Multi-Objective Falsification

Specification of Objectives

VerifAI now provides the ability to run falsification on multiple metrics at the same time. For example, consider the following VerifAI monitor:

from verifai.monitor import multi_objective_monitor

Example of multi-objective specification. This monitor specifies that the ego vehicle
must stay at least 5 meters away from each other vehicle in the scenario.
class distance_multi(multi_objective_monitor):
    def __init__(self, num_objectives=1):
        priority_graph = nx.DiGraph()
        self.num_objectives = num_objectives
        priority_graph.add_edge(0, 2)
        priority_graph.add_edge(1, 3)
        priority_graph.add_edge(2, 4)
        priority_graph.add_edge(3, 4)
        print(f'Initialized priority graph with {self.num_objectives} objectives')
        def specification(simulation):
            positions = np.array(simulation.result.trajectory)
            distances = positions[:, [0], :] - positions[:, 1:, :]
            distances = np.linalg.norm(distances, axis=2)
            rho = np.min(distances, axis=0) - 5
            return rho

        super().__init__(specification, priority_graph)

The monitor computes the distance between the ego vehicle and every other vehicle in the scenario and returns all of these distances. Note that the monitor class extends the multi_objective_monitor class, written specifically for vector-valued objectives. Additionally, a rulebook is defined in the priority_graph variable, which is a partial ordering over the metrics providing some pairwise information about which metrics are considered most important. This rulebook is encoded as a directed acyclic graph (DAG) using the NetworkX library.

Samplers Supporting Multiple Objectives

To mitigate issues with sensitivity to results of initial samples, VerifAI implements the multi-armed bandit sampler, an active sampler which uses the Upper Confidence Bound (UCB) algorithm to tradeoff exploration of new regions of the feature space as well as exploitation of previously found counterexamples. To use the multi-armed bandit sampler, either use the MultiArmedBanditSampler class or, if using Scenic, add the line

param verifaiSamplerType = 'mab'

For an example of using multi-objective sampling, see the examples/multi_objective folder.