Here is the schedule [PDF].
Some of the hardest problems in artificial intelligence, such as feature and concept learning, are solved seemingly effortlessly by people. These are problems of inductive inference, which are difficult because there are many solutions that are consistent with the information explicitly given with the problem (e.g., solving ab=2 for the value of a without being given any additional information). People solve problems of inductive inference by favoring solutions that are consistent with their prior knowledge and penalizing solutions that are inconsistent with prior beliefs. Bayesian inference provides a formal calculus for how people should update their prior belief in each solution in light of their observations. Prior beliefs are formulated as a probability distribution over the unobserved solutions. This methodology has provided a successful paradigm for exploring formal solutions to how people solve inductive problems.
Using Bayesian inference to formally represent human solutions to inductive problems not only provides a computational explanation of human behavior, but also offers novel methods for solving difficult problems in artificial intelligence. In this workshop, we present recent computational successes in human learning as a source of new artificial intelligence algorithms by exploiting the common computational language of these two communities, probability theory. This workshop is a forum for researchers in artificial intelligence, machine learning, and human learning, all interested in the same inductive problems, to discuss computational methodologies, insights, and research questions. We hope to foster a dialogue that leads to a greater understanding of human learning and further unites these two areas of research.