  There is a clear desire to model and comprehend human behavior. Trends in
research covering this topic show a clear assumption that many view human
reasoning as the presupposed standard in artificial reasoning. As such, topics
such as game theory, theory of mind, machine learning, etc. all integrate
concepts which are assumed components of human reasoning. These serve as
techniques to attempt to both replicate and understand the behaviors of humans.
In addition, next generation autonomous and adaptive systems will largely
include AI agents and humans working together as teams. To make this possible,
autonomous agents will require the ability to embed practical models of human
behavior, which allow them not only to replicate human models as a technique to
"learn", but to to understand the actions of users and anticipate their
behavior, so as to truly operate in symbiosis with them. The main objective of
this paper it to provide a succinct yet systematic review of the most important
approaches in two areas dealing with quantitative models of human behaviors.
Specifically, we focus on (i) techniques which learn a model or policy of
behavior through exploration and feedback, such as Reinforcement Learning, and
(ii) directly model mechanisms of human reasoning, such as beliefs and bias,
without going necessarily learning via trial-and-error.
