domains, and some applications of our models to problems in machine learning such as semi-supervised classification and relational clustering. (Joint work with Tom Griffiths, Charles Kemp, Tevye Krynski, and Sourabh Niyogi.)

Last updated: Mon Jun 19 17:44:06 2006