Markov Logic Style Weighted Rules under the Stable Model Semantics
نویسندگان
چکیده
We introduce the language LP that extends logic programs under the stable model semantics to allow weighted rules similar to the way Markov Logic considers weighted formulas. LP is a proper extension of the stable model semantics to enable probabilistic reasoning, providing a way to handle inconsistency in answer set programming. We also show that the recently established logical relationship between Pearl’s Causal Models and answer set programs can be extended to the probabilistic setting via LP.
منابع مشابه
Weighted Rules under the Stable Model Semantics
We introduce the concept of weighted rules under the stable model semantics following the log-linear models of Markov Logic. This provides versatile methods to overcome the deterministic nature of the stable model semantics, such as resolving inconsistencies in answer set programs, ranking stable models, associating probability to stable models, and applying statistical inference to computing w...
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