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Description:
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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 . |