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@incollection{LNAI15-Leottau,
  author = {David L. Leottau and Javier Ruiz-del-Solar and Patrick MacAlpine and Peter Stone},
  title = {A Study of Layered Learning Strategies Applied to Individual Behaviors in Robot Soccer},
  booktitle = {{R}obo{C}up-2015: Robot Soccer World Cup {XIX}},
  Editor={Luis Almeida and Jianmin Ji and Gerald Steinbauer and Sean Luke},
  Publisher="Springer Verlag",
  address="Berlin",
  year="2016",
  series="Lecture Notes in Artificial Intelligence",
  abstract={
    Hierarchical task decomposition strategies allow robots and agents in 
general to address complex decision-making tasks.  Layered learning is a 
hierarchical machine learning paradigm where a complex behavior is learned from 
a series of incrementally trained sub-tasks.  This paper describes how layered 
learning can be applied to design individual behaviors in the context of soccer 
robotics. Three different layered learning strategies are implemented and 
analyzed using a ball-dribbling behavior as a case study.  Performance indices 
for evaluating dribbling speed and ball-control are defined and measured.  
Experimental results validate the usefulness of the implemented layered learning
strategies showing a trade-off between performance and learning speed.
  },
}