Kenneth O. Stanley mostly deals with Artificial intelligence, Artificial neural network, Neuroevolution, Neuroevolution of augmenting topologies and Evolutionary computation. His work on Reinforcement learning and Evolutionary algorithm as part of general Artificial intelligence research is often related to Novelty, thus linking different fields of science. His Artificial neural network study combines topics from a wide range of disciplines, such as Robot and Deep learning.
Much of his study explores Neuroevolution relationship to Video game design. His Neuroevolution of augmenting topologies research integrates issues from Online community, HyperNEAT and Multimedia. His studies deal with areas such as Evolving networks, Time delay neural network and Search algorithm as well as Evolutionary computation.
His main research concerns Artificial intelligence, Artificial neural network, Neuroevolution, Machine learning and Evolutionary computation. His HyperNEAT, Reinforcement learning, Evolutionary algorithm and Encoding study in the realm of Artificial intelligence connects with subjects such as Novelty. His research investigates the connection between Reinforcement learning and topics such as Backpropagation that intersect with issues in Stochastic gradient descent.
His research in Artificial neural network intersects with topics in Domain, Robot and Deep learning. His biological study spans a wide range of topics, including Interactive evolution, Human–computer interaction and Benchmark. His research investigates the connection between Neuroevolution of augmenting topologies and topics such as Video game that intersect with problems in Video game design.
His primary areas of study are Artificial intelligence, Reinforcement learning, Artificial neural network, Neuroevolution and Artificial life. His Artificial intelligence study frequently draws connections between related disciplines such as Machine learning. His Reinforcement learning research includes themes of Gradient descent, Genetic algorithm, Local optimum and Human–computer interaction.
Kenneth O. Stanley has included themes like Variety and Deep learning in his Artificial neural network study. His research investigates the connection with Neuroevolution and areas like Evolutionary robotics which intersect with concerns in Sandbox, Video game and Anticipation. His Artificial life study incorporates themes from Domain, Evolutionary computation and Industrial engineering.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Evolving neural networks through augmenting topologies
Kenneth O. Stanley;Risto Miikkulainen.
Evolutionary Computation (2002)
Abandoning objectives: Evolution through the search for novelty alone
Joel Lehman;Kenneth O. Stanley.
Evolutionary Computation (2011)
A hypercube-based encoding for evolving large-scale neural networks
Kenneth O. Stanley;David B. D'Ambrosio;Jason Gauci.
Artificial Life (2009)
Search-Based Procedural Content Generation: A Taxonomy and Survey
J. Togelius;G. N. Yannakakis;K. O. Stanley;C. Browne.
IEEE Transactions on Computational Intelligence and AI in Games (2011)
Compositional pattern producing networks: A novel abstraction of development
Kenneth O. Stanley.
Genetic Programming and Evolvable Machines (2007)
Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning
Felipe Petroski Such;Vashisht Madhavan;Edoardo Conti;Joel Lehman.
arXiv: Neural and Evolutionary Computing (2017)
A Taxonomy for artificial embryogeny
Kenneth O. Stanley;Risto Miikkulainen.
Artificial Life (2003)
Exploiting Open-Endedness to Solve Problems Through the Search for Novelty
Joel Lehman;Kenneth O. Stanley.
Artificial Life (2008)
Competitive coevolution through evolutionary complexification
Kenneth O. Stanley;Risto Miikkulainen.
Journal of Artificial Intelligence Research (2004)
Real-time neuroevolution in the NERO video game
K.O. Stanley;B.D. Bryant;R. Miikkulainen.
IEEE Transactions on Evolutionary Computation (2005)
If you think any of the details on this page are incorrect, let us know.
We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:
OpenAI
The University of Texas at Austin
New York University
Michigan State University
University of Adelaide
University of Michigan–Ann Arbor
Université Paris Cité
French Institute for Research in Computer Science and Automation - INRIA
University College London
University of York
French Institute for Research in Computer Science and Automation - INRIA
Publications: 25
Wright State University
Zhengzhou University
University of Pardubice
National Institute of Standards and Technology
Edith Cowan University
University of Liège
Aberystwyth University
University of Florence
University of Milan
University of Calgary
University of Colorado Denver
University of Wisconsin–Madison
University of Campania "Luigi Vanvitelli"
Johns Hopkins University
University of Geneva
Australian National University