Michael Akintunde · AAMAS
Formal Verification of Neural Agents in Non-deterministic Environments
We introduce a model for agent-environment systems where the agents are implemented via feed-forward ReLU neural networks and the environment is non-deterministic. We study the verification problem of such systems against CTL properties. We introduce a bounded fragment of CTL and show its usefulness in identifying shallow bugs in the system. We present a novel parallel algorithm for MILP-based verification of agent-environment systems, present an implementation, and report the experimental results obtained against a variant of the VerticalCAS use-case.