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AI Researchers Produce New Kind of PC Game

Posted by ryuzaki0 on from the learning-by-killing dept.

Ken Stanley writes "In an unusual demonstration of video game innovation with limited funding and resources, a mostly volunteer team of over 30 student programmers, artists, and researchers at the University of Texas at Austin has produced a new game genre in which the player interacively trains robotic soldiers for combat. Unlike most games today that use scripting for the AI, non-player-characters in NERO learn new tactics in real-time using advanced machine learning techniques. Perhaps projects such as this one will encourage the video game industry to begin to seek alternatives to simple scripted AI."

4 of 342 comments (clear)

  1. Coral Cache by Anonymous Coward · · Score: 5, Informative

    Slashdotted before it even went live. Here is a working link. Downloads are currently at 511, I hope their counter has more than 9 bits...

  2. Not at all new by Digital+Avatar · · Score: 5, Informative

    This isn't entirely a new idea. CROBOTS, for example, put one in the position of designing AIs that control tanks and then pits them against one another in an arena.

  3. Torrent by TaxSlave · · Score: 4, Informative

    Only for the purposes of helping distribution, and for a limited time, torrent available at nerogame.exe.torrent

    --
    Visit Lockjaw's Lair. He won't bite.
  4. Re:AI? on a video card? by adam31 · · Score: 4, Informative
    but floating point operations aren't exactly optimal for things like AI.

    False.

    FLOPs are not generally useful for things like scripted AI which are very branch heavy with a lot of indirection, and many possible branch targets and data requirements.

    The techniques described in this game are highly mathematical in nature with a small memory foot-print, (adaptive neural networks and genetic programming via Kenneth Stanley's NEAT algorithm) and would benefit hugely from parallel vector proccessing.

    Additionally, at the end of the day, the AI decision making is not nearly as expensive as the proximity-query and pathfinding routines that affect the decisions. These routines also benefit hugely from vector processors and high bus-bandwidth.

    So fittingly, the AI will only suffer if the human intelligence can't adapt and make the fairly obvious decision to move toward more mathematical AI routines.