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Jump’n’Bump AI

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.

deirdre-holman
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Jump’n’Bump AI

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  1. Jump’n’Bump AI The Search For Adequacy

  2. Jump’n’Bump Refresher ?

  3. Progress • First try: Evolving weights over fixed topology networks. Result: Best results when evolved against stationary target (unimpressive). • Problem: Search space too large?

  4. 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

  5. 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

  6. 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

  7. Demo?

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