GAME is a new adversarial coevolutionary QD algorithm using generational alternation and vision embeddings that outperforms one-sided baselines across battle, wrestling, and deck-building tasks while revealing arms-race dynamics and the role of neutral mutations.
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The paper introduces the RECLAIM framework and OMEGA shift as a transition from top-down optimization to autopoietic cognitive ecologies for cultivating machine intelligence.
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Adversarial Coevolutionary Illumination with Generational Adversarial MAP-Elites
GAME is a new adversarial coevolutionary QD algorithm using generational alternation and vision embeddings that outperforms one-sided baselines across battle, wrestling, and deck-building tasks while revealing arms-race dynamics and the role of neutral mutations.