Detecting suspicious input in intelligent systems using answer set programming

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Title: Detecting suspicious input in intelligent systems using answer set programming
Author: Gianoutsos, Nicholas
Abstract: When presented with bad information people tend to make bad decisions . Even a rational person is unable to consistently make good decisions when presented with unsound information . The same holds true for intelligent agents . If at any point an agent accepts bad information into his reasoning process , the soundness of his decision making ability will begin to corrode . The purpose of this work is to develop programming methods that give intelligent systems the ability to handle potentially false information in a reasonable manner . In this research , we propose methods for detecting unsound information , which we call outliers , and methods for detecting the sources of these outliers . An outlier is informally defined as an observation or any combination of observations that are outside the realm of plausibility of a given state of the environment . With such reasoning ability , an intelligent agent is capable of not only learning about his environment , but he is also capable of learning about the reliability of the sources reporting the information . Throughout this work we introduce programming methods that enable intelligent agents to detect outliers in input information , as well as , learn about the accuracy of the sources submitting information .
URI: http : / /hdl .handle .net /2346 /1193
Date: 2005-05

Citation

Detecting suspicious input in intelligent systems using answer set programming. Master's thesis, Texas Tech University. Available electronically from http : / /hdl .handle .net /2346 /1193 .

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