Detecting suspicious input in intelligent systems using answer set programming
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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.