投票协议作为角色约束的多智能体辅导系统的协调机制
Voting Protocols as Coordination Mechanisms for Role-Constrained Multi-Agent Tutoring Systems
AI总结：
研究投票协议如何塑造四个角色约束的教学智能体之间的协调，通过比较四种投票协议在模拟辅导环境中的效果，发现协议选择显著影响集体决策和协调行为。
摘要：
智能辅导系统引入了一个协调挑战：多个智能体可能提出不同但合理的干预措施，但只能向学习者提供一个响应。在本文中，我们研究了投票协议如何塑造四个角色约束的教学智能体之间的合作，这些智能体负责搭建脚手架、误解、动机和元认知。我们在SciQ和HumanEval基准测试的两个模拟辅导环境中比较了四种投票协议——简单投票、排名投票、累积投票和批准投票。我们不是将投票用作简单的聚合步骤，而是用它来分析在部分教学冲突下集体决策规则如何塑造协调。在1200次模拟交互中，我们发现智能体 deliberation 和投票协议类型经常改变最终获胜的响应，表明两者都显著影响集体决策。不同的投票规则也产生不同的协调行为，即使是短暂的辅导回合也在模拟学生中显示出可测量的学习收益。总体而言，我们表明协议选择与角色专门化的教学智能体之间的不同协调模式相关。
英文摘要：
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.
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。
