提出大脑自我的数学模型，让智能体学会区分自我与环境。
Marti-5: A Mathematical Model of "Self in the World" as a First Step Toward Self-Awareness
- 用生物启发的神经结构建模自我与环境的分离机制。
- 在Pong和Breakout游戏中实现自主学习并成功通关。
- 适合研究意识起源、认知科学与类脑智能的学者。
三十年前提出的大脑'是什么'和'在哪里'信息处理路径，至今缺乏清晰的数学模型来阐明二者协同机制。本文提出一种生物启发的数学模型，通过新皮层柱状结构在基底节调控下，区分自我与环境，并构建自我模型以提升预测能力。模型中部分柱状单元作为'是什么'处理者，另一些作为'在哪里'处理者。基于此，我们构建了一个强化学习智能体，在虚拟环境中的Pong和Breakout游戏中成功学习到目标行为。结果表明，将自我从环境分离的能力为智能体带来优势，因此该机制可能在进化中出现。我们提出自知原则1：能分离自我与世界是自知的必要但非充分条件。
原文摘要 · Abstract (English)
The existence of 'what' and 'where' pathways of information processing in the brain was proposed almost 30 years ago, but there is still a lack of a clear mathematical model that could show how these pathways work together. We propose a biologically inspired mathematical model that uses this idea to identify and separate the self from the environment and then build and use a self-model for better predictions. This is a model of neocortical columns governed by the basal ganglia to make predictions and choose the next action, where some columns act as 'what' columns and others act as 'where' columns. Based on this model, we present a reinforcement learning agent that learns purposeful behavior in a virtual environment. We evaluate the agent on the Atari games Pong and Breakout, where it successfully learns to play. We conclude that the ability to separate the self from the environment gives advantages to the agent and therefore such a model could appear in living organisms during evolution. We propose Self-Awareness Principle 1: the ability to separate the self from the world is a necessary but insufficient condition for self-awareness.
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