Planning based on Dynamic Bayesian Network algorithm using dynamic programming and variable elimination

According to the development of robot technology, human-robot interaction (HRI) is the field of study highlighted. The study aims to find the goal of human action considering their intention and behavior based on their respective habits. To gain the principle of behavior on the goal by understanding that of human, engineers draw the inference of the result needed from planning through HRI. In this paper, plan inference for aimed goal is modeled by calculating with probability what task system performs through the observed behavior. Dynamic Bayesian network (DBN) uses the probabilistic inference to reveal the relation of data varying according to time. Machine repository pioneer data of UCI has proved that accuracy and efficiency of inference is higher than the existing DBN by lowering useless calculation applying the variable elimination method and the concept of dynamic programming for DBN algorithm.