We present an algorithm that derives actions' effects and preconditions in partially observable, relational domains. Our algorithm has two unique features: an expressive rela...
Social interactions profoundly impact the learning processes of learners in traditional societies. The rapid rise of the Internet using population has been the establishment of nu...
Ben Chang, Nien-Heng Cheng, Yi-Chan Deng, Tak-Wai ...
Reasoning with both probabilistic and deterministic dependencies is important for many real-world problems, and in particular for the emerging field of statistical relational lear...
We propose a new approach for learning Bayesian classifiers from data. Although it relies on traditional Bayesian network (BN) learning algorithms, the effectiveness of our approa...
Transfer learning aims to solve new learning problems by extracting and making use of the common knowledge found in related domains. A key element of transfer learning is to ident...
Qiang Yang, Vincent Wenchen Zheng, Bin Li, Hankz H...