title: Modeling cognitive loads for evolving shared mental models in human-agent collaboration.
abstract: Recent research on human-centered teamwork highly demands the design of cognitive agents that can model and exploit human partners' cognitive load to enhance team performance. In this paper, we focus on teams composed of human-agent pairs and develop a system called Shared Mental Models for all--SMMall. SMMall implements a hidden Markov model (HMM)-based cognitive load model for an agent to predict its human partner's instantaneous cognitive load status. It also implements a user interface (UI) concept called shared belief map, which offers a synergic representation of team members' information space and allows them to share beliefs. An experiment was conducted to evaluate the HMM-based load models. The results indicate that the HMM-based load models are effective in helping team members develop a shared mental model (SMM), and the benefit of load-based information sharing becomes more significant as communication capacity increases. It also suggests that multiparty communication plays an important role in forming/evolving team SMMs, and when a group of agents can be partitioned into subteams, splitting messages by their load status can be more effective for developing subteam SMMs.
authors.affiliation: Pennsylvania State University, University Park, PA 16802, USA. xfan@psu.edu
authors.forename: Xiaocong
authors.initials: X
authors.lastname: Fan
authors.forename: John
authors.initials: J
authors.lastname: Yen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003071:Cognition / Q000502:physiology*; D003198:Computer Simulation; D003661:Decision Support Techniques; D006801:Humans; D008328:Man-Machine Systems; D008954:Models, Biological; D008962:Models, Theoretical