Automatic Testing With Reusable Adversarial Agents

Understand

Autonomous systems such as self-driving cars and general-purpose robots are safety-critical systems that operate in highly uncertain and dynamic environments.

• We propose an interactive multi-agent framework where the system-under-design is modeled as an ego agent and its environment is modeled by a number of adversarial (ado) agents.
• For example, a self-driving car is an ego agent whose behavior is influenced by ado agents such as pedestrians, bicyclists, traffic lights, road geometry etc.
• Given a logical specification of the correct behavior of the ego agent, and a set of constraints that encode reasonable adversarial behavior, our framework reduces the adversarial testing problem to the problem of synthesizing controllers for (constrained) ado agents that cause the ego agent to violate its specifications.