  Current methods for authentication and key agreement based on public-key
cryptography are vulnerable to quantum computing. We propose a novel approach
based on artificial intelligence research in which communicating parties are
viewed as autonomous agents which interact repeatedly using their private
decision models. Authentication and key agreement are decided based on the
agents' observed behaviors during the interaction. The security of this
approach rests upon the difficulty of modeling the decisions of interacting
agents from limited observations, a problem which we conjecture is also hard
for quantum computing. We release PyAMI, a prototype authentication and key
agreement system based on the proposed method. We empirically validate our
method for authenticating legitimate users while detecting different types of
adversarial attacks. Finally, we show how reinforcement learning techniques can
be used to train server models which effectively probe a client's decisions to
achieve more sample-efficient authentication.
