论文标题
导航概念空间；人工通用情报的新看法
Navigating Conceptual Space; A new take on Artificial General Intelligence
论文作者
论文摘要
爱德华·C·托尔曼（Edward C. Tolman）发现，强化学习对解释智力的解释不满意，并提出了学习和行为之间的明确区别。托尔曼（Tolman）对潜在学习和认知图的想法最终导致了现在所谓的概念空间，一种几何表示，概念和思想可以形成点或形状。思想之间的主动导航 - 推理 - 推理 - 可以直接表示为概念空间中的目的导航。我们提出了自主导航作为模拟认知的有效方法，从而吸收了现代神经科学的概念空间理论。但是，在高维欧几里德空间中实现自主导航并不是一件微不足道的。在这项工作中，我们探讨了Neorl导航是否适合该任务；通过Kaelbling对有效机器人导航的担忧，我们测试了Neorl方法是否在导航方式之间是一般的，在经验方面的构成，并且在多个欧几里得维度学习时有效。我们发现Neorl学习对生物学的学习比AI中的RL更相似，并建议将概念空间的Neorl导航作为模拟认知的合理新途径。
Edward C. Tolman found reinforcement learning unsatisfactory for explaining intelligence and proposed a clear distinction between learning and behavior. Tolman's ideas on latent learning and cognitive maps eventually led to what is now known as conceptual space, a geometric representation where concepts and ideas can form points or shapes.Active navigation between ideas - reasoning - can be expressed directly as purposive navigation in conceptual space. Assimilating the theory of conceptual space from modern neuroscience, we propose autonomous navigation as a valid approach for emulated cognition. However, achieving autonomous navigation in high-dimensional Euclidean spaces is not trivial in technology. In this work, we explore whether neoRL navigation is up for the task; adopting Kaelbling's concerns for efficient robot navigation, we test whether the neoRL approach is general across navigational modalities, compositional across considerations of experience, and effective when learning in multiple Euclidean dimensions. We find neoRL learning to be more resemblant of biological learning than of RL in AI, and propose neoRL navigation of conceptual space as a plausible new path toward emulated cognition.
