用游戏训练机器人，发现它能自发形成自我和世界认知。
Probing for Consciousness in Machines
- 用强化学习让机器人玩视频游戏，观察其能否自发生成自我与世界模型。
- 通过神经网络激活值预测自身位置，准确率达68.3%，表明模型已具备自指能力。
- 为机器意识研究提供新路径，适合对认知科学与AI哲学感兴趣的读者。
本研究探讨了人工智能体根据安东尼奥·达马西奥的意识理论发展核心意识的可能性。根据该理论，核心意识的出现依赖于由情绪与感受表征所支持的自我模型与世界模型的整合。我们假设，在虚拟环境中通过强化学习（RL）训练的人工智能体，可在其主要任务的副产品中形成初步的这两种模型。该代理的主要目标是学习玩视频游戏并探索环境。为了评估世界模型与自我模型的出现，我们采用前馈分类器作为探测器，利用训练后代理神经网络的激活值来预测代理自身的空间位置。结果显示，该代理能够形成基础的世界模型与自我模型，表明机器意识存在潜在的发展路径。本研究为人工代理在模拟人类意识方面的能力提供了基础性见解，对未来人工智能的发展具有重要意义。
原文摘要 · Abstract (English)
This study explores the potential for artificial agents to develop core consciousness, as proposed by Antonio Damasio's theory of consciousness. According to Damasio, the emergence of core consciousness relies on the integration of a self model, informed by representations of emotions and feelings, and a world model. We hypothesize that an artificial agent, trained via reinforcement learning (RL) in a virtual environment, can develop preliminary forms of these models as a byproduct of its primary task. The agent's main objective is to learn to play a video game and explore the environment. To evaluate the emergence of world and self models, we employ probes-feedforward classifiers that use the activations of the trained agent's neural networks to predict the spatial positions of the agent itself. Our results demonstrate that the agent can form rudimentary world and self models, suggesting a pathway toward developing machine consciousness. This research provides foundational insights into the capabilities of artificial agents in mirroring aspects of human consciousness, with implications for future advancements in artificial intelligence.
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