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Counterexample Generation for Probabilistic Model Checking Micro-Scale Cyber-Physical Systems.

Yang Liu1, Yan Ma2, Yongsheng Yang1

  • 1Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China.

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|September 28, 2021
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Summary
This summary is machine-generated.

This study introduces a heuristic Genetic Algorithm to find counterexamples for Micro-scale Cyber-Physical Systems (MCPSs) using probabilistic model checking. This approach aids in debugging and synthesizing MCPSs by efficiently generating diagnostic subgraphs.

Keywords:
counterexamplegenetic algorithmmicro-scale cyber-physical systemsprobabilistic model checking

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Area of Science:

  • Computer Science
  • Control Systems Engineering
  • Formal Methods

Background:

  • Micro-scale Cyber-Physical Systems (MCPSs) require formal verification for reliability.
  • Probabilistic model checking using Markov Decision Processes (MDPs) and Probabilistic Computation Tree Logic (PCTL) is a key technique.
  • Identifying counterexamples is crucial for debugging but finding the smallest one is computationally challenging (NPC problem).

Purpose of the Study:

  • To propose a novel heuristic Genetic Algorithm for generating counterexamples in probabilistic model checking of MCPSs.
  • To address the computational complexity of finding minimal counterexamples.
  • To provide a practical method for debugging and synthesizing MCPSs.

Main Methods:

  • Development of a heuristic Genetic Algorithm tailored for MDP counterexample generation.
  • Utilizing indirect path coding to expand the search space.
  • Employing a heuristic crossover operator for more effective diagnostic path generation.
  • Constructing diagnostic subgraphs from MDP diagnostic paths represented as an AND/OR tree.

Main Results:

  • The heuristic Genetic Algorithm effectively generates counterexamples for MCPS models.
  • The approach demonstrates feasibility and efficiency in case studies, including dynamic power management and communication protocols.
  • A prototype tool integrated with the PAT probabilistic model checker was developed.

Conclusions:

  • The proposed heuristic Genetic Algorithm offers a viable solution for generating counterexamples in probabilistic model checking of MCPSs.
  • This method aids in the debugging, control, and synthesis of complex systems.
  • The tool and methodology show promise for enhancing the reliability and design of MCPSs.