Human interactive machine learning for trust in teams of autonomous robots

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

Abstract

Unmanned systems are increasing in number, while their manning requirements remain the same. To decrease manpower demands, machine learning techniques and autonomy are gaining traction and visibility. One barrier is human perception and understanding of autonomy. Machine learning techniques can result in 'black box' algorithms that may yield high fitness, but poor comprehension by operators. However, Interactive Machine Learning (IML), a method to incorporate human input over the course of algorithm development by using neuro-evolutionary machine-learning techniques, may offer a solution. IML is evaluated here for its impact on developing autonomous team behaviors in an area search task. Initial findings show that IML-generated search plans were chosen over plans generated using a non-interactive ML technique, even though the participants trusted them slightly less. Further, participants discriminated each of the two types of plans from each other with a high degree of accuracy, suggesting the IML approach imparts behavioral characteristics into algorithms, making them more recognizable. Together the results lay the foundation for exploring how to team humans successfully with ML behavior.

Original languageEnglish (US)
Title of host publication2017 IEEE Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781509063802
DOIs
StatePublished - May 16 2017
Externally publishedYes
Event2017 IEEE Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2017 - Savannah, United States
Duration: Mar 27 2017Mar 31 2017

Publication series

Name2017 IEEE Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2017

Conference

Conference2017 IEEE Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2017
CountryUnited States
CitySavannah
Period3/27/173/31/17

Keywords

  • Human automation interaction
  • machine learning
  • robots
  • supervisory control
  • unmanned vehicles

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction
  • Decision Sciences (miscellaneous)
  • Experimental and Cognitive Psychology

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