发表机构
Technische Universität Darmstadt; Honda Research Institute Europe GmbH（达姆施塔特工业大学; 本田欧洲研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
借鉴认知科学，提出极简机器框架，用Prolog子集通过课程式教学从空知识库逐步习得算术递归程序，产生透明归纳推理轨迹。
AI 中文摘要
人类往往能从极少的经验中获取并综合出复杂、递归的概念。借鉴认知科学的洞见，我们提出了极简机器（Minimalist Machine），一个用于归纳程序合成的框架，旨在模拟此类概念学习。该系统使用Prolog的一个紧凑关系子集：程序在由无体事实和两体合取Horn子句组成的固定模式内进行搜索。递归并非由专门的元规则定义，而是当目标谓词在学习到的子句体内被重用时涌现。受小学课程启发，该模型通过人工策划的、按顺序引入的算术新概念进行教学。从最初为空的知识库出发，它首先习得简单的结构谓词，然后是基于后继者的状态转换，最后是加法、减法、乘法和除法的递归程序。最终，这种方法产生了完全透明、归纳推理的轨迹，这对于类似人类的概念学习是必要的。
英文摘要
Humans can often acquire and synthesize complex, recursive concepts from minimal experience. Leveraging cognitive insights, we propose the Minimalist Machine, a framework for inductive program synthesis designed to model such conceptual learning. The system uses a compact relational subset of Prolog: Programs are searched within a fixed schema of body-free facts and two-body conjunctive Horn clauses. Recursion is not defined by a dedicated metarule. Instead, it emerges when a target predicate is reused inside the body of a learned clause. Inspired by a primary school curriculum, the model is taught through a human-curated, sequential introduction of new concepts in arithmetic. Starting from initially empty knowledge base, it first acquires simple structural predicates, then successor-based state transformations, and finally recursive programs for addition, subtraction, multiplication, and division. Ultimately, this approach yields the fully transparent, inductive reasoning trace necessary for human-like conceptual learning.
Comments15 pages, 5 figures. Accepted for oral presentation at the 6th International Joint Conference on Learning and Reasoning 2026. Code: https://github.com/cognitive-modeling/minimalist-machine/tree/IJCLR-2026
