Automated Robot Behavior Recognition

Automated recognition of the behavior of robots is increasingly needed in a variety of tasks, as we develop more autonomous robots and general information processing agents. For example, in environments with multiple autonomous robots, a robot may need to make decisions based on the behavior of the other robots. As another interesting example, an intelligent narrator agent observing a robot will need to automatically identify the robot’s behaviors. In this paper, we introduce a novel framework for using Hidden Markov Models (HMMs) to represent and recognize strategic behaviors of robotic agents. We first introduce and characterize the perceived signal in terms of behavioral-relevant state features. We then show how several HMMs capture different defined robot behaviors. Finally we present the HMMbased recognition algorithm which orchestrates and selects the appropriate HMMs in real time. We use the multi-robot robotic soccer domain as the substrate of our empirical validation, both in simulation and using real robots. Robots will then adapt their behaviors as a function of the autonomously recognized behavior of the other agents, either teammates or opponents.