In this paper we embrace this observation and introduce the Dense Convolutional Network (DenseNet), where each layer is directly connected to every other layer in a feed-forward fashion. Recent work has shown that convolutional networks can be substantially deeper, more accurate and efficient to train if they contain shorter connections between layers close to the input and those close to the output. © 2017 Macmillan Publishers Limited, part of Springer Nature. Starting tabula rasa, our new program AlphaGo Zero achieved superhuman performance, winning 100-0 against the previously published, champion-defeating AlphaGo. This neural network improves the strength of the tree search, resulting in higher quality move selection and stronger self-play in the next iteration. AlphaGo becomes its own teacher: a neural network is trained to predict AlphaGo's own move selections and also the winner of AlphaGo's games.
![the rules of nine mens morris the rules of nine mens morris](https://www.oldest.org/wp-content/uploads/2017/11/Nine-Mens-Morris.jpg)
Here we introduce an algorithm based solely on reinforcement learning, without human data, guidance or domain knowledge beyond game rules. These neural networks were trained by supervised learning from human expert moves, and by reinforcement learning from self-play. The tree search in AlphaGo evaluated positions and selected moves using deep neural networks. Recently, AlphaGo became the first program to defeat a world champion in the game of Go. Nevertheless, we already have intriguing results on the classical Partial Latin Square and N-Queen completion problems.Ī long-standing goal of artificial intelligence is an algorithm that learns, tabula rasa, superhuman proficiency in challenging domains. This research line is still at an early stage, and a number of complex issues remain open. to take into account (soft) constraints that are implicit in past solutions or hard to capture in a traditional declarative model.
In practice, the network could also be used to guide a search process, e.g. From a scientific standpoint, we are interested in whether a DNN can learn the structure of a combinatorial problem, even when trained on (arbitrarily chosen) construction sequences of feasible solutions. The training is done over intermediate steps of the construction of feasible solutions. We train a DNN to extend a feasible solution by making a single, globally consistent, variable assignment. Here, we probe whether a DNN can learn how to construct solutions of a CSP, without any explicit symbolic information about the problem constraints. We use the Logic Tensor Networks framework to introduce logic rules during the training process and establish that they give a positive contribution under multiple dimensions.ĭeep Neural Networks (DNNs) have been shaking the AI scene, for their ability to excel at Machine Learning tasks without relying on complex, hand-crafted, features. Finally, we realize the first implementation of neural-symbolic argument mining. We exploit such a method to train an ensemble of deep residual networks and test them on four different corpora for Argument Mining, reaching or advancing the state of the art in most of the datasets we considered for this study. We analyze Neural Attention, a mechanism widely used in NLP to improve networks' performance and interpretability, providing a taxonomy of its implementations. Then, we move to Argument Mining, a complex NLP task which consists of finding the argumentative elements in a document and identifying their relationships. We observe that the networks are capable of learning to play by the rules and to make feasible assignments in the CSPs. We provide the networks only with examples, without encoding any information regarding the task. We start by addressing two tasks: learning the rule of a game and learning to construct the solution to Constraint Satisfaction Problems. We aim to investigate the behavior of Deep Networks, assessing whether they are capable of learning complex concepts such as rules and constraints without explicit information, and then how to improve them by providing such symbolic knowledge in a general and modular way. Moreover, there are still challenging tasks for Deep Networks: contexts where the success depends on structured knowledge that can not be easily provided to the networks in a standardized way. Nonetheless, Deep Networks are still difficult to interpret, and their inference process is all but transparent.
![the rules of nine mens morris the rules of nine mens morris](https://bargames101.com/wp-content/uploads/2019/12/How-to-Play-Nine-Men%E2%80%99s-Morris.png)
Deep Learning has revolutionized the whole discipline of machine learning, heavily impacting fields such as Computer Vision, Natural Language Processing, and other domains concerned with the processing of raw inputs.