• Title of article

    What evolutionary game theory tells us about multiagent learning Original Research Article

  • Author/Authors

    Karl Tuyls، نويسنده , , Peter McBurney and Simon Parsons ، نويسنده ,

  • Issue Information
    روزنامه با شماره پیاپی سال 2007
  • Pages
    11
  • From page
    406
  • To page
    416
  • Abstract
    This paper discusses If multi-agent learning is the answer, what is the question? [Y. Shoham, R. Powers, T. Grenager, If multi-agent learning is the answer, what is the question? Artificial Intelligence 171 (7) (2007) 365–377, this issue] from the perspective of evolutionary game theory. We briefly discuss the concepts of evolutionary game theory, and examine the main conclusions from [Y. Shoham, R. Powers, T. Grenager, If multi-agent learning is the answer, what is the question? Artificial Intelligence 171 (7) (2007) 365–377, this issue] with respect to some of our previous work. Overall we find much to agree with, concluding, however, that the central concerns of multiagent learning are rather narrow compared with the broad variety of work identified in [Y. Shoham, R. Powers, T. Grenager, If multi-agent learning is the answer, what is the question? Artificial Inteligence 171 (7) (2007) 365–377, this issue].
  • Keywords
    Evolutionary game theory , Replicator dynamics , Multiagent learning
  • Journal title
    Artificial Intelligence
  • Serial Year
    2007
  • Journal title
    Artificial Intelligence
  • Record number

    1207536