The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
A Bayesian Based Machine Learning Application to Task Analysis
Abstract
Many task analysis techniques and methods have been developed over the past decades, but identifying and decomposing a user’s task into small task components remains a difficult, impractically time-consuming, and expensive process that involves extensive manual effort (Sheridan, 1997; Liu, 1997; Gramopadhye and Thaker, 1999; Annett and Stanton, 2000; Bridger, 2003; Stammers and Shephard, 2005; Hollnagel, 2006; Luczak et al., 2006; Morgeson et al., 2006). A practical need exists for developing automated task analysis techniques to help practitioners perform task analysis efficiently and effectively (Lin, 2007). This chapter summarizes a Bayesian methodology for task analysis tool to help identify and predict the agents’ subtasks from the call center’s naturalistic decision making’s environment.
Related Content
|
Muhammad Naeem, Salman Memon, Anita Larik, Syed Rizwan Mehdi, Hasan Ahmed Faridi, Khalida Khan, Sana Zafar, Manoj Kumar.
© 2026.
20 pages.
|
|
Imdad Ali Shah, N. Z. Jhanjhi.
© 2026.
12 pages.
|
|
Hafsa Muzammal, Muhammad Zaman, Muhammad Safdar, Muhammad Adnan Shahid, Zuhaib Nishtar, Muhammad Bilal, Muntaha Munir, Mehar Muhammad Haseeb, Aamir Raza, Syed Intsar Hussain Shah, Usman Zafar, Nalain E. Muhammad, Hafiz Muhammad Bilawal Akram.
© 2026.
30 pages.
|
|
Luminita Diaconu, Yassine Mouniane.
© 2026.
32 pages.
|
|
Kumar J. Parmar, Tejas Chandulal Chauhan, T. Premavathi.
© 2026.
32 pages.
|
|
Mahmoud Oudghiri, Mohamed El Bakkali, Yassine Mouniane, Nagla Abid, Samah Bouhassoun, Fatima-ezzahra Jaayefar, Fath Alah Elwahab, Issam El-Khadir, Ahmed Chriqui, Mohammed Ibriz.
© 2026.
26 pages.
|
|
Issam El-Khadir, Yassine Mouniane, Ahmed Chriqui, Mohamed El Bakkali, Driss Hmouni.
© 2026.
34 pages.
|
|
|