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If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
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Establishing Human Observer Criterion in Evaluating Artificial Social Intelligence Agents in a Search and Rescue

Lixiao Huang1, Jared Freeman2, Nancy J Cooke1

  • 1Center for Human, Artificial Intelligence, and Robot Teaming, Arizona State University.

Topics in Cognitive Science
|April 13, 2023
PubMed
Summary

Artificial social intelligence (ASI) agents can infer user knowledge and predict actions in complex tasks. These AI agents outperformed human observers in a simulated urban search and rescue environment.

Keywords:
Artificial social intelligenceBaselineEvaluationHuman observer criterionMinecraftSearch and rescueTheory of mind

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Area of Science:

  • Artificial Intelligence
  • Human-Computer Interaction
  • Cognitive Science

Background:

  • Artificial social intelligence (ASI) agents show promise for enhancing individual and team performance.
  • Developing effective ASI agents requires evaluating their ability to understand human partners in complex environments.

Purpose of the Study:

  • To create and utilize a Minecraft-based urban search and rescue (USAR) environment to assess ASI agents.
  • To evaluate ASI agents' capabilities in inferring human participants' knowledge states and predicting their actions within the USAR task.

Main Methods:

  • ASI agents and human observers inferred participant knowledge and predicted actions using different data sources (event messages vs. video).
  • Evaluations included comparisons against ground truth, among different ASI agents, and against a human observer benchmark.
  • The study used a simulated USAR task in Minecraft to provide a controlled yet complex environment for evaluation.

Main Results:

  • ASI agents demonstrated superior performance compared to human observers in inferring knowledge training conditions.
  • ASI agents also outperformed human observers in predicting the next victim type to be rescued.
  • The study established a benchmark for ASI agent performance in a team-based task environment.

Conclusions:

  • ASI agents show significant potential for assisting human-AI and human-human teams in complex operational settings.
  • The developed evaluation methodology provides a framework for designing and assessing future ASI agents.
  • Further refinement of evaluation criteria is essential for advancing ASI agent capabilities in diverse team compositions and task environments.