An AI Framework for the Automatic Assessment of e-Government Forms

This article describes the architecture and AI technology behind an XML-based AI framework designed to streamline e-government form processing. The framework performs several crucial assessment and decision support functions, including workflow case assignment, automatic assessment, follow-up action generation, precedent case retrieval, and learning of current practices. To implement these services, several AI techniques were used, including rule-based processing, schema-based reasoning, AI clustering, case-based reasoning, data mining, and machine learning. The primary objective of using AI for e-government form processing is of course to provide faster and higher quality service as well as ensure that all forms are processed fairly and accurately. With AI, all relevant laws and regulations as well as current practices are guaranteed to be considered and followed. An AI framework has been used to implement an AI module for one of the busiest immigration agencies in the world.

[1]  E. H. Turner,et al.  A Schema-based Approach To Cooperative Problem Solving With Autonomous Underwater Vehicles , 1991, OCEANS 91 Proceedings.

[2]  John R. Smith,et al.  IBM Research TRECVID-2009 Video Retrieval System , 2009, TRECVID.

[3]  Dianne P. O'Leary,et al.  QCS: A system for querying, clustering and summarizing documents , 2007, Inf. Process. Manag..

[4]  Ronald M. Lee,et al.  Schematic evaluation of internal accounting control systems , 1992 .

[5]  Said Tabet,et al.  Using XML as a Language Interface for AI Applications , 2000, PRICAI Workshops.

[6]  John P. McDermott,et al.  OPS, A Domain-Independent Production System Language , 1977, IJCAI.

[7]  Chris Gokey,et al.  SAIRE—a scalable agent-based information retrieval engine , 1997, AGENTS '97.

[8]  Roy M. Turner Adaptive Reasoning for Real-world Problems: A Schema-based Approach , 1994 .

[9]  D. Cousins Bureau of Customs and Border Protection , 2003 .

[10]  Hans-Peter Kriegel,et al.  Incremental Clustering for Mining in a Data Warehousing Environment , 1998, VLDB.

[11]  Josef Pieprzyk,et al.  Case-based reasoning for intrusion detection , 1996, Proceedings 12th Annual Computer Security Applications Conference.

[12]  Steven J. Fenves,et al.  Applying AI clustering to engineering tasks , 1993, IEEE Expert.

[13]  TAE-WAN RYU,et al.  SIMILARITY MEASURES FOR MULTI-VALUED ATTRIBUTES FOR DATABASE CLUSTERING , 1998 .

[14]  Rosina O. Weber,et al.  PlayMaker: An Application of Case-Based Reasoning to Air Traffic Control Plays , 2004, ECCBR.

[15]  J. Ross Quinlan,et al.  Generating Production Rules from Decision Trees , 1987, IJCAI.

[16]  Bruce G. Buchanan,et al.  The MYCIN Experiments of the Stanford Heuristic Programming Project , 1985 .

[17]  Paul E. Utgoff,et al.  Incremental Induction of Decision Trees , 1989, Machine Learning.