Uncertainty in Artificial Intelligence
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Probabilistic Semantics and Defaults
Eric Neufeld, David Poole, Romas Aleliunas
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.
Keywords: null
Pages: 121-131
PS Link:
PDF Link: /papers/88/p121-neufeld.pdf
AUTHOR = "Eric Neufeld and David Poole and Romas Aleliunas",
TITLE = "Probabilistic Semantics and Defaults",
BOOKTITLE = "Uncertainty in Artificial Intelligence 4 Annual Conference on Uncertainty in Artificial Intelligence (UAI-88)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1988",
PAGES = "121--131"

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