Nonparametric Bayesian Logic
This work addresses data association and population estimation challenges in AI, representing an incremental extension of existing Bayesian logic frameworks.
The paper tackled the problem of modeling worlds with unknown numbers of objects in AI by extending the Bayesian Logic (BLOG) language with nonparametric generative processes, resulting in a method that uses Dirichlet processes to handle varying object counts and applied it to citation matching.
The Bayesian Logic (BLOG) language was recently developed for defining first-order probability models over worlds with unknown numbers of objects. It handles important problems in AI, including data association and population estimation. This paper extends BLOG by adopting generative processes over function spaces - known as nonparametrics in the Bayesian literature. We introduce syntax for reasoning about arbitrary collections of objects, and their properties, in an intuitive manner. By exploiting exchangeability, distributions over unknown objects and their attributes are cast as Dirichlet processes, which resolve difficulties in model selection and inference caused by varying numbers of objects. We demonstrate these concepts with application to citation matching.