Taking It to the Limit: Approximate Reasoning for Markov Processes

  • Kim Guldstrand Larsen
  • Radu Mardare
  • Prakash Panangaden
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7464)

Abstract

We develop a fusion of logical and metrical principles for reasoning about Markov processes. More precisely, we lift metrics from processes to sets of processes satisfying a formula and explore how the satisfaction relation behaves as sequences of processes and sequences of formulas approach limits. A key new concept is dynamically-continuous metric bisimulation which is a property of (pseudo)metrics. We prove theorems about satisfaction in the limit, robustness theorems as well as giving a topological characterization of various classes of formulas. This work is aimed at providing approximate reasoning principles for Markov processes.

Keywords

Markov Process Convergent Sequence Logical Formula Approximate Reasoning Markov Kernel 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Kim Guldstrand Larsen
    • 1
  • Radu Mardare
    • 1
  • Prakash Panangaden
    • 2
  1. 1.Aalborg UniversityDenmark
  2. 2.McGill UniversityCanada

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