Uncertainty in Artificial Intelligence
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Uncertainty and Incompleteness: Breaking the Symmetry of Defeasible Reasoning
Piero Bonissone, David Cyrluk, James Goodwin, Jonathan Stillman
Two major difficulties in using default logics are their intractability and the problem of selecting among multiple extensions. We propose an approach to these problems based on integrating nommonotonic reasoning with plausible reasoning based on triangular norms. A previously proposed system for reasoning with uncertainty (RUM) performs uncertain monotonic inferences on an acyclic graph. We have extended RUM to allow nommonotonic inferences and cycles within nonmonotonic rules. By restricting the size and complexity of the nommonotonic cycles we can still perform efficient inferences. Uncertainty measures provide a basis for deciding among multiple defaults. Different algorithms and heuristics for finding the optimal defaults are discussed.
Keywords: null
Pages: 67-85
PS Link:
PDF Link: /papers/89/p67-bonissone.pdf
AUTHOR = "Piero Bonissone and David Cyrluk and James Goodwin and Jonathan Stillman",
TITLE = "Uncertainty and Incompleteness: Breaking the Symmetry of Defeasible Reasoning",
BOOKTITLE = "Uncertainty in Artificial Intelligence 5 Annual Conference on Uncertainty in Artificial Intelligence (UAI-89)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1989",
PAGES = "67--85"

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