Probabilistic Semantics and Defaults
  There is much interest in providing probabilistic semantics for defaults but
most approaches seem to suffer from one of two problems: either they require
numbers, a problem defaults were intended to avoid, or they generate peculiar
side effects. Rather than provide semantics for defaults, we address the
problem defaults were intended to solve: that of reasoning under uncertainty
where numeric probability distributions are not available. We describe a
non-numeric formalism called an inference graph based on standard probability
theory, conditional independence and sentences of favouring where a favours b -
favours(a, b) - p(a|b) > p(a). The formalism seems to handle the examples from
the nonmonotonic literature. Most importantly, the sentences of our system can
be verified by performing an appropriate experiment in the semantic domain.
