Abstract
Engineering of complex intelligent systems often requires experts to decompose the task into smaller constituent processes. This allows the domain experts to identify and solve specific sub-tasks, which collectively solve the system’s goals. The engineering of individual processes and their relationships represent a knowledge acquisition challenge, which is complicated by incremental ad-hoc revisions that are inevitable in light of evolving data and expertise. Incremental revisions introduce a risk of degrading the system and limit experts’ ability to build complex intelligent systems. We present an incremental engineering method called ProcessNet that structures incremental ad-hoc changes to a system and mitigates the risks of the changes degrading the system. A medical image analysis application developed using ProcessNet demonstrates that despite a large number of ad-hoc, incremental changes the system’s ability and accuracy in segmenting multiple anatomical regions in High Resolution Computed Tomography (HRCT) scans continue to improve.
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Misra, A., Sowmya, A., Compton, P. (2010). Incremental System Engineering Using Process Networks. In: Kang, BH., Richards, D. (eds) Knowledge Management and Acquisition for Smart Systems and Services. PKAW 2010. Lecture Notes in Computer Science(), vol 6232. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15037-1_14
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DOI: https://doi.org/10.1007/978-3-642-15037-1_14
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