Abstract: Noah Goodman

Title: Probabilistic Programs: A New Language for AI

How can logical and probabilistic approaches to understanding
intelligence be reconciled? I will argue that probabilistic
programming is the best way to merge logic and probability, providing
a new set of tools for thinking about representation and inference in
systems with human-like intelligence. I will illustrate these ideas by
introducing the probabilistic programming language Church (a
stochastic LISP), describing two universal inference algorithms (i.e.
algorithms that can perform probabilistic inference for any Church
program), and giving a series of examples. These examples, drawn from
cognitive science and AI, will include multi-agent reasoning and
concept learning.