用投票机制协调多个教学智能体，提升辅导系统协作效果。
Voting Protocols as Coordination Mechanisms for Role-Constrained Multi-Agent Tutoring Systems
- 设计四种投票协议协调四类角色教学智能体。
- 不同投票规则导致显著不同的协作模式和学习提升。
- 适合研究多智能体协作与教育AI的开发者与学者。
代理式辅导系统带来协调挑战：多个智能体可能提出不同但合理的干预措施，却只能输出一个响应。本文研究投票协议如何影响四个角色受限的教学智能体（支架、误解处理、动机激励、元认知）间的协作。在SciQ和HumanEval两个模拟辅导环境中，对比了简单投票、排序投票、累计投票和同意投票四种协议。实验基于1200次模拟交互，发现智能体讨论与投票协议类型会频繁改变最终采纳的响应，表明二者均深刻影响集体决策。不同投票规则产生各异的协作行为，即使短时辅导也能观察到学生学习成效的明显提升。结果表明，协议选择与角色专业化教学智能体间的协调模式密切相关。
原文摘要 · Abstract (English)
Agentic tutoring systems introduce a coordination challenge: multiple agents may propose different but reasonable interventions, yet only one response can be delivered to the learner. In this paper, we study how voting protocols shape cooperation among four role-constrained pedagogical agents responsible for scaffolding, misconception, motivation, and metacognition. We compare four voting protocols -- simple, ranked, cumulative, and approval voting -- across two simulated tutoring environments on SciQ and HumanEval benchmarks. Rather than using voting as a simple aggregation step, we use it to analyze how collective decision rules shape coordination under partial pedagogical conflict. Across 1,200 simulated interactions, we find that agent deliberation and voting protocol type frequently change which response ultimately wins, showing that both meaningfully shape the collective decision. Different voting rules also produce distinct coordination behaviors, and even brief tutoring turns show measurable learning gains in simulated students. Overall, we show that protocol choice is associated with distinct coordination patterns among role-specialized pedagogical agents.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。