  In safe opponent exploitation players hope to exploit their opponents'
potentially sub-optimal strategies while guaranteeing at least the value of the
game in expectation for themselves. Safe opponent exploitation algorithms have
been successfully applied to small instances of two-player zero-sum imperfect
information games, where Nash equilibrium strategies are typically known in
advance. Current methods available to compute these strategies are however not
scalable to desirable large domains of imperfect information such as No-Limit
Texas Hold 'em (NLHE) poker, where successful agents rely on game abstractions
in order to compute an equilibrium strategy approximation. This paper will
extend the concept of safe opponent exploitation by introducing prime-safe
opponent exploitation, in which we redefine the value of the game of a player
to be the worst-case payoff their strategy could be susceptible to. This allows
weaker epsilon equilibrium strategies to benefit from utilising a form of
opponent exploitation with our revised value of the game, still allowing for a
practical game-theoretical guaranteed lower-bound. We demonstrate the empirical
advantages of our generalisation when applied to the main safe opponent
exploitation algorithms.
