Stochastic Differential Dynamic Logic for Stochastic Hybrid Programs

Abstract

Logic is a powerful tool for analyzing and verifying systems, including programs, discrete systems real-time systems, hybrid systems, and distributed systems. Some applications also have a stochastic behavior, however, either because of fundamental properties of nature, uncertain environments or simplifications to overcome complexity. Discrete probabilistic systems have been studied using logic. But logic has been chronically underdeveloped in the context of stochastic hybrid systems, i.e., systems with interacting discrete, continuous, and stochastic dynamics. We aim at overcoming this deficiency and introduce a dynamic logic for stochastic hybrid systems. Our results indicate that logic is a promising tool for understanding stochastic hybrid systems and can help taming some of their complexity. We introduce a compositional model for stochastic hybrid systems. We prove adaptivity, c`adl`ag, and Markov time properties, and prove that the semantics of our logic is measurable. We present compositional proof rules, including rules for stochastic differential equations, and prove soundness.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Apr 01, 2011
Accession Number
ADA543485

Entities

People

  • AndrĂ© Platzer

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Brownian Motion
  • Computational Science
  • Computer Science
  • Computers
  • Differential Equations
  • Dynamics
  • Equations
  • Hybrid Systems
  • Language
  • Markov Chains
  • Markov Processes
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Random Variables
  • Semantics
  • Stochastic Processes

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Applied Combinatorial Optimization and Logic Circuit Design.
  • Distributed Systems and Data Platform Development