Handling Uncertainty in Answer Set Programming
DOI:
https://doi.org/10.1609/aaai.v29i1.9726Keywords:
answer set programming, stable model semantics, markov logic networkAbstract
We present a probabilistic extension of logic programs under the stable model semantics, inspired by the concept of Markov Logic Networks. The proposed language takes advantage of both formalisms in a single framework, allowing us to represent commonsense reasoning problems that require both logical and probabilistic reasoning in an intuitive and elaboration tolerant way.
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Published
2015-03-04
How to Cite
Wang, Y., & Lee, J. (2015). Handling Uncertainty in Answer Set Programming. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1). https://doi.org/10.1609/aaai.v29i1.9726
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