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Ph.D. Theses

Intelligent Agent Development Using Unstructured Text Corpora and Multiple Choice Questions

By Joseph Johnson
Advisor: Selmer Bringsjord
July 21, 2016

This thesis explores various approaches for developing an intelligent agent in a particular domain: fraud detection. The framework by which we measure our agent is psychometric artificial intelligence, or, more commonly, psychometric AI, which focuses on the development of agents that can successfully pass tests. We set our sights on one particular test in the fraud-detection domain - the Certified Fraud Examiners (CFE) exam, administered by the Association of Fraud Examiners (ACFE), the governing body overseeing the fraud examiners profession. Version 1 of the agent focuses on shallow text processing techniques that leverage features of the exam and the high-level structure of the Fraud Examiners Manual (FEM) document. Version 2 employs information retrieval-based approaches wherein the agent breaks up the FEM into more granular documents using the FEM's table of contents and text features. Version 3 incorporates machine learning to target the precise paragraphs within the FEM relevant to each question. Finally, version 4 features an agent with a deep, semantic representation of a subdomain of fraud detection, doctor shopping, and whose knowledge-base consists of assertions expressed in the deontic cognitive event calculus, DCEC*.

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