Combining logic and probability has been a long stand- ing goal of AI research. Markov Logic Networks (MLNs) achieve this by attaching weights to formulas in first-order logic, and can be seen as templates for constructing features for ground Markov networks. Most techniques for learning weights of MLNs are domain-size agnostic, i.e., the size of the domain is not explicitly taken into account while learn- ing the parameters of the model. This often results in ex- treme probabilities when testing on domain sizes different from those seen during training. In this paper, we propose Domain Aware Markov logic Networks (DA-MLNs) which present a principled solution to this problem. While defin- ing the ground network distribution, DA-MLNs divide the ground feature weight by a scaling factor which is a function of the number of connections the ground atoms appearing in the feature are involved in. We show that standard MLNs fall out as a special case of our formalism when this func- tion evaluates to a constant equal to 1. Experiments on the benchmark Friends & Smokers domain show that our ap- proach results in significantly higher accuracies compared to existing methods when testing on domains whose sizes different from those seen during training.
Quantum Logic Gate Synthesis as a Markov Decision Process
Rigetti Computing（里盖蒂计算公司）；Quantum Artificial Intelligence Laboratory (QuAIL), NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心量子人工智能实验室）；USRA Research Institute for Advanced Computer Science (RIACS)（美国空间研究协会高级计算机科学研究所）；University of Maryland（马里兰大学）；Ames Laboratory（艾姆斯实验室）；Iowa State University（爱荷华州立大学）；Department of Physics and Astronomy, Iowa State University（爱荷华州立大学物理与天文学系）
DRL-STAF: A Deep Reinforcement Learning Framework for State-Aware Forecasting of Complex Multivariate Hidden Markov Processes
Department of Industrial Engineering, Tsinghua University, Beijing 100084, China（清华大学工业工程系）；Department of Industrial and Systems Engineering, University of Iowa, Iowa City, IA 52242, USA（爱荷华大学工业与系统工程系）
