RankPL: A Qualitative Probabilistic Programming Language

TL;DR

RankPL is a new qualitative probabilistic programming language based on ranking theory, enabling reasoning about normal and surprising events, with support for revision, abduction, and causal inference.

Contribution

It introduces RankPL, a novel language combining qualitative probabilistic reasoning with practical semantics and implementation, expanding the tools for modeling uncertain processes.

Findings

Supports reasoning about normal vs. surprising events

Allows iterative revision of rankings over program states

Enables abduction and causal inference in qualitative models

Abstract

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.