  Modelling other agents' behaviors plays an important role in decision models
for interactions among multiple agents. To optimise its own decisions, a
subject agent needs to model what other agents act simultaneously in an
uncertain environment. However, modelling insufficiency occurs when the agents
are competitive and the subject agent can not get full knowledge about other
agents. Even when the agents are collaborative, they may not share their true
behaviors due to their privacy concerns. In this article, we investigate into
diversifying behaviors of other agents in the subject agent's decision model
prior to their interactions. Starting with prior knowledge about other agents'
behaviors, we use a linear reduction technique to extract representative
behavioral features from the known behaviors. We subsequently generate their
new behaviors by expanding the features and propose two diversity measurements
to select top-K behaviors. We demonstrate the performance of the new techniques
in two well-studied problem domains. This research will contribute to
intelligent systems dealing with unknown unknowns in an open artificial
intelligence world.
