  How can one detect friendly and adversarial behavior from raw data? Detecting
whether an environment is a friend, a foe, or anything in between, remains a
poorly understood yet desirable ability for safe and robust agents. This paper
proposes a definition of these environmental "attitudes" based on an
characterization of the environment's ability to react to the agent's private
strategy. We define an objective function for a one-shot game that allows
deriving the environment's probability distribution under friendly and
adversarial assumptions alongside the agent's optimal strategy. Furthermore, we
present an algorithm to compute these equilibrium strategies, and show
experimentally that both friendly and adversarial environments possess
non-trivial optimal strategies.
