Healthcare predictive analytics using machine learning and deep learning techniques: a survey
Healthcare prediction has been a significant factor in saving lives in recent years. In the
domain of health care, there is a rapid development of intelligent systems for analyzing …
domain of health care, there is a rapid development of intelligent systems for analyzing …
Artificial intelligence-based traffic flow prediction: a comprehensive review
The expansion of the Internet of Things has resulted in new creative solutions, such as smart
cities, that have made our lives more productive, convenient, and intelligent. The core of …
cities, that have made our lives more productive, convenient, and intelligent. The core of …
A survey of monte carlo tree search methods
Monte Carlo tree search (MCTS) is a recently proposed search method that combines the
precision of tree search with the generality of random sampling. It has received considerable …
precision of tree search with the generality of random sampling. It has received considerable …
Mastering the game of Go with deep neural networks and tree search
The game of Go has long been viewed as the most challenging of classic games for artificial
intelligence owing to its enormous search space and the difficulty of evaluating board …
intelligence owing to its enormous search space and the difficulty of evaluating board …
On monte carlo tree search and reinforcement learning
Fuelled by successes in Computer Go, Monte Carlo tree search (MCTS) has achieved wide-
spread adoption within the games community. Its links to traditional reinforcement learning …
spread adoption within the games community. Its links to traditional reinforcement learning …
Monte-carlo robot path planning
Path planning is a crucial algorithmic approach for designing robot behaviors. Sampling-
based approaches, like rapidly exploring random trees (RRTs) or probabilistic roadmaps …
based approaches, like rapidly exploring random trees (RRTs) or probabilistic roadmaps …
[HTML][HTML] Monte carlo tree search-based deep reinforcement learning for flexible operation & maintenance optimization of a nuclear power plant
Nuclear power plants (NPPs) are required to operate on a flexible profitable production plan
while guaranteeing high safety standards. Deep reinforcement learning (DRL) is an effective …
while guaranteeing high safety standards. Deep reinforcement learning (DRL) is an effective …
A Monte-Carlo tree search algorithm for the flexible job-shop scheduling in manufacturing systems
Flexible job-shop scheduling problem (FJSP) is an extension of the simple JSP with
additional features of routing flexibility. It is an essential class of sequencing and planning …
additional features of routing flexibility. It is an essential class of sequencing and planning …
[BUCH][B] Learning to play: reinforcement learning and games
A Plaat - 2020 - books.google.com
In this textbook the author takes as inspiration recent breakthroughs in game playing to
explain how and why deep reinforcement learning works. In particular he shows why two …
explain how and why deep reinforcement learning works. In particular he shows why two …
N-grams and the last-good-reply policy applied in general game playing
MJW Tak, MHM Winands… - IEEE Transactions on …, 2012 - ieeexplore.ieee.org
The aim of general game playing (GGP) is to create programs capable of playing a wide
range of different games at an expert level, given only the rules of the game. The most …
range of different games at an expert level, given only the rules of the game. The most …