RankPL: A Qualitative Probabilistic Programming Language

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10369)

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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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Computer Science and CommunicationUniversity of LuxembourgLuxembourg CityLuxembourg

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