  There are many situations in which it would be beneficial for a robot to have
predictive abilities similar to those of rational humans. Some of these
situations include collaborative robots, robots in adversarial situations, and
for dynamic obstacle avoidance. This paper presents an approach to modeling
behaviors of dynamic agents in order to empower robots with the ability to
predict the agent's actions and identify the behavior the agent is executing in
real time. The method of behavior modeling implemented uses hidden Markov
models (HMMs) to model the unobservable states of the dynamic agents. The
background and theory of the behavior modeling is presented. Experimental
results of realistic simulations of a robot predicting the behaviors and
actions of a dynamic agent in a static environment are presented.
