Visible to the public Predicting trust in human control of swarms via inverse reinforcement learning

TitlePredicting trust in human control of swarms via inverse reinforcement learning
Publication TypeConference Paper
Year of Publication2017
AuthorsNam, C., Walker, P., Lewis, M., Sycara, K.
Conference Name2017 26th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN)
KeywordsComputational modeling, decision theory, existing trust models, Human Behavior, human trust, human-in-the-loop systems, inverse reinforcement learning algorithm, learning (artificial intelligence), Learning systems, Markov Decision Process, Markov processes, multi-robot systems, pubcrawl, Robot kinematics, Robot sensing systems, robotic swarm, search mission, telerobotics, Training, trust level
AbstractIn this paper, we study the model of human trust where an operator controls a robotic swarm remotely for a search mission. Existing trust models in human-in-the-loop systems are based on task performance of robots. However, we find that humans tend to make their decisions based on physical characteristics of the swarm rather than its performance since task performance of swarms is not clearly perceivable by humans. We formulate trust as a Markov decision process whose state space includes physical parameters of the swarm. We employ an inverse reinforcement learning algorithm to learn behaviors of the operator from a single demonstration. The learned behaviors are used to predict the trust level of the operator based on the features of the swarm.
Citation Keynam_predicting_2017