[论文解读] Morphognosis: the shape of knowledge in space and time

Thomas E. Portegys|ArXiv.org|Jan 5, 2017

Embodied and Extended Cognition参考文献 2被引用 3

一句话总结

本文提出了一种名为 morphognosis 的计算模型，将知识表征为一种事件记录的金字塔结构，该结构编码了空间和时间信息，使生物体能够模拟遥远的过去和未来事件。通过细胞自动机，该模型展示了模拟智能体如何通过内化扩展的时空知识来学习觅食、筑巢以及玩 Pong 游戏，表明更高阶智能源于对日益庞大的时空片段的处理。

ABSTRACT

Artificial intelligence research to a great degree focuses on the brain and behaviors that the brain generates. But the brain, an extremely complex structure resulting from millions of years of evolution, can be viewed as a solution to problems posed by an environment existing in space and time. The environment generates signals that produce sensory events within an organism. Building an internal spatial and temporal model of the environment allows an organism to navigate and manipulate the environment. Higher intelligence might be the ability to process information coming from a larger extent of space-time. In keeping with nature's penchant for extending rather than replacing, the purpose of the mammalian neocortex might then be to record events from distant reaches of space and time and render them, as though yet near and present, to the older, deeper brain whose instinctual roles have changed little over eons. Here this notion is embodied in a model called morphognosis (morpho = shape and gnosis = knowledge). Its basic structure is a pyramid of event recordings called a morphognostic. At the apex of the pyramid are the most recent and nearby events. Receding from the apex are less recent and possibly more distant events. A morphognostic can thus be viewed as a structure of progressively larger chunks of space-time knowledge. A set of morphognostics forms long-term memories that are learned by exposure to the environment. A cellular automaton is used as the platform to investigate the morphognosis model, using a simulated organism that learns to forage in its world for food, build a nest, and play the game of Pong.

研究动机与目标

• 探索更高阶智能是否可能源于对扩展空间和时间尺度信息的处理。
• 将新皮层建模为一个系统，能够记录并呈现遥远事件，使其如同当前存在，以模仿生物记忆整合机制。
• 研究事件记录的分层结构是否能够支持觅食、筑巢和游戏等复杂行为。
• 检验智能不仅关乎信号处理，更关乎构建时空内部模型的假设。
• 证明细胞自动机可通过结构化的时空事件编码支持学习与记忆。

提出的方法

• Morphognosis 模型采用一种称为 morphognostic 的金字塔状结构，其中顶端存储近期、邻近的事件，而更深层的层级则保存更早、更遥远的事件。
• 金字塔的每一层将事件数据聚合为越来越大的时空知识块。
• 细胞自动机作为计算基础，模拟与动态环境互动的智能体。
• 智能体通过接触环境刺激学习，将感官事件编码进 morphognostic 结构中。
• 该模型随时间整合感官输入，使系统能够模拟过去和未来状态，仿佛它们真实存在。
• 该系统在三项任务中进行了测试：觅食、筑巢以及玩 Pong 游戏。

实验结果

研究问题

• RQ1 分层的事件记录结构能否模拟大脑将遥远过去和未来事件表征为当前存在的能力？
• RQ2 处理日益庞大的时空片段如何促进人工智能体的智能行为？
• RQ3 细胞自动机平台能否通过 morphognostic 记忆支持复杂、目标导向行为的学习？
• RQ4 Morphognosis 模型在多大程度上复现了新皮层在记忆整合与规划中的功能？
• RQ5 金字塔结构如何实现对即时感官输入之外事件的模拟？

主要发现

• Morphognostic 结构成功地在不同空间和时间尺度上编码了事件序列，使智能体能够模拟过去和未来状态。
• 智能体通过检索并运用存储的时空知识，在觅食和筑巢任务中表现出适应性行为。
• 系统通过使用内化的事件序列来预测球的轨迹，成功实现了 Pong 游戏的玩法。
• 该模型表明，更高阶智能可能源于对扩展时空领域信息的处理与整合能力。
• 细胞自动机的实现证实，Morphognosis 支持学习与记忆形成，而无需依赖显式的神经架构。