The Internet as a Virtual Ecology: Coevolutionary Arms Races Between Human and Artificial Populations

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@TechReport{cs-97-197,
  author =       "Pablo Funes and Elizabeth Sklar and Hugues Juille and 
                 Jordan Pollack",
  title =        "The Internet as a Virtual Ecology: Coevolutionary Arms
                 Races Between Human and Artificial Populations",
  institution =  "Computer Science, Brandeis University",
  year =         "1997",
  type =         "Technical Report",
  number =       "CS-97-197",
  address =      "415 South St., Waltham MA 02254 USA",
  keywords =     "genetic algorithms, genetic programming, autonomous
                 agents, adaptive software, evolutionary robotics, game
                 learning, coevolution, Tron, interactive evolution",
  URL =          "http://helen.cs-i.brandeis.edu/papers/cs-97-197.pdf",
  URL =          "http://helen.cs-i.brandeis.edu/papers/cs-97-197.ps.gz",
  URL =          "http://helen.cs-i.brandeis.edu/papers/cs-97-197.ps",
  URL =          "http://www.demo.cs.brandeis.edu/papers/long.html#cs-97-197",
  abstract =     "we propose that learning complex behaviours can be
                 achieved in a coevolutionary environment where one
                 population consists of the human users of an
                 interactive adaptive software tool and the
                 {"}opposing{"} population is artificial, generated by a
                 coevolutionary learning engine. We take advantage of
                 the Internet, a connected community where people and
                 software coexist. A new kind of adaptive agent can
                 exploit its interactions with thousands of users-inside
                 a virtual {"}niche{"}-to learn in a coevolutionary
                 human-robot arms race. Our model is Tron, a simple
                 dynamic game where introspective self-play quickly
                 leads to collusive stagnation. We describe an
                 application where thousands of small programs are sent
                 to play with people through the Java interpreter
                 running in their web browsers. The feedback provided by
                 these agents is collected in our server and used to
                 augment an ever improving fitness landscape for local
                 robot-robot games. Speciation and fitness sharing
                 provide diversity to challenge humans with a variety of
                 differ ent strategies. In this way, we obtain an
                 evolving environment where human as well as artificial
                 adaptation are simultaneously taking place.",
  notes =        "See also \cite{funes_sab98} and
                 http://helen.cs-i.brandeis.edu/tron/html/about.html

                 Two populations: computer play (1000), playing against
                 people (100). Generation based. non-standard selection
                 and migration strategies. Deterministic play. Limited
                 knowledge of game arena. Java. Problem with {"}live and
                 let live{"} or conclusion between (evolved)
                 players.

                 p10 People played 22494 games in two months. p11
                 Marginal improvement in computer players (28% -> 35%).
                 Humans better than computer.

                 ",
  size =         "20 pages",
}

Genetic Programming entries for Pablo J Funes Elizabeth Sklar Hugues Juille Jordan B Pollack