用于图着色的神经符号快与慢架构
A Neurosymbolic Fast and Slow Architecture for Graph Coloring
AI总结：
本文提出神经符号双系统架构SOFAI_v2，通过元认知协调LLM快速求解与符号审慎求解，在图着色任务上显著提升成功率和速度。
摘要：
约束满足问题（CSPs）因其错综复杂的约束以及对精确解的需求，给人工智能带来了重大挑战。现有的符号求解器通常速度较慢，而且先前研究表明，仅凭大型语言模型（LLMs）难以应对CSPs，原因在于其复杂性。为弥合这一差距，我们在现有SOFAI架构（SOFAI_v1）的基础上进行构建，该架构将Daniel Kahneman的《思考，快与慢》认知模型适配到人工智能中。我们的增强架构SOFAI_v2集成了经过改进的元认知治理机制，以提升在复杂领域中的适应性，此处专门针对求解图着色问题（一种特定类型的CSP）进行定制。SOFAI_v2将利用LLMs的快速系统1（S1）与由元认知模块治理的审慎系统2（S2）相结合。S1的初始解往往受限于约束遵守问题，通过来自元认知的有针对性反馈和示例得到改进，使S1更紧密地符合CSP要求。如果S1未能解决问题，元认知会策略性地调用S2，从而确保准确可靠的解。我们的实证结果表明，在求解图着色问题时，SOFAI_v2相比传统符号求解器实现了10.5%更高的成功率，并且速度最高提升30%。
英文摘要：
Constraint Satisfaction Problems (CSPs) present significant challenges to artificial intelligence due to their intricate constraints and the necessity for precise solutions. Existing symbolic solvers are often slow, and prior research has shown that Large Language Models (LLMs) alone struggle with CSPs because of their complexity. To bridge this gap, we build upon the existing SOFAI architecture (SOFAI_v1), which adapts Daniel Kahneman's ''Thinking, Fast and Slow'' cognitive model to AI. Our enhanced architecture, SOFAI_v2, integrates refined metacognitive governance mechanisms to improve adaptability across complex domains, specifically tailored here for solving the graph coloring problem, a specific type of CSP. SOFAI_v2 combines a fast System 1 (S1), leveraging LLMs, with a deliberative System 2 (S2), governed by a metacognition module. S1's initial solutions, often limited by constraint adherence issues, are improved through targeted feedback and examples from metacognition, aligning S1 more closely with CSP requirements. If S1 fails to resolve the problem, metacognition strategically invokes S2, ensuring accurate and reliable solutions. Our empirical results demonstrate that SOFAI_v2 achieves a 10.5% higher success rate and is up to 30% faster than a traditional symbolic solver in solving graph coloring problems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
