In this task, you are given the abstract of a research paper. Your task is to generate a summary of this abstract. Your summary should not be very short, but it's better if it's not more than 30 words.

In multiagent systems (MASs), each agent makes individual decisions but all of them contribute globally to the system evolution. Learning in MASs is difficult since each agent's selection of actions must take place in the presence of other co-learning agents. Moreover, the environmental stochasticity and uncertainties increase exponentially with the increase in the number of agents. Previous works borrow various multiagent coordination mechanisms into deep learning architecture to facilitate multiagent coordination. However, none of them explicitly consider action semantics between agents that different actions have different influences on other agents. In this paper, we propose a novel network architecture, named Action Semantics Network (ASN), that explicitly represents such action semantics between agents. ASN characterizes different actions' influence on other agents using neural networks based on the action semantics between them. ASN can be easily combined with existing deep reinforcement learning (DRL) algorithms to boost their performance. Experimental results on StarCraft II micromanagement and Neural MMO show ASN significantly improves the performance of state-of-the-art DRL approaches compared with several network architectures.
Our proposed ASN characterizes different actions' influence on other agents using neural networks based on the action semantics between them.

Knowledge Bases (KBs) are becoming increasingly large, sparse and probabilistic. These KBs are typically used to perform query inferences and rule mining. But their efficacy is only as high as their completeness. Efficiently utilizing incomplete KBs remains a major challenge as the current KB completion techniques either do not take into account the inherent uncertainty associated with each KB tuple or do not scale to large KBs.

 Probabilistic rule learning not only considers the probability of every KB tuple but also tackles the problem of KB completion in an explainable way. For any given probabilistic KB, it learns probabilistic first-order rules from its relations to identify interesting patterns. But, the current probabilistic rule learning techniques perform grounding to do probabilistic inference for evaluation of candidate rules. It does not scale well to large KBs as the time complexity of inference using grounding is exponential over the size of the KB. In this paper, we present SafeLearner -- a scalable solution to probabilistic KB completion that performs probabilistic rule learning using lifted probabilistic inference -- as faster approach instead of grounding. 

 We compared SafeLearner to the state-of-the-art probabilistic rule learner ProbFOIL+ and to its deterministic contemporary AMIE+ on standard probabilistic KBs of NELL (Never-Ending Language Learner) and Yago. Our results demonstrate that SafeLearner scales as good as AMIE+ when learning simple rules and is also significantly faster than ProbFOIL+.
Probabilistic Rule Learning system using Lifted Inference

Historically, the pursuit of efficient inference has been one of the driving forces be-hind the research into new deep learning architectures and building blocks. Some of the recent examples include:  the squeeze-and-excitation module of (Hu et al.,2018), depthwise separable convolutions in Xception (Chollet, 2017), and the inverted bottleneck in MobileNet v2 (Sandler et al., 2018).   Notably, in all of these cases, the resulting building blocks enabled not only higher efficiency, but also higher accuracy, and found wide adoption in the field. In this work, we further expand the arsenal of efficient building blocks for neural network architectures; but instead of combining standard primitives (such as convolution), we advocate for the replacement of these dense primitives with their sparse counterparts.   While the idea of using sparsity to decrease the parameter count is not new (Mozer & Smolensky, 1989), the conventional wisdom is that this reduction in theoretical FLOPs does not translate into real-world efficiency gains.   We aim to correct this misconception by introducing a family of efficient sparse kernels for several hardware platforms, which we plan to open-source for the benefit of the community. Equipped with our efficient implementation of sparse primitives,  we show that sparse versions of MobileNet  v1 and MobileNet  v2  architectures  substantially outperform strong dense baselines on the efficiency-accuracy curve.    On Snapdragon 835 our sparse networks outperform their dense equivalents by 1.1−2.2x – equivalent to approximately one entire generation of improvement.   We hope that our findings will facilitate wider adoption of sparsity as a tool for creating efficient and accurate deep learning architectures.

Sparse MobileNets are faster than Dense ones with the appropriate kernels.