Evolving a Roving Eye for Go

By Kenneth O. Stanley, Risto Miikkulainen. 2004

109 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. J. Urgen Schmidhuber
2. Sergey Levine
3. Shimon Whiteson
4. Pieter Abbeel
5. Kenneth O. Stanley
6. Yoshua Bengio
7. Quoc V. Le
8. Jeff Clune

In Computer Vision and Pattern Recognition

1. Luc Van Gool
2. J. Urgen Schmidhuber
3. Xiaogang Wang
4. Trevor Darrell
5. Dacheng Tao
6. Jitendra Malik
7. Ming-Hsuan Yang
8. Li Fei-Fei

Citations in the corpus

• On the Binding Problem in Artificial Neural Networks (2020, methodology): “A policy for the agent (frequently implemented by a neural network) can then be evolved (Stanley and Miikkulainen, 2004), trained with Q-learning (Paletta et al.”
• A Neuroevolution Approach to General Atari Game Playing (2014, methodology): “NEAT has been applied to evolving agents for the NERO video game [39] and the game of Go [44].”
• Revising the evolutionary computation abstraction: minimal criteria novelty search (2010, methodology): “proven in many control tasks [3, 16, 31, 32, 34, 35], including maze navigation [16, 31], which is the basis of the experiments in this paper as well as the original novelty search paper [16].”
• Multi-Dimensional Deep Memory Atari-Go Players for Parameter Exploring Policy Gradients (2010, methodology): “For the comparison of the different methods we are using the Capture Game, a simplified version of the two-player board game Go, a game frequently used to demonstrate the power of algorithms [1, 11, 12].”
• Picbreeder: A Case Study in Collaborative Evolutionary Exploration of Design Space (2011, methodology): “Through the compounding effect of branching, and the ability of the underlying NeuroEvolution of Augmenting Topologies (NEAT) algorithm (Stanley and Miikkulainen, 2002b, 2004a) to increase the images’ complexity, users succeed in collaboratively searching the space of possible images.”
• A Hypercube-Based Encoding for Evolving Large-Scale Neural Networks (2009, background): “For example, board games like Checkers [16] and Go [54] likely require hidden nodes to permit effective strategies.”
• Recurrent Models of Visual Attention (2014, background): “[2, 4, 6, 14, 16, 17, 20] have embraced vision as a sequential decision task as we do here.”
• Real-time neuroevolution in the NERO video game (2005, methodology): “Using this approach, several NE algorithms have been applied to board games [11], [12], [63], [64].”
• Evolutionary Computation for Reinforcement Learning (2012, background): “Since TWEANNs can automatically expand their representations, coevolution can give rise to continual complexification (Stanley and Miikkulainen, 2004a).”