NL2GenSym: Natural Language to Generative Symbolic Rules for SOAR Cognitive Architecture via Large Language Models
College of Systems Engineering, National University of Defense Technology（系统工程学院，国防科技大学）；State Key Laboratory of Digital Intelligent Modeling and Simulation（数字智能建模与仿真国家重点实验室）；Test Center, National University of Defense Technology（测试中心，国防科技大学）
SOAR是一种经典的基于符号的认知架构，一直致力于促进通用且类人智能体的发展。然而，其手动编写规则的过程十分费力，阻碍了实际应用。新兴的大语言模型（LLMs）在高效生成规则方面展现出巨大潜力。然而，目前存在一个关键空白：当前研究主要集中于概念框架，且缺乏稳健的实验验证。为了填补这一空白，我们提出了自然语言到生成式符号规则（NL2GenSym），这是一种将LLMs与SOAR集成以从自然语言中自主生成生成式符号规则的新颖框架。具体而言，该框架引入了一种新颖的基于执行落地的生成器-评论家机制。基于LLM的生成器在访问检索增强生成（RAG）的自演化领域知识库的引导下，从自然语言中提出规则。随后，这些规则在SOAR环境中立即执行，以严格验证其正确性。基于这种基于执行落地的反馈，具有反思能力的基于LLM的评论器驱动这些规则的迭代优化。在我们专用的Water Jug Problem（WJP）数据集上进行的实验利用了Gemini和Qwen系列模型，验证了我们框架的有效性。它在从自然语言生成规则方面实现了超过86%的成功率。至关重要的是，该框架还生成了新颖的启发式规则，将解决WJP的平均决策周期减少至最优解的1.98倍以及基线方法的1/1000。此外，我们的初步实验表明，NL2GenSym使较小参数模型能够实现比较大规模同类模型更好的性能。
英文摘要：
SOAR, a classic symbol-based cognitive architecture, has been fostering the development of general, human-like intelligent agents. Nevertheless, its practical adoption is hindered by the laborious manual rule coding. Emerging Large Language Models (LLMs) present the immense potential for efficient rules generation. However, there is a critical gap that current research predominantly focuses on conceptual frameworks and lacks robust experimental validation. To bridge this gap, we propose \textit{N}atural \textit{L}anguage to \textit{Gen}erative \textit{Sym}bolic Rules (NL2GenSym), a novel framework that integrates LLMs with SOAR to autonomously produce generative symbolic rules from natural language. Specifically, our framework introduces a novel Execution-Grounded Generator-Critic mechanism. The LLM-based Generator, guided by a Retrieval-Augmented Generation-accessed self-evolving domain knowledge base, proposes rules from natural language. Subsequently, these rules are immediately executed within the SOAR environment to rigorously validate their correctness. Based on this execution-grounded feedback, a reflective LLM-based Critic drives the iterative refinement of these rules. Experiments on our specialized Water Jug Problem (WJP) dataset, utilizing both Gemini and Qwen series models, validate the efficacy of our framework. It achieves a success rate over 86\% in generating rules from natural language. Crucially, the framework also generates novel heuristic rules, reducing average decision cycles for solving the WJP to 1.98 times the optimal solution and 1/1000 of baseline methods. Additionally, our initial experiments show that NL2GenSym enables smaller-parameter models to achieve better performance than larger counterparts.
Graph Reasoning Paradigm: Structured and Symbolic Reasoning with Topology-Aware Reinforcement Learning for Large Language Models
Harbin Institute of Technology（哈尔滨工业大学）；State Key Laboratory of Cognitive Intelligence（认知智能国家重点实验室）；Tianjin Normal University（天津师范大学）；Pengcheng Laboratory（鹏城实验室）
Rate-Coding Bundle Memory: A Unified Model of Memory and Control for Symbolic Computation in the Brain
Max Planck Institute for Psycholinguistics（马克斯·普朗克心理语言学研究所）；Donders Institute for Brain, Cognition and Behaviour（唐德斯大脑、认知与行为研究所）；Radboud University（拉德堡德大学）
