An implementation of statistical default logic

In Jose Alferes & Joao Leite (eds.), Logics in Artificial Intelligence (JELIA 2004). Springer (2004)
Statistical Default Logic (SDL) is an expansion of classical (i.e., Reiter) default logic that allows us to model common inference patterns found in standard inferential statistics, e.g., hypothesis testing and the estimation of a population‘s mean, variance and proportions. This paper presents an embedding of an important subset of SDL theories, called literal statistical default theories, into stable model semantics. The embedding is designed to compute the signature set of literals that uniquely distinguishes each extension on a statistical default theory at a pre-assigned error-bound probability.
Keywords Statistical Default Logic  Literal statistical default theories  Non-monotonic reasoning
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