Credibility Discounting in the Theory of Approximate Reasoning
This work addresses the need for more robust reasoning systems in AI by handling uncertain or untrustworthy information, though it appears incremental as it builds on existing theories of approximate and nonmonotonic reasoning.
The paper tackles the problem of incorporating credibility information into reasoning systems to discount unreliable agent-provided data, proposing a representational scheme for credibility qualification in approximate reasoning and introducing the concept of relative credibility based on compatibility with higher-priority evidence.
We are concerned with the problem of introducing credibility type information into reasoning systems. The concept of credibility allows us to discount information provided by agents. An important characteristic of this kind of procedure is that a complete lack of credibility rather than resulting in the negation of the information provided results in the nullification of the information provided. We suggest a representational scheme for credibility qualification in the theory of approximate reasoning. We discuss the concept of relative credibility. By this idea we mean to indicate situations in which the credibility of a piece of evidence is determined by its compatibility with higher priority evidence. This situation leads to structures very much in the spirit of nonmonotonic reasoning.