Title :
A machine-learning apprentice for the completion of repetitive forms
Author :
Hermens, Leonard A. ; Shlimmer, J.C.
Author_Institution :
Sch. of Electr. Eng. & Comput. Sci., Washington State Univ., Pullman, WA, USA
Abstract :
The authors have developed a software environment in which workers can complete repetitive forms, and a machine-learning and prediction system that works within it. The nonintrusive assistant or apprentice provides viable default values for blank fields in a form, saving users up to 87 percent in keystroke effort and correctly predicting nearly 90 percent of the form´s values. The system and prediction methods are active, yet not intrusive. Default predictions are always displayed, yet the user can override them easily with normal editing commands.<>
Keywords :
business forms; commerce; knowledge based systems; learning (artificial intelligence); blank fields; default values; editing commands; keystroke effort; machine-learning apprentice; machine-learning software environment; nonintrusive assistant; prediction system; repetitive forms; Computer errors; Computer networks; Government; Image recognition; Image segmentation; Machine learning; Microcomputers; NASA; Routing; Workstations;
Journal_Title :
IEEE Expert
