HyperNEAT-GGP: a hyperNEAT-based Atari General Game Player

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{Hausknecht:2012:GECCO,

• author = "Matthew Hausknecht and Piyush Khandelwal and Risto Miikkulainen and Peter Stone",
• title = "{HyperNEAT-GGP: a hyperNEAT-based Atari} General Game Player",
• booktitle = "GECCO '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference",
• year = "2012",
• editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb",
• pages = "217--224",
• month = "7-11 " # jul,
• organisation = "SIGEVO",
• address = "Philadelphia, Pennsylvania, USA",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• keywords = "genetic algorithms, genetic programming, digital entertainment technologies and arts",
• isbn13 = "978-1-4503-1177-9",
• URL = "http://nn.cs.utexas.edu/downloads/papers/hausknecht.gecco12.pdf",
• DOI = "doi:10.1145/2330163.2330195",
• size = "8 pages",
• abstract = "This paper considers the challenge of enabling agents to learn with as little domain-specific knowledge as possible. The main contribution is HyperNEAT-GGP, a HyperNEAT-based General Game Playing approach to Atari games. By leveraging the geometric regularities present in the Atari game screen, HyperNEAT effectively evolves policies for playing two different Atari games, Asterix and Freeway. Results show that HyperNEAT-GGP outperforms existing benchmarks on these games. HyperNEAT-GGP represents a step towards the ambitious goal of creating an agent capable of learning and seamlessly transitioning between many different tasks.",
• notes = "Also known as \cite{2330195} GECCO-2012 A joint meeting of the twenty first international conference on genetic algorithms (ICGA-2012) and the seventeenth annual genetic programming conference (GP-2012)",

}

Genetic Programming entries for Matthew Hausknecht Piyush Khandelwal Risto Miikkulainen Peter Stone

Citations