Rule extraction is a central problem in interpretable machine learning because it seeks to convert opaque predictive behavior into human-readable symbolic structure. This paper presents Chat Incremental Pattern Constructor (ChatIPC), a lightweight incremental symbolic learning system that extracts ordered token-transition rules from text, enriches them with definition-based expansion, and constructs responses by similarity-guided candidate selection. The system may be viewed as a rule extractor operating over a token graph rather than a conventional classifier. I formalize the knowledge base, definition expansion, candidate scoring, repetition control, English-rule heuristics, and response construction mechanisms used by ChatIPC. I further situate the method within the literature on rule extraction, decision tree induction, association rules, interpretable machine learning, and sequence construction. The updated C++ code implementation of ChatIPC is also reviewed in detail: it parses an embedded dictionary, normalizes lexical keys, caches definition tokens and part-of-speech tags, computes Jaccard scores on bitsets, applies heuristic linguistic bonuses, and persists the knowledge base with a versioned binary format. The paper emphasizes mathematical formulation and algorithmic clarity, and it provides pseudocode for the learning, scoring, and construction algorithms.

翻译：规则提取是可解释机器学习中的核心问题，因其致力于将黑箱式预测行为转化为人类可读的符号化结构。本文提出聊天增量模式构建器（ChatIPC），这是一种轻量级增量符号学习系统，可从文本中提取有序的令牌转移规则，通过基于定义的扩展对其进行丰富，并通过相似性引导的候选选择构建响应。该系统可被视为一种作用于令牌图而非传统分类器的规则提取器。本文形式化定义了ChatIPC所使用的知识库、定义扩展、候选评分、重复控制、英语规则启发式方法以及响应构建机制。进一步将本方法置于规则提取、决策树归纳、关联规则、可解释机器学习及序列构建等领域的文献中展开论述。此外，本文详细回顾了ChatIPC的更新版C++代码实现：解析嵌入式词典、规范化词汇键、缓存定义令牌及词性标注、计算位集上的Jaccard分数、应用启发式语言奖励，并通过带版本控制的二进制格式持久化知识库。本文强调数学形式化与算法清晰性，并为学习、评分及构建算法提供了伪代码。