A Bayesian Based Machine Learning Application to Task Analysis

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Author(s): Shu-Chiang Lin (Purdue University, USA)
Copyright: 2009
Pages: 7
Source title: Encyclopedia of Data Warehousing and Mining, Second Edition
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-60566-010-3.ch022

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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.

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