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Computer Science

D-Index
71
Citations
23379
World Ranking
1761
National Ranking
896

Overview

Kenneth O. Stanley is affiliated with the University of Central Florida in the United States. Their research primarily focuses on computer science, with an emphasis on artificial intelligence as the main subfield. Other areas of study include cognitive neuroscience, mechanical engineering, electrical and electronic engineering, and computational theory and mathematics.

The scientist's recent publications cover a range of topics related to evolutionary computation, reinforcement learning, and neural network training. Notable papers include:

  • The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities, 2020, Artificial Life
  • Learning to Continually Learn, 2020, arXiv (Cornell University)
  • Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their Solutions, 2020, arXiv (Cornell University)
  • Backpropamine: training self-modifying neural networks with differentiable neuromodulated plasticity, 2020, arXiv (Cornell University)

Frequent coauthors in Kenneth O. Stanley's work include:

  • Joel Lehman
  • Jeff Clune
  • Thomas Miconi
  • Aditya Rawal
  • Nick Cheney

Their studies have been published mainly in venues such as:

  • arXiv (Cornell University)
  • Artificial Life
  • Proceedings of the AAAI Conference on Artificial Intelligence

Research topics addressed by Stanley often encompass:

  • Reinforcement Learning in Robotics
  • Evolutionary Algorithms and Applications
  • Modular Robots and Swarm Intelligence
  • EEG and Brain-Computer Interfaces
  • Advanced Memory and Neural Computing
  • Explainable Artificial Intelligence (XAI)
  • Evolutionary Game Theory and Cooperation

Best Publications

  • Evolving neural networks through augmenting topologies

    Kenneth O. Stanley;Risto Miikkulainen

  • Abandoning objectives: Evolution through the search for novelty alone

    Joel Lehman;Kenneth O. Stanley

  • A hypercube-based encoding for evolving large-scale neural networks

    Kenneth O. Stanley;David B. D'Ambrosio;Jason Gauci

  • Search-Based Procedural Content Generation: A Taxonomy and Survey

    J. Togelius;G. N. Yannakakis;K. O. Stanley;C. Browne

  • Compositional pattern producing networks: A novel abstraction of development

    Kenneth O. Stanley

  • 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

  • Designing neural networks through neuroevolution

    Kenneth O. Stanley;Kenneth O. Stanley;Jeff Clune;Jeff Clune;Joel Lehman;Risto Miikkulainen

  • A Taxonomy for artificial embryogeny

    Kenneth O. Stanley;Risto Miikkulainen

  • Exploiting Open-Endedness to Solve Problems Through the Search for Novelty

    Joel Lehman;Kenneth O. Stanley

  • Competitive coevolution through evolutionary complexification

    Kenneth O. Stanley;Risto Miikkulainen

  • Real-time neuroevolution in the NERO video game

    K.O. Stanley;B.D. Bryant;R. Miikkulainen

  • Quality Diversity: A New Frontier for Evolutionary Computation

    Justin K. Pugh;Lisa B. Soros;Kenneth O. Stanley

  • Evolving a diversity of virtual creatures through novelty search and local competition

    Joel Lehman;Kenneth O. Stanley

  • First return, then explore

    Adrien Ecoffet;Adrien Ecoffet;Joost Huizinga;Joost Huizinga;Joel Lehman;Joel Lehman;Kenneth O. Stanley;Kenneth O. Stanley

  • Efficient Reinforcement Learning Through Evolving Neural Network Topologies

    Kenneth O. Stanley;Risto Miikkulainen

  • Go-Explore: a New Approach for Hard-Exploration Problems

    Adrien Ecoffet;Joost Huizinga;Joel Lehman;Kenneth O. Stanley

  • Improving Exploration in Evolution Strategies for Deep Reinforcement Learning via a Population of Novelty-Seeking Agents

    Edoardo Conti;Vashisht Madhavan;Felipe Petroski Such;Joel Lehman

  • The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities

    Joel Lehman;Jeff Clune;Dusan Misevic;Christoph Adami

  • Efficient evolution of neural networks through complexification

    Kenneth Owen Stanley;Risto P. Miikkulainen

  • Picbreeder: evolving pictures collaboratively online

    Jimmy Secretan;Nicholas Beato;David B. D Ambrosio;Adelein Rodriguez

  • The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities

    Joel Lehman;Jeff Clune;Dusan Misevic;Christoph Adami

Frequent Co-Authors

Jeff Clune
Jeff Clune University of British Columbia
Risto Miikkulainen
Risto Miikkulainen The University of Texas at Austin
Sebastian Risi
Sebastian Risi IT University of Copenhagen
Charles Ofria
Charles Ofria Michigan State University
Julian Togelius
Julian Togelius New York University
Kalyanmoy Deb
Kalyanmoy Deb Michigan State University
Marc Schoenauer
Marc Schoenauer French Institute for Research in Computer Science and Automation - INRIA
Hod Lipson
Hod Lipson Columbia University
François Taddei
François Taddei Université Paris Cité

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