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This repository contains the code and experiments contained in the paper "Analyzing Learning-Based Networked Systems with Formal Verification" by Arnaud Dethise, Marco Canini and Nina Narodytska, published in INFOCOM 2021.

Requirements

To run the experiments, the following is required:

  • Python 3.7.9 (other versions were not tested)
  • Tensorflow 2
  • IBM ILOG CPLEX with Python bindings
  • Install pypolyhedron and matplotlib (required for graphical output)

Repository content

  • experiments.ipynb: Contains the code used to encode properties and run experiments.
  • building_blocks.ipynb: Contains the MILP implementation of the primitives presented in the paper.
  • pensieve.py: Contains the MILP encoding of the Pensieve agent.
  • layers.py: Contains generic implementation for Fully-Connected Linear and ReLU layers.
  • ilp/, utils/ and ilp_utils*.py contain support code with a high-level interface to interface with Cplex.
  • model/ contains a trained model of Pensieve which is used to extract the model parameters.