The Design and Implementation of IBAL: A General-Purpose Probabilistic Language

Abstract

This paper describes IBAL, a high level representation language for probabilistic AI. IBAL integrates several aspects of probability-based rational behavior, including probabilistic reasoning, Bayesian parameter estimation and decision theoretic utility maximization. IBAL is based on the functional programming paradigm, and is an ideal rapid prototyping language for probabilistic modeling. The paper presents the IBAL language, and presents a number of examples in the language. It then discusses the semantics of IBAL, presenting the semantics in two different ways. Finally, the inference algorithm of IBAL is presented. Seven desiderata are listed for inference, and it is shown how the algorithm fulfills each of them.