A big-stepped probability approach for discovering default rules. (English) Zbl 1072.68616
Summary: This paper deals with the extraction of default rules from a database of examples. The proposed approach is based on a special kind of probability distributions, called ”big-stepped probabilities”, which are known to provide a semantics for non-monotonic reasoning. The rules which are learnt are genuine default rules, which could be used (under some conditions) in a non-monotonic reasoning system and can be encoded in possibilistic logic.
MSC:
| 68T37 | Reasoning under uncertainty in the context of artificial intelligence |
| 68T05 | Learning and adaptive systems in artificial intelligence |
Keywords:
knowledge discovery; default rules; nonmonotonic reasoning; big stepped probabilities; possibility theoryReferences:
| [1] | DOI: 10.1016/0004-3702(80)90038-7 · Zbl 0445.68067 · doi:10.1016/0004-3702(80)90038-7 |
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