A Higher-Order Characterization of Probabilistic Polynomial Time

  • Ugo Dal Lago
  • Paolo Parisen Toldin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7177)


We present RSLR, an implicit higher-order characterization of the class PP of those problems which can be decided in probabilistic polynomial time with error probability smaller than \(\frac{1}{2}\). Analogously, a (less implicit) characterization of the class BPP can be obtained. RSLR is an extension of Hofmann’s SLR with a probabilistic primitive, which enjoys basic properties such as subject reduction and confluence. Polynomial time soundness of RSLR is obtained by syntactical means, as opposed to the standard literature on SLR-derived systems, which use semantics in an essential way.


Normal Form Polynomial Time Error Probability Inference Rule Turing Machine 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ugo Dal Lago
    • 1
  • Paolo Parisen Toldin
    • 1
  1. 1.Dipartimento di Scienze dell’InformazioneUniversità di Bologna, Équipe FOCUS, INRIA Sophia AntipolisBolognaItaly

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