Journal ArticleDOI
Mastering the game of Go with deep neural networks and tree search
David Silver,Aja Huang,Chris J. Maddison,Arthur Guez,Laurent Sifre,George van den Driessche,Julian Schrittwieser,Ioannis Antonoglou,Veda Panneershelvam,Marc Lanctot,Sander Dieleman,Dominik Grewe,John Nham,Nal Kalchbrenner,Ilya Sutskever,Timothy P. Lillicrap,Madeleine Leach,Koray Kavukcuoglu,Thore Graepel,Demis Hassabis +19 more
TLDR
Using this search algorithm, the program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0.5, the first time that a computer program has defeated a human professional player in the full-sized game of Go.Abstract:
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 positions and moves. Here we introduce a new approach to computer Go that uses ‘value networks’ to evaluate board positions and ‘policy networks’ to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of stateof-the-art Monte Carlo tree search programs that simulate thousands of random games of self-play. We also introduce a new search algorithm that combines Monte Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.read more
Citations
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Proceedings ArticleDOI
DeepXplore: Automated Whitebox Testing of Deep Learning Systems
TL;DR: DeepXplore as discussed by the authors is a white box framework for systematically testing real-world deep learning (DL) systems, which leverages multiple DL systems with similar functionality as cross-referencing oracles to avoid manual checking.
Journal ArticleDOI
Interactive machine learning for health informatics: when do we need the human-in-the-loop?
TL;DR: Interactive machine learning (iML) is defined as “algorithms that can interact with agents and can optimize their learning behavior through these interactions, where the agents can also be human.”
Posted Content
Adversarial Learning for Neural Dialogue Generation
TL;DR: This paper proposed using adversarial training for open-domain dialogue generation, where the generator is trained to generate sequences that are indistinguishable from human-generated dialogue utterances, and the outputs from the discriminator are used as rewards for the generator.
Proceedings ArticleDOI
Adversarial Learning for Neural Dialogue Generation
TL;DR: This work applies adversarial training to open-domain dialogue generation, training a system to produce sequences that are indistinguishable from human-generated dialogue utterances, and investigates models for adversarial evaluation that uses success in fooling an adversary as a dialogue evaluation metric, while avoiding a number of potential pitfalls.
Journal ArticleDOI
State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent Network Traffic Control Systems
Zubair Md. Fadlullah,Fengxiao Tang,Bomin Mao,Nei Kato,Osamu Akashi,Takeru Inoue,Kimihiro Mizutani +6 more
TL;DR: An overview of the state-of-the-art deep learning architectures and algorithms relevant to the network traffic control systems, and a new use case, i.e., deep learning based intelligent routing, which is demonstrated to be effective in contrast with the conventional routing strategy.
References
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Journal ArticleDOI
Human-level control through deep reinforcement learning
Volodymyr Mnih,Koray Kavukcuoglu,David Silver,Andrei Rusu,Joel Veness,Marc G. Bellemare,Alex Graves,Martin Riedmiller,Andreas K. Fidjeland,Georg Ostrovski,Stig Petersen,Charles Beattie,Amir Sadik,Ioannis Antonoglou,Helen King,Dharshan Kumaran,Daan Wierstra,Shane Legg,Demis Hassabis +18 more
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