Human-agent collaboration for time-stressed multicontext decision making

Xiaocong Fan, Michael McNeese, Bingjun Sun, Timothy Hanratty, Laurel Allender, John Yen

Research output: Contribution to journalArticle

20 Scopus citations

Abstract

Multicontext team decision making under time stress is an extremely challenging issue faced by various real-world application domains. In this paper, we employ an experience-based cognitive agent architecture (called R-CAST) to address the informational challenges associated with military command and control (C2) decision-making teams, the performance of which can be significantly affected by dynamic context switching and tasking complexities. Using context switching frequency and task complexity as two factors, we conducted an experiment to evaluate whether the use of R-CAST agents as teammates and decision aids can benefit C2 decision-making teams. Members from a U.S. Army Reserve Officer Training Corps organization were randomly recruited as human participants. They were grouped into ten humanhuman teams, each composed of two participants, and ten humanagent teams, each composed of one participant and two R-CAST agents, as teammates and decision aids. The statistical inference of experimental results indicates that R-CAST agents can significantly improve the performance of C2 teams in multicontext decision making under varying time-stressed situations.

Original languageEnglish (US)
Article number5345826
Pages (from-to)306-320
Number of pages15
JournalIEEE Transactions on Systems, Man, and Cybernetics Part A:Systems and Humans
Volume40
Issue number2
DOIs
StatePublished - Mar 1 2010

All Science Journal Classification (ASJC) codes

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'Human-agent collaboration for time-stressed multicontext decision making'. Together they form a unique fingerprint.

  • Cite this