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

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Part of the Lecture Notes in Computer Science book series (LNISA,volume 8021)

Abstract

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.

Keywords

  • Mental Model
  • Search Task
  • Search Region
  • World Trade Center
  • Shared Belief

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Goodrich, M.A., Yi, D. (2013). Toward Task-Based Mental Models of Human-Robot Teaming: A Bayesian Approach. In: Shumaker, R. (eds) Virtual Augmented and Mixed Reality. Designing and Developing Augmented and Virtual Environments. VAMR 2013. Lecture Notes in Computer Science, vol 8021. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39405-8_30

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  • DOI: https://doi.org/10.1007/978-3-642-39405-8_30

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39404-1

  • Online ISBN: 978-3-642-39405-8

  • eBook Packages: Computer ScienceComputer Science (R0)