  As we discussed in Part I of this topic, there is a clear desire to model and
comprehend human behavior. Given the popular presupposition of human reasoning
as the standard for learning and decision-making, there have been significant
efforts and a growing trend in research to replicate these innate human
abilities in artificial systems. In Part I, we discussed learning methods which
generate a model of behavior from exploration of the system and feedback based
on the exhibited behavior as well as topics relating to the use of or
accounting for beliefs with respect to applicable skills or mental states of
others. In this work, we will continue the discussion from the perspective of
methods which focus on the assumed cognitive abilities, limitations, and biases
demonstrated in human reasoning. We will arrange these topics as follows (i)
methods such as cognitive architectures, cognitive heuristics, and related
which demonstrate assumptions of limitations on cognitive resources and how
that impacts decisions and (ii) methods which generate and utilize
representations of bias or uncertainty to model human decision-making or the
future outcomes of decisions.
