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Jump’n’Bump AI. The Search For Adequacy. Jump’n’Bump Refresher. ?. Progress. First try: Evolving weights over fixed topology networks. Result: Best results when evolved against stationary target (unimpressive). Problem: Search space too large?. Evolving with NEAT.

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Jump n bump ai

Jump’n’Bump AI

The Search For Adequacy



Progress
Progress

  • First try: Evolving weights over fixed topology networks. Result: Best results when evolved against stationary target (unimpressive).

  • Problem: Search space too large?


Evolving with neat
Evolving with NEAT

  • Java implementation: ANJI (Another Neat…)

  • Many different evolution opportunities:

    • Direct Tournament (play against fixed opponents)

    • K-Random Opponents

    • Single-Elimination Tournament

  • Best results seem to be from direct tournament against hand-coded AI.

  • But need quantitative answer, not qualitative observations


The grand tournament
The Grand Tournament

  • 13 Contestants

    • Fixed Topology Networks

    • NEAT Evolved Networks

      • Direct, single-elimination, and k-random opponents tournaments

    • Hand Coded AI

  • 98000 Jump’n’Bump Match Round-Robin

  • Conclusion: Jump’n’Bump playing ability is not transitive.

  • Overall Winner: Hand Coded AI


Conclusions
Conclusions

  • NEAT seems to be able to evolve an AI better than anything you can pit it against…

  • …but to be better than human, you would already need a human-class AI

  • Also: AIs that are evolved against each other generalize poorly

  • Maybe the Internet could be a solution



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