Probabilistic Semantics and Defaults
This addresses a foundational issue in AI for handling defaults and uncertainty, though it appears incremental as it builds on existing probabilistic concepts.
The paper tackles the problem of reasoning under uncertainty without numeric probabilities by introducing a non-numeric formalism called an inference graph, which handles examples from nonmonotonic literature and allows verification through experiments.
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.