Toward Task-Based Mental Models of Human-Robot Teaming: A Bayesian Approach

• Published in: Virtual Augmented and Mixed Reality. Designing and Developing Augmented and Virtual Environments pp. 267 - 276
• Main Authors: Goodrich, Michael A., Yi, Daqing
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2013
• Series: Lecture Notes in Computer Science
• Subjects: Mental Model; Search Region; Search Task; Shared Belief; World Trade Center
• ISBN: 9783642394041; 3642394043
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-642-39405-8_30

We consider a set of team-based information tasks, meaning that the team’s goals are to choose behaviors that provide or enhance information available to the team. These information tasks occur across a region of space and must be performed for a period of time. We present a Bayesian model for (a) how information flows in the world and (b) how information is altered in the world by the location and perceptions of both humans and robots. Building from this model, we specify the requirements for a robot’s computational mental model of the task and the human teammate, including the need to understand where and how the human processes information in the world. The robot can use this mental model to select its behaviors to support the team objective, subject to a set of mission constraints.