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What can AI Learn from Human Exploration? Intrinsically-Motivated Humans and Agents in Open-World Exploration – Center for Human-Compatible Artificial Intelligence

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Paper titled What can AI Learn from Human Exploration? Intrinsically-Motivated Humans and Agents in Open-World Exploration was selected for an oral presentation at the Intrinsically Motivated Open-Ended Learning workshop at NeurIPS 2023 conference that took place on 12/16/2023 in New Orleans. In their paper, originally published on 10/20/2023, the authors Yuqing Du, Eliza Kosoy, Alyssa Dayan, Maria Rufova, Pieter Abbeel, and Alison Gopnik compare human and AI agent exploration in a complex, open-ended environment. What drives exploration? Understanding intrinsic motivation is a long-standing question in both cognitive science and artificial intelligence (AI); numerous exploration objectives have been proposed and tested in human experiments and used to train reinforcement learning (RL) agents. However, experiments in the former are often in simplistic environments that do not capture the complexity of real world exploration. On the other hand, experiments in the latter use more complex

What can AI Learn from Human Exploration? Intrinsically-Motivated Humans and Agents in Open-World Exploration – Center for Human-Compatible Artificial Intelligence Paper titled What can AI Learn from Human Exploration? Intrinsically-Motivated Humans and Agents in Open-World Exploration was selected for an oral presentation at the Intrinsically Motivated Open-Ended Learning workshop at NeurIPS 2023 conference that took place on 12/16/2023 in New Orleans. In their paper, originally published on 10/20/2023, the authors Yuqing Du, Eliza Kosoy, Alyssa Dayan, Maria Rufova, Pieter Abbeel, and A

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