让智能体与人协作时能多步规划意图，提升复杂任务表现。
Human-Agent Coordination in Games under Incomplete Information via Multi-Step Intent
- 引入多步意图机制，允许每回合执行多个动作
- 在夜间侏儒测试中减少18.52%失败率，控制切换更少
- 适合需要长期协作的交互场景，如人机协同游戏
在不完全信息条件下，自主智能体与人类伙伴的战略协作可建模为回合制合作博弈。本文扩展了原有的共享控制博弈，允许玩家每回合执行多个动作，从而支持多步意图机制。我们提出一种结合记忆模块与在线规划算法IntentMCTS的方法，通过动态更新环境信念并利用通信中的多步意图进行奖励增强，以优化行动选择。在Gnomes at Night测试平台的智能体间仿真显示，IntentMCTS相比基线方法减少了步骤数与控制切换次数。人类用户研究进一步验证：相比启发式基线，成功率提升18.52%；相较于单步意图方法，提升5.56%。参与者报告认知负荷更低、挫败感更少，并对合作体验满意度更高。
原文摘要 · Abstract (English)
Strategic coordination between autonomous agents and human partners under incomplete information can be modeled as turn-based cooperative games. We extend a turn-based game under incomplete information, the shared-control game, to allow players to take multiple actions per turn rather than a single action. The extension enables the use of multi-step intent, which we hypothesize will improve performance in long-horizon tasks. To synthesize cooperative policies for the agent in this extended game, we propose an approach featuring a memory module for a running probabilistic belief of the environment dynamics and an online planning algorithm called IntentMCTS. This algorithm strategically selects the next action by leveraging any communicated multi-step intent via reward augmentation while considering the current belief. Agent-to-agent simulations in the Gnomes at Night testbed demonstrate that IntentMCTS requires fewer steps and control switches than baseline methods. A human-agent user study corroborates these findings, showing an 18.52% higher success rate compared to the heuristic baseline and a 5.56% improvement over the single-step prior work. Participants also report lower cognitive load, frustration, and higher satisfaction with the IntentMCTS agent partner.
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