用动态知识感知的强化学习，让多个智能体协作更聪明、更省资源。
KABB: Knowledge-Aware Bayesian Bandits for Dynamic Expert Coordination in Multi-Agent Systems
- 构建三维语义距离模型，理解智能体间知识差异
- 双适应机制持续优化专家能力，响应环境变化
- 基于知识的汤普森采样策略，高效选择最优协作方案
随着大语言模型规模扩大带来高昂成本，多智能体系统成为有前景的替代方案，但面临知识静态假设和协作效率低下的挑战。本文提出知识感知贝叶斯博弈（KABB）框架，通过语义理解与动态适应提升多智能体协作效率。该框架包含三项核心创新：用于深层语义理解的三维知识距离模型、支持持续专家优化的双适应机制，以及实现高效专家选择的知识感知汤普森采样策略。大量实验表明，KABB在多智能体协作中实现了成本与性能的最优平衡，在保持高性能的同时显著降低计算开销。
原文摘要 · Abstract (English)
As scaling large language models faces prohibitive costs, multi-agent systems emerge as a promising alternative, though challenged by static knowledge assumptions and coordination inefficiencies. We introduces Knowledge-Aware Bayesian Bandits (KABB), a novel framework that enhances multi-agent system coordination through semantic understanding and dynamic adaptation. The framework features three key innovations: a three-dimensional knowledge distance model for deep semantic understanding, a dual-adaptation mechanism for continuous expert optimization, and a knowledge-aware Thompson Sampling strategy for efficient expert selection. Extensive evaluation demonstrates KABB achieves an optimal cost-performance balance, maintaining high performance while keeping computational demands relatively low in multi-agent coordination.
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