RankPL: A Qualitative Probabilistic Programming Language
Tjitze Rienstra

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.
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Taxonomy
TopicsLogic, Reasoning, and Knowledge · Bayesian Modeling and Causal Inference · Semantic Web and Ontologies
