  Protecting against adversarial attacks is a common multiagent problem.
Attackers in the real world are predominantly human actors, and the protection
methods often incorporate opponent models to improve the performance when
facing humans. Previous results show that modeling human behavior can
significantly improve the performance of the algorithms. However, modeling
humans correctly is a complex problem, and the models are often simplified and
assume humans make mistakes according to some distribution or train parameters
for the whole population from which they sample. In this work, we use data
gathered by psychologists who identified personality types that increase the
likelihood of performing malicious acts. However, in the previous work, the
tests on a handmade game could not show strategic differences between the
models. We created a novel model that links its parameters to psychological
traits. We optimized over parametrized games and created games in which the
differences are profound. Our work can help with automatic game generation when
we need a game in which some models will behave differently and to identify
situations in which the models do not align.
