Task Boundary Inference via Topic Modeling to Predict Interruption Timings for Human-Machine Teaming

  • Nia S. PetersEmail author
  • George C. Bradley
  • Tina Marshall-Bradley
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 903)


Human-machine teaming aims to meld human cognitive strengths with the unique capabilities of smart machines. An issue within human-machine teaming is a lack of communication skills on the part of the machine such as the inability to know when to interrupt human teammates. A proposed solution to this issue is an intelligent interruption system that monitors the spoken communication of human teammates and predicts appropriate times to interrupt without disrupting the teaming interaction. The current research expands on a prosody-only task boundary model as an intelligent interruption system with a topic-only task boundary model. The topic-only task boundary model outperforms the prosody-only model with a 9.5% increase in the F1 score, but is limited in its ability to process topical data in real-time, a previous benefit of the prosody-only task boundary model.


Human machine teaming Intelligent interruption systems Topic modeling Collaborative communication 


  1. 1.
    Arroyo, E., Selker, T.: Attention and Intention Goals Can Mediate Disruption in Human-Computer Interaction, pp. 454–470 (2011)CrossRefGoogle Scholar
  2. 2.
    Peters, N.: Interruption timing prediction via prosodic task boundary model for human-machine teaming. In: Proceedings of the 2019 Future Information and Communication Conference (2019)Google Scholar
  3. 3.
    Adamczyk, P.D., Bailey, B.P.: If Not Now, When?: The Effects of Interruption at Different Moments Within Task Execution, vol. 6, no. 1, pp. 271–278 (2004)Google Scholar
  4. 4.
    Bailey, B.P., Konstan, J.A.: On the need for attention-aware systems: measuring effects of interruption on task performance, error rate, and affective state. Comput. Hum. Behav. 22(4), 685–708 (2006)CrossRefGoogle Scholar
  5. 5.
    Czerwinski, M., Cutrell, E., Horvitz, E.: Instant messaging and interruption: influence of task type on performance. In: OZCHI Conference Proceedings, SRC, vol. 356, pp. 361–367 (2000)Google Scholar
  6. 6.
    Iqbal, S.T., Bailey, B.P.: Leveraging characteristics of task structure to predict the cost of interruption. In: Proceedings of the SIGCHI Conference on Human Factors Computer Systems - CHI ’06, p. 741 (2006)Google Scholar
  7. 7.
    Iqbal, S.T., Bailey, B.P.: Investigating the Effectiveness of Mental Workload as a Predictor of Opportune Moments for InterruptionGoogle Scholar
  8. 8.
    Blei, D., Ng, A., Jordan, M., Bohus, D., Horvitz, E.: Latent dirichlet allocation. J. Mach. Learn. Res. pp nd Learn. to Predict Engagem. with a Spok. Dialog Syst. openworld settings Proc. SIGDIAL, pp. 993–1022 SRC-GoogleScholar FG-0 (2009)Google Scholar
  9. 9.
    Druck, G., Mann, G., McCallum, A.: Learning from labeled features using generalized expectation criteria. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR ’08, no. 1, p. 595 (2008)Google Scholar
  10. 10.
    McCallum, A.K.: MALLET: Learning for Language Toolkit (2002)Google Scholar
  11. 11.
    Banerjee, S.: Random Forest Classifier (2016)Google Scholar

Copyright information

© This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2019

Authors and Affiliations

  • Nia S. Peters
    • 1
  • George C. Bradley
    • 2
  • Tina Marshall-Bradley
    • 2
  1. 1.Air Force Research Laboratory711th Human Performance Wing, Battlespace Acoustic BranchWright-Patterson AFBUSA
  2. 2.Walden UniversityMinneapolisUSA

Personalised recommendations