The future of cognitive strategy-enhanced persuasive dialogue agents: new perspectives and trends

Abstract

Persuasion, as one of the crucial abilities in human communication, has garnered extensive attention from researchers within the field of intelligent dialogue systems. Developing dialogue agents that can persuade others to accept certain standpoints is essential to achieving truly intelligent and anthropomorphic dialogue systems. 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 \mathbf{C}\mathbf{o}\mathbf{g}\mathbf{A}\mathbf{g}\mathbf{e}\mathbf{n}\mathbf{t}), 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.

Keywords

persuasive dialogue / cognitive strategy / cognitive psychology / persuasion strategy

Mengqi Chen received her master’s degree in digital textiles from Xi’an Polytechnical University (XPU), China in 2022. She is currently working toward a PhD degree at Northwestern Polytechnical University (NWPU), China. Her current research interests include natural language processing, dialog systems, and large language models

Bin Guo is a PhD professor and PhD supervisor at Northwestern Polytechnical University (NWPU), China. He is a senior member of the China Computer Federation. His main research interests include ubiquitous computing, social and community intelligence, urban big data mining, mobile crowdsensing, and human-computer interaction

Hao Wang received his BE degree in computer science and technology from Northwestern Polytechnical University (NWPU), China in 2019. He is currently working toward a PhD degree at NWPU. His current research interests include natural language processing, dialog systems, and large language models

Haoyu Li received his BE degree in computer science and technology from Northwestern Polytechnical University (NWPU), China in 2023. He is currently working toward a master’s degree at NWPU. His current research interests include natural language processing, large language models, and robot dynamic obstacle avoidance

Qian Zhao received her BE degree in Internet of Things engineering from Tianjin University of Technology (TUT), China in 2023. She is currently working toward a master’s degree at Northwestern Polytechnical University (NWPU), China. Her current research interests include multimodal dialogue, large language models, and visual human-computer interaction

Jingqi Liu entered Northwestern Polytechnical University(NWPU) to study for a bachelor’s degree in information and computing science in 2020. Her current research interests include natural language processing, dialogue systems, and large language models

Yasan Ding received his BE degree in computer science and technology from Northwestern Polytechnical University (NWPU), China in 2018. He is currently working toward a PhD degree at NWPU. His current research interests include fake news detection and natural language processing

Yan Pan is a lecturer at the Science and Technology on Information Systems Engineering Laboratory. He respectively received the BS degree in 2013 and the PhD degree in 2020 from Northwestern Polytechnical University (NWPU), China. His research interests include Big Data, Machine Learning, and Crowd Intelligence

Zhiwen Yu is a PhD professor and PhD supervisor. He is a senior member of the China Computer Federation. His main research interests include mobile internet, ubiquitous computing, social and community intelligence, urban big data mining, mobile crowdsensing, and human-computer interaction

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Acknowledgements

This work was partially supported by the National Science Fund for Distinguished Young Scholars (62025205) and the National Natural Science Foundation of China (Grant No. 62032020).

Competing interests

Bin Guo is an Editorial Board member of the journal and a co-author of this article. To minimize bias, he was excluded from all editorial decision-making related to the acceptance of this article for publication. The remaining authors declare no conflict of interest.

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