Journal
ACM COMPUTING SURVEYS
Volume 54, Issue 4, Pages -Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3447581
Keywords
Probabilistic rule learning; symbolic rule learning; sub-symbolic rule learning; probabilistic logic programming
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Funding
- InternationalMacquarie Research Excellence Scholarship (iMQRES), Macquarie University, Sydney, Australia
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This survey provides an overview of rule learning systems that can learn the structure of probabilistic rules for uncertain domains, highlighting their usefulness and human-friendly output.
This survey provides an overview of rule learning systems that can learn the structure of probabilistic rules for uncertain domains. These systems are very useful in such domains because they can be trained with a small amount of positive and negative examples, use declarative representations of background knowledge, and combine efficient high-level reasoning with the probability theory. The output of these systems are probabilistic rules that are easy to understand by humans, since the conditions for consequences lead to predictions that become transparent and interpretable. This survey focuses on representational approaches and system architectures, and suggests future research directions.
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