On novelty driven evolution in Poker

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  author =       "J. P. C. Bonson and A. R. McIntyre and M. I. Heywood",
  booktitle =    "2016 IEEE Symposium Series on Computational
                 Intelligence (SSCI)",
  title =        "On novelty driven evolution in Poker",
  year =         "2016",
  abstract =     "This work asks the question as to whether `novelty as
                 an objective' is still beneficial under tasks with a
                 lot of ambiguity, such as Poker. Specifically, Poker
                 represents a task in which there is partial information
                 (public and private cards) and stochastic changes in
                 state (what card will be dealt next). In addition,
                 bluffing plays a fundamental role in successful
                 strategies for playing the game. On the face of it, it
                 appears that multiple sources of variation already
                 exist, making the additional provision of novelty as an
                 objective unwarranted. Indeed, most previous work in
                 which agent strategies are evolved with novelty
                 appearing as an explicit objective are not rich in
                 sources of ambiguity. Conversely, the task of learning
                 strategies for playing Poker, even under the 2-player
                 case of heads-up Limit Texas Hold'em, is widely
                 considered to be particularly challenging on account of
                 the multiple sources of uncertainty. We benchmark a
                 form of genetic programming, both with and without
                 (task independent) novelty objectives. It is clear that
                 pursuing behavioural diversity, even under the heads-up
                 Limit Texas Hold'em task is central to learning
                 successful strategies. Benchmarking against static and
                 Bayesian opponents illustrates the capability of the
                 resulting Genetic Programming (GP) agents to bluff and
                 vary their style of play.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/SSCI.2016.7849968",
  month =        dec,
  notes =        "Also known as \cite{7849968}",

Genetic Programming entries for J P C Bonson Andrew R McIntyre Malcolm Heywood