A Bayesian Based Machine Learning Application to Task Analysis

A Bayesian Based Machine Learning Application to Task Analysis

Shu-Chiang Lin (Purdue University, USA)
Copyright: © 2009 |Pages: 7
DOI: 10.4018/978-1-60566-010-3.ch022
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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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Numerous computer-based task analysis techniques have been developed over the years (Gael, 1988; Kirwan and Ainsworth, 1992; Wickens and Hollands, 2000; Hollnagel, 2003; Stephanidis and Jacko, 2003; Diaper and Stanton, 2004; Wilson and Corlett, 2005; Salvendy, 2006; Lehto and Buck, 2008). These approaches are similar in many ways to methods of knowledge acquisition commonly used during the development of expert systems (Vicente, 1999; Schraagen et al., 2000; Elm et al., 2003; Shadbolt and Burton, 2005). Several taxonomies exist to classify knowledge elicitation approaches. For example, Lehto et al. (1992) organize knowledge elicitation methods (including 140 computer-based tools), identified in an extensive review of 478 articles, into three categories: manual methods, interactive or semi-automated methods, and automated or machine learning methods. Manual methods such as protocol analysis or knowledge organization are especially useful as an initial approach because they can be used to effectively retrieve structure and formalize knowledge components, resulting in a knowledge base that is accurate and complete (Fujihara, et al., 1997). Studies such as Trafton et al. (2000) have shown this technique can capture the essence of qualitative mental models used in complex visualization and other tasks. The drawbacks of this technique are similar to those of classic task analysis techniques in that they involve extensive manual effort and may interfere with the expert’s ability to perform the task. Semi-automated methods generally utilize computer programs to simplify applications of the manual methods of knowledge acquisition. The neural network model is one of the methods in common use today, especially when learning and recognition of patterns are essential (Bhagat, 2005). A neural network can self-update its processes to provide better estimates and results with further training. However, one arguable disadvantage is that this approach may require considerable computational power should the problem be somewhat complex (Dewdney, 1997).

Automated methods or machine learning based methods primarily focus on learning from recorded data rather than through direct acquisition of knowledge from human experts. Many variations of commonly used machine learning algorithms can be found in the literature. In general, the latter approach learns from examples-guided deductive/inductive processes to infer rules applicable to other similar situations (Shalin, et al., 1988; Jagielska et al., 1999; Wong & Wang, 2003; Alpaydın, 2004; Huang et al., 2006; Bishop, 2007).

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