@inproceedings{huang03a,
  author =	 {Pu Huang and Katia Sycara},
  title =	 {Multi-agent learning in extensive games with
                  complete information},
  booktitle =	 {Proceedings of the second international joint
                  conference on Autonomous agents and multiagent
                  systems},
  year =	 2003,
  pages =	 {701--708},
  location =	 {Melbourne, Australia},
  doi =		 {10.1145/860575.860688},
  publisher =	 {ACM Press},
  abstract =	 {Learning in a multi-agent system is di cult because
                  the learning environment jointly created by all
                  learning agents is time-variant. This paper studies
                  the model of multi-agent learning in
                  complete-information extensive games (CEGs). We
                  provide two provably convergent algorithms for this
                  model. Both algorithms utilize the special structure
                  of CEGs and guarantee both individual and collective
                  convergence. Our work contributes to the multi-agent
                  learning literature in several aspects: 1. We
                  identify a model of multi-agent learn- ing, namely,
                  learning in CEGs, and provide two provably
                  convergent algorithms for this model. 2. We
                  explicitly address the environment-shifting problem
                  and show that how patient agents can collectively
                  learn to play equilib- rium strategies. 3. Many
                  game-theoretical work on learning uses a technique
                  called fictitious play, which requires agents to
                  build beliefs about their opponents. For our model
                  of learning in CEGs, we show it is true that agents
                  can collec- tively converge to the sub-game perfect
                  equilibrium (SPE) by repeatedly reinforcing their
                  previous success/failure ex- perience; no belief
                  building is necessary.},
  keywords =     {multiagent game-theory learning},
  url =		 {http://jmvidal.cse.sc.edu/library/huang03a.pdf},
  googleid = 	 {o1RUIWpozS0J:scholar.google.com/},
  comment =	 {masrg},
  cluster = 	 {3300408906967438499},
}