Knowledge-Based Probabilistic Logic Learning
DOI:
https://doi.org/10.1609/aaai.v29i1.9690Keywords:
Relational Probabilistic Models, Uncertainty in AI, Knowledge-Intensive LearningAbstract
Advice giving has been long explored in artificial intelligence to build robust learning algorithms. We consider advice giving in relational domains where the noise is systematic. The advice is provided as logical statements that are then explicitly considered by the learning algorithm at every update. Our empirical evidence proves that human advice can effectively accelerate learning in noisy structured domains where so far humans have been merely used as labelers or as designers of initial structure of the model.
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Published
2015-03-04
How to Cite
Odom, P., Khot, T., Porter, R., & Natarajan, S. (2015). Knowledge-Based Probabilistic Logic Learning. Proceedings of the AAAI Conference on Artificial Intelligence, 29(1). https://doi.org/10.1609/aaai.v29i1.9690
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Section
AAAI Technical Track: Reasoning under Uncertainty