Evolving Soccer Keepaway Players Through Task Decomposition

By Shimon Whiteson, Nate Kohl, Risto Miikkulainen, Peter Stone. 2005

92 citations recorded by the source corpus.

Subfields: Artificial Intelligence

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. Shimon Whiteson
2. J. Urgen Schmidhuber
3. Kenneth O. Stanley
4. Jason Gauci
5. Jeff Clune
6. Sebastian Risi
7. Risto Miikkulainen
8. Sergey Levine

Citations in the corpus

• Autonomous Evolution of Topographic Regularities in Artificial Neural Networks (2010, methodology): “Like other work on NE (Gomez, Schmidhuber, & Miikkulainen, 2008 ; Stanley & Miikkulainen, 2002c ; Whiteson, Kohl, Miikkulainen, & Stone, 2003 ), it includes a performance comparison that demonstrates the advantage of its approachin this case, in the domain of checkers.”
• Evolutionary Computation for Reinforcement Learning (2012, methodology): “For example, in the task of robot soccer keepaway, domain knowledge has been used to decompose the task into different components, each representing an important skill such as running towards the ball or getting open for a pass (Whiteson et al, 2005).”