RankPL: A Qualitative Probabilistic Programming Language
This work addresses the problem of representing and reasoning about qualitative uncertainty in computational processes, offering a novel approach for researchers in AI and probabilistic modeling.
The authors introduced RankPL, a qualitative probabilistic programming language based on Spohn's ranking theory, designed to model uncertainty by distinguishing normal from surprising events, and they provided its semantics, examples, and an implementation.
In this paper we introduce RankPL, a modeling language that can be thought of as a qualitative variant of a probabilistic programming language with a semantics based on Spohn's ranking theory. Broadly speaking, RankPL can be used to represent and reason about processes that exhibit uncertainty expressible by distinguishing "normal" from" surprising" events. RankPL allows (iterated) revision of rankings over alternative program states and supports various types of reasoning, including abduction and causal inference. We present the language, its denotational semantics, and a number of practical examples. We also discuss an implementation of RankPL that is available for download.