认知策略增强的说服性对话智能体的未来：新视角与趋势
The Future of Cognitive Strategy-enhanced Persuasive Dialogue Agents: New Perspectives and Trends
AI总结：
本文综述认知策略增强的说服性对话智能体（CogAgent），提出融合说服、话题路径规划和论证结构预测三种策略的系统架构，并总结基准与未来方向。
摘要：
说服作为人类交流中的关键能力之一，已引起智能对话系统领域研究者的广泛关注。我们人类倾向于通过对话在各种场景中说服他人改变观点、态度或行为（例如，为社会公益进行说服、在在线平台上辩论）。开发能够说服他人接受特定立场的对话智能体，对于实现真正智能且拟人化的对话系统至关重要。得益于大型语言模型（LLMs）的实质性进展，对话智能体在上下文理解和响应生成方面获得了卓越的能力。然而，作为一个典型且复杂的认知心理系统，说服性对话智能体还需要认知心理学领域的知识以达到类人的说服水平。因此，认知策略增强的说服性对话智能体（定义为CogAgent），即通过结合认知策略在对话中实现说服目标，已成为主导的研究范式。为了描绘CogAgent的研究趋势，本文首先介绍了若干基础认知心理学理论，并给出了三种典型认知策略的形式化定义，包括说服策略、话题路径规划策略和论证结构预测策略。然后，我们提出了一种结合形式化定义的新系统架构，为CogAgent奠定基础。根据组合的认知策略，对代表性工作进行了详细研究和考察，随后总结了权威基准和评估指标。最后，我们总结了关于CogAgent开放问题和未来方向的见解，以供后续研究者参考。
英文摘要：
Persuasion, as one of the crucial abilities in human communication, has garnered extensive attention from researchers within the field of intelligent dialogue systems. We humans tend to persuade others to change their viewpoints, attitudes or behaviors through conversations in various scenarios (e.g., persuasion for social good, arguing in online platforms). Developing dialogue agents that can persuade others to accept certain standpoints is essential to achieving truly intelligent and anthropomorphic dialogue system. Benefiting from the substantial progress of Large Language Models (LLMs), dialogue agents have acquired an exceptional capability in context understanding and response generation. However, as a typical and complicated cognitive psychological system, persuasive dialogue agents also require knowledge from the domain of cognitive psychology to attain a level of human-like persuasion. Consequently, the cognitive strategy-enhanced persuasive dialogue agent (defined as CogAgent), which incorporates cognitive strategies to achieve persuasive targets through conversation, has become a predominant research paradigm. To depict the research trends of CogAgent, in this paper, we first present several fundamental cognitive psychology theories and give the formalized definition of three typical cognitive strategies, including the persuasion strategy, the topic path planning strategy, and the argument structure prediction strategy. Then we propose a new system architecture by incorporating the formalized definition to lay the foundation of CogAgent. Representative works are detailed and investigated according to the combined cognitive strategy, followed by the summary of authoritative benchmarks and evaluation metrics. Finally, we summarize our insights on open issues and future directions of CogAgent for upcoming researchers.
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。
