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Metric Reasoning About \(\lambda \)-Terms: The General Case

  • Raphaëlle Crubillé
  • Ugo Dal Lago
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10201)

Abstract

In any setting in which observable properties have a quantitative flavor, it is natural to compare computational objects by way of metrics rather than equivalences or partial orders. This holds, in particular, for probabilistic higher-order programs. A natural notion of comparison, then, becomes context distance, the metric analogue of Morris’ context equivalence. In this paper, we analyze the main properties of the context distance in fully-fledged probabilistic \(\lambda \)-calculi, this way going beyond the state of the art, in which only affine calculi were considered. We first of all study to which extent the context distance trivializes, giving a sufficient condition for trivialization. We then characterize context distance by way of a coinductively-defined, tuple-based notion of distance in one of those calculi, called \(\varLambda ^\oplus _!\). We finally derive pseudometrics for call-by-name and call-by-value probabilistic \(\lambda \)-calculi, and prove them fully-abstract.

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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  1. 1.IRIFUniversité Denis Diderot - Paris 7ParisFrance
  2. 2.Università di BolognaBolognaItaly
  3. 3.InriaSophia AntipolisFrance

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