Title :
Knowledge acquisition for an Internal Revenue Service classification system
Author :
Whipple, Cynthia ; Davis, Lawrence ; Kam, John ; Needham, Jm
Abstract :
The authors introduce the Automated Issue Identification System (AIIS), an expert system prototype designed and built for the US Internal Revenue Service (IRS). They describe the classification task as currently performed by IRS auditors, and the expected benefits of automating this task with an expert system. The multilayer objectives of the knowledge engineering team are specifically detailed. Knowledge engineering techniques and procedures used in the initial phases of the project are examined, including the advantages and challenges of working with several experts. The main phases of the knowledge engineering process are described, from the initial task analysis, through early modeling of the IRS experts by the system, to the very effective rapid prototyping cycle supported by the object-oriented programming environment used in this project. The authors concludes with the current status of the prototype and the knowledge acquisition process, and a discussion of projected goals
Keywords :
expert systems; financial data processing; government data processing; knowledge acquisition; Automated Issue Identification System; IRS auditors; Internal Revenue Service; early modeling; expert system prototype; initial task analysis; knowledge acquisition; knowledge engineering; multilayer objectives; object-oriented programming environment; rapid prototyping cycle; Artificial intelligence; Automation; Expert systems; Fasteners; Fatigue; Guidelines; Knowledge acquisition; Prototypes; Time factors;
Conference_Titel :
AI Systems in Government Conference, 1989.,Proceedings of the Annual
Conference_Location :
Washington, DC
Print_ISBN :
0-8186-1934-1
DOI :
10.1109/AISIG.1989.47336