Haziness for Common Sensical Inference from Uncertain and Inconsistent Linear Knowledge Base
Lionel Daniel · 2008
We theoretically address the problem of reasoning common sensically in uncertain and inconsistent linear knowledge bases.Those bases linearly combine degrees of belief about sentences of a propositional logic, where degrees of belief are assumed to be probabilities. A knowledge base is inconsistent iff no probability function satisfies it. We propose a new process that consistently infers information from such bases. Contrary to ordinary inference processes, ours tackles inconsistencies by trusting every single item of knowledge, where trust can be an application-specific parameter. Moreover, our inference process behaves common sensically when applied to a consistent knowledge base, since it coincides with the maximum entropy inference process. Besides, we provide new measures of inconsistency and similarity that deal with possibly inconsistent knowledge bases. Injecting a bit of common sense into decision systems should make them more easily trustworthy.