Anthropic Dials Back AI Safety Commitments - WSJ
<a href="https://news.google.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?oc=5" target="_blank">Anthropic Dials Back AI Safety Commitments</a> <font color="#6f6f6f">WSJ</font>
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A Player Selection Network for Scalable Game-Theoretic Prediction and Planning
arXiv:2505.00213v3 Announce Type: replace Abstract: While game-theoretic planning frameworks are effective at modeling multi-agent interactions, they require solving large optimization problems where the number of variables increases with the number of agents, resulting in long computation times that limit their use in large-scale, real-time systems. To address this issue, we propose 1) PSN Game-a learning-based, game-theoretic prediction and planning framework that reduces game size by learning a Player Selection Network (PSN); and 2) a Goal Inference Network (GIN) that makes it possible to use the PSN in incomplete-information games where other agents' intentions are unknown to the ego agent. A PSN outputs a player selection mask that distinguishes influential players from less relevant
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