Lying on the Web: Implications For Expert Systems Redesign

Abstract

We consider a new variety of sequential information gathering problems that are applicable for web based applications in which data provided as input may be distorted by the system user, such as an applicant for a credit card application. We propose two methods to compensate for input distortion. The first method, termed knowledge base modification, considers redesigning the knowledge base of an expert system to best account for distortion in the input provided by the user. The second method, termed input modification, modifies the input directly to account for distortion, and uses the modified input in the existing (unmodified) knowledge base of the system. These methods are compared with an approach where input noise is ignored. Experimental results indicate that both types of modification substantially improve the accuracy of recommendations, with knowledge base modification outperforming input modification in most cases. Knowledge base modification is, however, more computationally intensive than input modification. Therefore, when computational resources are adequate, the knowledge base modification approach is preferred; when such resources are very limited, input modification may be the only viable alternative.

Keywords: Sequential Information Gathering; Expert Systems; Input Distortion; Noise Handling