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- W2952811074 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." @default.
- W2952811074 created "2019-06-27" @default.
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- W2952811074 date "2017-05-19" @default.
- W2952811074 modified "2023-09-27" @default.
- W2952811074 title "RankPL: A Qualitative Probabilistic Programming Language" @default.
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