Using Markov Decision Process to Model Deception for Robotic and Interactive Game Applications
This work addresses deception in robotics and interactive games, offering an incremental improvement in adaptive strategy selection.
The paper tackles the problem of generating deceptive trajectories for a mobile robot to deceive humans over multiple interactions, proposing an adaptive algorithm that outperforms random strategy selection in a user study.
This paper investigates deception in the context of motion using a simulated mobile robot. We analyze some previously designed deceptive strategies on a mobile robot simulator. We then present a novel approach to adaptively choose target-oriented deceptive trajectories to deceive humans for multiple interactions. Additionally, we propose a new metric to evaluate deception on data collected from the users when interacting with the mobile robot simulator. We performed a user study to test our proposed adaptive deceptive algorithm, which shows that our algorithm deceives humans even for multiple interactions and it is more effective than random choice of deceptive strategies.