In a rational programming language, a program specifies a situation faced by an agent; evaluating the program amounts to computing what a rational agent would believe or do in the situation. This paper presents IBAL, a rational programming language for probabilistic and decision-theoretic agents. IBAL provides a rich declarative language for describing probabilistic models. The expression language allows the description of arbitrarily complex generative models. In addition, IBAL's observation language makes it possible to express and compose rejective models that result from conditioning on the observations. IBAL also integrates Bayesian parameter estimation and decision-theoretic utility maximization thoroughly into the framework. All these are packaged together into a programming language that has a rich type system and built-in extensibility. This paper presents a detailed account of the syntax and semantics of IBAL, as well as an overview of the implementation.
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