用多智能体强化学习让AI自发产生语言，探索意识的可能路径
Emergent Language as an Approach to Conscious AI
- 从零开始训练智能体，在任务压力下自发形成语言通信
- 在简单环境中发现自我指涉的语言结构和错配检测机制
- 适合对意识、自组织系统、生成式智能体感兴趣的读者
人工系统是否具备意识仍无定论，现有方法或依赖理论检查清单（判别式），或直接构建类意识模块（架构式），均难以排除人类语言先验的影响。本文提出一种生成式方法：在多智能体强化学习中构建涌现语言（EL），智能体初始状态极简（无语言、无自我概念、极少接触人类文本），仅在任务压力下发展通信，确保行为可归因于任务需求而非人类语言先验。我们阐述了该方法作为研究意识相关结构的生成工具，包括环境复杂性的作用与涌现语言的解释。作为概念验证，我们在最小化环境中实现该方法，发现智能体发展出自我指涉的通信，包括一种未被任务结构或架构预测的回声-错配检测电路，其源于特定环境可及性。
原文摘要 · Abstract (English)
The question of whether artificial systems can be conscious remains open, in part because existing approaches either evaluate systems against theory-derived checklists (discriminative) or engineer consciousness-inspired modules directly (architectural); both leave open whether observed structures are artifacts of human language priors. We propose a generative methodology: emergent language (EL) in multi-agent reinforcement learning, where agents start from minimal (no language, no concept of self, minimal exposure to human text) and develop communication under task pressure alone, ensuring causal attributability to task demands rather than inherited human language priors. We position our methodology by discussing how EL serves as a generative tool for studying consciousness-relevant structure, including the role of environment complexity and the interpretation of emergent communication. As a proof of concept, we instantiate this methodology in a minimal environment and show that agents develop self-referential communication, including an echo-mismatch detection circuit that is not predicted by task structure or architecture alone but emerges from a specific environmental affordance.
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