Design of an optimal automation system: Finding a balance between a human's task engagement and exhaustion

Michel Klein*, Rianne van Lambalgen

*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

In demanding tasks, human performance can seriously degrade as a consequence of increased workload and limited resources. In such tasks it is very important to maintain an optimal performance quality, therefore automation assistance is required. On the other hand, automation can also impose additional demands on a person monitoring the system and it will likely reduce the person's awareness of the situation. Therefore, automation should be adapted to the human's state in order to keep the human in the loop. In this paper an agent model is proposed that calculates a person's workload and engagement to an automated task. Based on this agent model, an intelligent support system can provide different types of automation and aims at adapting to the human state such that an optimal balance is found between exhaustion and task engagement.

Original languageEnglish
Title of host publicationModern Approaches in Applied Intelligence - 24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2011, Proceedings
Pages98-108
Number of pages11
Volume6704 LNAI
EditionPART 2
DOIs
Publication statusPublished - 2011
Event24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2011 - Syracuse, NY, United States
Duration: 28 Jun 20111 Jul 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume6704 LNAI
ISSN (Print)03029743
ISSN (Electronic)16113349

Conference

Conference24th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2011
Country/TerritoryUnited States
CitySyracuse, NY
Period28/06/111/07/11

Keywords

  • Adaptive Automation
  • Agents
  • Cognitive Modeling

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