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README.md

Table of Contents

About verifai

We present verifai, a software toolkit for the formal design and analysis of systems that include artificial intelligence (AI) and machine learning (ML) components. verifai particularly seeks to address challenges with applying formal methods to perception and ML components, including those based on neural networks, and to model and analyze system behavior in the presence of environment uncertainty. verifai centers on simulation guided by formal models and specifications. Several use cases are illustrated with examples, including temporal-logic falsification, model-based systematic fuzz testing, parameter synthesis, counterexample analysis, and data set augmentation.

Here is the associated paper Verifai:A Toolkit for the Design and Analysis of Artificial Intelligence-Based Systems.

If you have any problems using VerifAI, please contact Shromona Ghosh at shromona.ghosh@berkeley.edu or Tommaso Dreossi at dreossi@berkeley.edu or submit an issue to the GitHub repository.

Installation

$ pip install .

or as a developer:

$ pip install -e .

You need to ensure that your your pip version is >= 18.1.

Verifai requires at least Python 3.6. We recommend using 3.6 since TensorFlow does not currently support Python 3.7.

Some features of verifai require additional packages (the tool will prompt you if they are needed but not installed):

  • Bayesian Optimization sampler: GPy and GPyOpt (for Python 3.7, see note below);
  • Examples using neural networks: tensorflow, opencv and PIL;
  • Examples using OpenAI Gym: gym and baselines;
  • Examples using Scenic: Scenic

Note that the package for GPy on PyPI currently does not work with Python 3.7. If necessary, you can build it from source as follows:

$ git clone https://github.com/SheffieldML/GPy
$ find Gpy -name '*.pyx' -exec cython {} \;
$ pip install Gpy/

Case Studies

Lane keeping with inbuilt simulator

verifai comes with an inbuilt simulator developed from this car simulator. In this example we have a car (in red) whose task is to stay within its lane using an LQR controller.

Task: Falsify the LQR lane keeping controller

Sample space: Initial x-position, angle of rotation, and cruising speed.

Relevant files:

  1. verifai/simulators/car_simulator/examples/lanekeeping_LQR/lanekeeping_falsifier.py : Defines the sample space and type of falsifier (sampler and number of iterations)
  2. verifai/simulators/car_simulator/examples/lanekeeping_LQR/lanekeeping_simulation.py : Defines the controller and the simulation environment

Running the falsifier: To run this example open two terminal shells and go to cd verifai/simulators/car_simulator in each of them. Then in first one run python examples/lanekeeping_LQR/lanekeeping_falsifier.py and wait till you see "Initialized sampler" in the terminal; then run python examples/lanekeeping_LQR/lanekeeping_simulation.py in the other one.

The falsifier runs for 20 iterations, you can change this by modifying MAX_ITERS in examples/lanekeeping_LQR/lanekeeping_falsifier.py. At the end of the runs, you should see "End of all simulations" in the terminal where you ran python examples/lanekeeping_LQR/lanekeeping_simulation.py.

Expected Output: During the running of the falsifier you should the samples and the associated value of the specification satisfaction (rho). Rho represents the quantitative satisfaction of the specification such that the sample satisfies the specification if the rho is positive or falsifies the specification if the rho is negative.

You should see two tables in the first terminal where you ran python examples/lanekeeping_LQR/lanekeeping_falsifier.py, labeled Falsified Samples a collection of all falsified samples and Safe Samples a collection of all the samples which were safe.

Data augmentation

In this example we try to falsify a Neural Network (NN) trained to detect images of cars on roads. We re-create the data augmentation example from this paper. We implemented our own picture renderer which generates images by sampling from a low dimensional modification (sample) space.

Task: Falsify the NN trained on the synthetic images generated by the picture rendered

Sample space: Image background (37 backgrounds), number of cars- (x, y) position and type of car, overall image brightness, color, contrast, and sharpness.

Relevant files:

  1. examples/data_augmenatation/falsifier.py : Defines the sample space and type of falsifier (sampler and number of iterations)
  2. examples/data_augmentation/classifier.py : Interface to the picture renderer and instantiate the NN

To run this example, you need to install tensorflow. We suggest installing version 1.8 since we trained our network using this version. $ pip install tensorflow=1.8 You also need to install openCV $pip install opencv_python and PIL $pip install pillow

Running the falsifier: Open two terminal shells and go to cd data_augmentation in each of them. Then in first one run python falsifier.py and wait till you see "Initialized sampler" in the terminal; then run python classifier.py in other one.

The falsifier runs for 20 iterations, you can change this by modifying MAX_ITERS in examples/data_augmenatation/falsifier.py. At the end of the runs, you should see "End of all classifier calls" in the terminal where you ran python classifier.py.

The falsifying samples are stored in the data structure error_table. We can further analyse the error_table to generate images for retraining the NN. We have introduced three techniques to generate new images for the NN re-training:

  1. Randomly sample samples from the error_table
  2. Top k closest (similar) sampler from the error_table
  3. Use the PCA analysis on the samples to generate new samples

Expected Output: During the running of the falsifier you should the samples and the associated value of the specification satisfaction (rho). Here rho represents the qualitative (boolean) satisfaction. Its True if the NN correctly classifies the image.

You should see a table labeled Error Table a collection of all falsified samples in the first terminal where you ran python falsifier.py. This follows two counterexample sets, one randomly generated from the error table and the other with k closest samples. We set k = 4 in this example. This is followed by the PCA analysis results, we report the pivot and the 2 principle components.

The images in the two counterexample sets will pop up at the end of the run. The images are saved in the counterexample_images folder. The images with the prefix "random_" are from the random samples counterexample set and those with the prefix "kclosest_" are from the k closest counterexample set.

OpenAI Gym examples

To run these examples, you need to install OpenAI gym, $ pip install gym, and openAI baselines,

$ git clone https://github.com/openai/baselines.git $ cd baselines $ pip install -e .

Cartpole

In this example we want to test the robustness of a controllers to changes in model parameters and initial states of the cartpole from openAI gym.

cartpole env

Cartpole Environment from OpenAI Gym: Model parameters - length and mass of pole; and mass of cart

We use OpenAI baselines to train a NN to control the cartpole . We train a NN using Proximal Policy Optimization algorithms (PPO) with 100000 training episodes.

We use the reward function as the specification for testing; i.e., if the reward is positive for all the environments the controller is safe. For the cartpole, the specification is: the maximum variation of the pole from the center should be less than 12 degree and maximum variation of the initial position should be less than 2.4m. To test the controller we loosen the training thresholds for angle, by 0.01 radians, and the x variation, by 0.1m, to get the testing thresholds. To capture this we define a specification using metric temporal logic python library which verifai can convert into a monitor internally.

Task: Test the robustness of the NN controller trained using PPO

Sample space: Initial state x position and rotation of the cartpole, model parameters - mass and length of pole and mass of the cart

Relevant files:

  1. examples/openai_gym/cartpole/cartpole_falsifier.py : Defines the sample space and type of falsifier (sampler and number of iterations)
  2. examples/openai_gym/cartpole/cartpole_simulation.py : Interface to OpenAI gym and baselines

Running the falsifier: During the running of the falsifier you should the samples and the associated value of the specification satisfaction (rho). Rho is the quantitative satisfaction.

Open two terminal shells and go to cd examples/openai_gym/cartpole in each of them. Then in first one run python cartpole_falsifier.py and wait till you see "Initialized sampler" in the terminal; then run python cartpole_simulation.py in other one.

The falsifier runs for 20 iterations, you can change this by modifying MAX_ITERS in examples/openai_gym/cartpole/cartpole_falsifier.py. At the end of the runs, you should see "End of all cartpole simulations" in the terminal where you ran python cartpole_simulation.py.

Expected Output: During the running of the falsifier you should the samples and the associated value of the specification satisfaction (rho). Rho is the quantitative satisfaction.

You should see the error table Counter-example samples containing all falsified samples in the terminal where you ran python cartpole_falsifier.py

Mountaincar

In this example we show the effect of hyper-parameters to train NN to control the mountaincar environment from openAI gym.

mountaincar env

MountainCar Environment from OpenAI Gym: Goal - reach the flag

Specifically, we see the effects of different training algorithms and size of the training set on synthesizing a NN controller.

We treat the reward function provided by the environment as our specification. For the mountaincar, this is characterized by the distance to the flag. Here, unlike the previous examples, we would like to find the set of parameters which which ensures the NN is able to correctly control the mountaincar. To do this, we negate the specification so that falsifying the specification implies finding a safe controller.

Task: Study the effect of hyperparameters in training NN controllers for mountaincar

Sample space: Training algorithms - Deep RL algorithms, number of training episodes

Relevant files:

  1. examples/openai_gym/mountaincar/mountaincar_falsifier.py : Defines the sample space and type of falsifier (sampler and number of iterations)
  2. examples/openai_gym/mountaincar/mountaincar_simulation.py : Interface to OpenAI gym and baselines

Running the falsifier: During the running of the falsifier you should the samples and the associated value of the specification satisfaction (rho). Rho is the quantitative satisfaction.

Open two terminal shells and go to cd examples/openai_gym/mountaincar in each of them. Then in the first one run python mountaincar_falsifier.py and wait till you see "Initialized sampler" in the terminal; then run python mountaincar_simulation.py in the other one.

The falsifier runs for 20 iterations, you can change this by modifying MAX_ITERS in examples/openai_gym/mountaincar/mountaincar_falsifier.py. At the end of the runs, you should see "End of all mountaincar simulations" in the terminal where you ran python mountaincar_simulation.py.

Expected Output: You should see a table Hyper-parameters leading to good controllers containing all sampled hyperparamaters which build NN which can safely control the mountaincar in the terminal where you ran python mountaincar_falsifier.py.

Webots examples

To run these examples you need to download and install Webots 2018 from here. Webots 2018 is not a free software, however you can get a 30 free trial. While choosing license please select PRO license. When you open Webots the first time go to Webots->Preferences changes Startup Mode to Pause.

We do not recommend Webots 2019 (even though it is a free software) since we found its performance very slow in machines without GPUs (personal laptops). Further Webots 2019 needs python 3.7 which does not yet support tensorflow and we could only recreate a subset of our examples (those without NN) for it.

Scene Generation using Scenic

In this example we use the probabilistic-programming language Scenic to generate scenes where a road is obstructed by a broken car behind traffic cones. The scene we would like to generate is made up of an ego car (in red) and a broken car (in silver) parked behind three traffic cones.

Task: Generate scenes using Scenic

Sample space: Position of the ego car in the city, position and orientation of the cones, position and orientation of the broken car

Relevant files:

  1. examples/webots/controllers/scenic_cones_supervisor/lane_cones.sc : Scenic code to generate scenes
  2. examples/webots/controllers/scenic_cones_supervisor/scenic_cones_sampler.py : Interface to scenic
  3. examples/webots/controllers/scenic_cones_supervisor/scenic_cones_supervisor.py : Interface to webots
  4. examples/webots/worlds/shattuck_build.wbt : Webots world of downtown Berkeley

Running the sampler: Launch Webots and load the world examples/webots/worlds/shattuck_build.wbt (File->Open World). Open a terminal and go to examples/webots/controllers/scenic_cones_supervisor and run python scenic_cones_sampler.py. Once that starts running (you will notice a message "Initialized Sampler" in the terminal), you can start the simulation.

The sampler runs for 20 iterations, you can change this by modifying MAX_ITERS in examples/webots/controllers/scenic_cones_supervisor/scenic_cones_sampler.py. At the end of the runs, you should see "End of scene generation" in the Webots console and the Webots window closes.

Expected Output: During the running of the sampler you should the samples and the associated value of the specification satisfaction (rho). Since in this application we focus only on scene generation, rho does not have any practical relevance (and has been set to infinity).

You should see the collection of all samples generated by the scenic script in the terminal where you ran python scenic_cones_sampler.py.

Falsification of accident scene with cones

In this example we choose a scene generated from the above case study and run our falsifier to find small variations in the initial scene variation which lead to the ego car (in red) to crash into the traffic cones. For this purpose, we fix the location of the broken car, cones, and the ego_car but introduce variations in the color of the broken, and noise in the position and orientation of the ego car, cones and the broken car.

The ego car' controller is responsible for safely maneuvering around the cones. To achieve this, we implemented a hybrid controller. Initially, the car tries to remain in its lane. The controller in the ego car relies on a NN we trained to detect the cones and estimate the distance to the cones. When the distance to the cone is less than 15 meters the car starts a lane changing maneuver. This can be seen as the NN warning a human in the car to change lanes. To capture the human behavior, we introduce a reaction time (which is also a parameter) of the human.

Task: Falsify the NN detecting cones and the hybrid controller

Sample space: Initial position and orientation noise of the ego car, cruising speed of the ego car, position and orientation noise in the broken car, position error and orientation of the cones, color of the broken car, reaction time of the human

Relevant files:

  1. examples/webots/controllers/cones_lanechange_supervisor/cones_lanechange_falsifier.py : Defines the sample space and type of falsifier (sampler and number of iterations)
  2. examples/webots/controllers/cones_lanechange_supervisor/cones_lanechange_supervisor.py : Interface to webots
  3. examples/webots/worlds/shattuck_buildings_falsif.wbt : Webots world of downtown Berkeley

To run this example, you need to install tensorflow. We suggest installing version 1.8 since we trained our network using this version. $ pip install tensorflow=1.8 You also need to install openCV $pip install opencv_python and PIL $pip install pillow

Running the falsifier: During the running of the falsifier you should the samples and the associated value of the specification satisfaction (rho). Rho is the quantitative satisfaction.

Launch Webots and load the world examples/webots/worlds/shattuck_buildings_falsif.wbt (File->Open World). Open a terminal and go to examples/webots/controllers/cones_lanechange_supervisor and run python cones_lanechange_falsifier.py. Once that starts running (you will notice a message "Initialized Sampler" in the terminal), you can start the simulation.

The falsifier runs for 20 iterations, you can change this by modifying MAX_ITERS in examples/webots/controllers/cones_lanechange_supervisor/cones_lanechange_falsifier.py. At the end of the runs, you should see "End of simulations" in the Webots console and the Webots window closes.

Each simulation run takes about 16 seconds in the simulation time; but may take more time in real time because of overheads like the neural networks calls and rendering.

Expected Output: You should see a table Unsafe Samples the collection of all samples generated that caused the ego car to crash into the cones in the terminal where you ran python cones_lanechange_falsifier.py.

Fuzz testing using Scenic

In this example we use Scenic to initial conditions to recreate collision scenarios at an intersection. In this example, the ego car (in green) if going straight through an intersection. The front view of the ego car is obstructed by a set of cars (in silver) which are stopped at the intersection. A human car (in red) attempts to the take a left turn at the intersection. In this scenario, the camera of both the ego car and the human car are obstructed by the silver cars standing at the intersection. We use scenic to sample the initial positions and orientations of the ego car, human car and couple of the standing cars at the intersection.

To reduce the chances of collision, we designed a controller for the ego car which utilizes the information coming from a "smart intersection". The smart intersection sends a warning to the ego car, when the human car approaches the intersection. Based on how early the warning comes in, we reduce the number of collision scenarios.

Task: Generate accident scenarios and test controllers with "smart intersection"

Sample space: Position and orientation of the ego car, human car and standing cars

Relevant files:

  1. examples/webots/controllers/scenic_intersection_supervisor/intersection_crash.sc : Scenic code to generate scenes
  2. examples/webots/controllers/scenic_intersection_supervisor/scenic_intersection_sampler.py : Interface to scenic
  3. examples/webots/controllers/scenic_intersection_supervisor/scenic_intersection_supervisor.py : Interface to webots
  4. examples/webots/worlds/scenic_intersection.wbt : Webots world of the intersection

To run this example, you need to install pyproject, $pip install pyproj

Running the sampler: Launch Webots and load the world examples/webots/worlds/scenic_intersection.wbt (File->Open World) . Open a terminal and go to examples/webots/controllers/scenic_intersection_supervisor and run python scenic_intersection_sampler.py. Once that starts running (you will notice a message "Initialized Sampler" in the terminal), you can start the simulation.

To test the original scenario without the smart intersection, ensure that ignore_smart_intersection = True on line 24 in the file examples/webots/controllers/smart_brake/smart_brake.py. To test with smart intersection set ignore_smart_intersection = False. You can update how early the warning is sent by updating the value of intersection_buffer in line 111 in scenic_intersection_supervisor.py. If set to 0, then the warning is given only when the human car enters the intersection. If > 0, the warning is sent when the car is some distance away from the intersection. If < 0, the warning is sent when the car is some distance inside the intersection.

The sampler runs for 20 iterations, you can change this by modifying MAX_ITERS in examples/webots/controllers/scenic_intersection_supervisor/scenic_intersection_sampler.py. At the end of the runs, you should see "End of accident scenario generation" in the Webots console and the Webots window closes.

Each simulation run takes about 16 seconds in the simulation time; but may take more time in real time because of overheads like rendering and communication between controllers.

Expected Output: During the running of the sampler you should the samples and the associated value of the specification satisfaction (rho). Since in this application we focus only on scenario generation, rho does not have any practical relevance (and has been set to infinity).

You should see the collection of all samples generated by the scenic script in the terminal where you ran python scenic_intersection_sampler.py.

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