规划前模拟：构建内在用户世界模型以实现用户定制化对话策略规划
Simulating Before Planning: Constructing Intrinsic User World Model for User-Tailored Dialogue Policy Planning
AI总结：
针对现有对话策略规划忽视用户特征的问题，提出整合内在用户世界模型的UDP框架，通过三阶段建模和主动学习优化用户定制化策略，实验验证其鲁棒性与适应性。
摘要：
对话策略规划领域的最新进展强调优化系统智能体策略以实现预定义目标，重点关注策略设计、轨迹获取和高效训练范式。然而，这些方法往往忽视用户特征的关键作用，而在对话搜索和推荐等现实场景中，交互必须适应个性、偏好和目标等个体用户特质，用户特征至关重要。为填补这一空白，我们首先利用任务特定用户角色开展全面研究，系统评估不同用户行为下的对话策略规划。通过为不同任务利用真实用户档案，我们的研究揭示现有方法存在显著局限性，凸显用户定制化对话策略规划的必要性。基于此，我们提出用户定制化对话策略规划（UDP）框架，该框架整合内在用户世界模型以建模用户特质和反馈。UDP分为三个阶段：（1）用户角色描绘，使用扩散模型动态推断用户档案；（2）用户反馈预测，利用受布朗桥启发的预测器预测用户反应；（3）用户定制化策略规划，整合这些见解以优化响应策略。为确保鲁棒性能，我们进一步提出主动学习方法，在训练期间优先考虑具有挑战性的用户角色。在协作和非协作设置等基准上的综合实验表明，UDP在学习用户特定对话策略方面有效。结果验证了该协议的实用性，并凸显UDP的鲁棒性、适应性及其推动以用户为中心的对话系统发展的潜力。
英文摘要：
Recent advancements in dialogue policy planning have emphasized optimizing system agent policies to achieve predefined goals, focusing on strategy design, trajectory acquisition, and efficient training paradigms. However, these approaches often overlook the critical role of user characteristics, which are essential in real-world scenarios like conversational search and recommendation, where interactions must adapt to individual user traits such as personality, preferences, and goals. To address this gap, we first conduct a comprehensive study utilizing task-specific user personas to systematically assess dialogue policy planning under diverse user behaviors. By leveraging realistic user profiles for different tasks, our study reveals significant limitations in existing approaches, highlighting the need for user-tailored dialogue policy planning. Building on this foundation, we present the User-Tailored Dialogue Policy Planning (UDP) framework, which incorporates an Intrinsic User World Model to model user traits and feedback. UDP operates in three stages: (1) User Persona Portraying, using a diffusion model to dynamically infer user profiles; (2) User Feedback Anticipating, leveraging a Brownian Bridge-inspired anticipator to predict user reactions; and (3) User-Tailored Policy Planning, integrating these insights to optimize response strategies. To ensure robust performance, we further propose an active learning approach that prioritizes challenging user personas during training. Comprehensive experiments on benchmarks, including collaborative and non-collaborative settings, demonstrate the effectiveness of UDP in learning user-specific dialogue strategies. Results validate the protocol's utility and highlight UDP's robustness, adaptability, and potential to advance user-centric dialogue systems.
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。
