用离散预测编码构建化学式般的规则系统，实现目标导向的通用智能。
ActPC-Chem: Discrete Active Predictive Coding for Goal-Guided Algorithmic Chemistry as a Potential Cognitive Kernel for Hyperon & PRIMUS-Based AGI
- 基于规则重写机制的离散主动预测编码，模拟认知动态演化
- 系统能自主组织规则应对延迟奖励任务，融合因果推理与概率逻辑
- 适合研究通用人工智能底层架构的学者，尤其关注认知模型融合
我们提出一种新型范式 ActPC-Chem，基于离散主动预测编码（ActPC）在重写规则构成的算法化学体系中实现生物启发的目标导向人工智能。该系统被视为高级认知架构（如 OpenCog Hyperon）的基础‘认知内核’，整合了 PRIMUS 认知架构的核心要素。核心观点是：当数据与模型均以元图重写规则的演化模式表示时，通过预测误差、内外部奖励及语义约束持续重组和优化规则，可涌现出具备通用智能的认知结构与动态。通过虚拟‘机器人虫’思想实验，展示了系统如何自组织应对涉及延迟与上下文依赖奖励的复杂任务，结合因果规则推断（AIRIS）与概率逻辑抽象（PLN）发现并利用概念模式与因果约束。进一步描述了连续预测编码神经网络与离散 ActPC 基底的协同融合方式。最后，展望构建类 Transformer 架构的可能性——摒弃传统反向传播，改用由 ActPC 指导的规则变换。这一分层架构结合 AIRIS 与 PLN，有望实现结构化、多模态且逻辑一致的下一项预测与叙事生成。
原文摘要 · Abstract (English)
We explore a novel paradigm (labeled ActPC-Chem) for biologically inspired, goal-guided artificial intelligence (AI) centered on a form of Discrete Active Predictive Coding (ActPC) operating within an algorithmic chemistry of rewrite rules. ActPC-Chem is envisioned as a foundational "cognitive kernel" for advanced cognitive architectures, such as the OpenCog Hyperon system, incorporating essential elements of the PRIMUS cognitive architecture. The central thesis is that general-intelligence-capable cognitive structures and dynamics can emerge in a system where both data and models are represented as evolving patterns of metagraph rewrite rules, and where prediction errors, intrinsic and extrinsic rewards, and semantic constraints guide the continual reorganization and refinement of these rules. Using a virtual "robot bug" thought experiment, we illustrate how such a system might self-organize to handle challenging tasks involving delayed and context-dependent rewards, integrating causal rule inference (AIRIS) and probabilistic logical abstraction (PLN) to discover and exploit conceptual patterns and causal constraints. Next, we describe how continuous predictive coding neural networks, which excel at handling noisy sensory data and motor control signals, can be coherently merged with the discrete ActPC substrate. Finally, we outline how these ideas might be extended to create a transformer-like architecture that foregoes traditional backpropagation in favor of rule-based transformations guided by ActPC. This layered architecture, supplemented with AIRIS and PLN, promises structured, multi-modal, and logically consistent next-token predictions and narrative sequences.
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