@Book{stone00a,
  author =	 {Peter Stone},
  title =	 {Layered Learning in Multiagent Systems},
  googleid =	 {s7Zbsj9xKVIJ:scholar.google.com/},
  publisher =	 {{MIT} Press},
  year =	 2000,
  abstract =	 {This book looks at multiagent systems that consist
                  of teams of autonomous agents acting in real-time,
                  noisy, collaborative, and adversarial
                  environments. The book makes four main contributions
                  to the fields of machine learning and multiagent
                  systems. First, it describes an architecture within
                  which a flexible team structure allows member agents
                  to decompose a task into flexible roles and to
                  switch roles while acting. Second, it presents
                  layered learning, a general-purpose machine-learning
                  method for complex domains in which learning a
                  mapping directly from agents' sensors to their
                  actuators is intractable with existing
                  machine-learning methods. Third, the book introduces
                  a new multiagent reinforcement learning
                  algorithm--team-partitioned, opaque-transition
                  reinforcement learning (TPOT-RL)--designed for
                  domains in which agents cannot necessarily observe
                  the state-changes caused by other agents'
                  actions. The final contribution is a fully
                  functioning multiagent system that incorporates
                  learning in a real-time, noisy domain with teammates
                  and adversaries--a computer-simulated robotic soccer
                  team.},
  keywords =     {multiagent learning robocup},
  isbn =	 {0262194384},
  cluster = 	 {6673121134849295024}
}