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Front. Comput. Sci.    2025, Vol. 19 Issue (5) : 195315    https://doi.org/10.1007/s11704-024-40057-x
Artificial Intelligence
The future of cognitive strategy-enhanced persuasive dialogue agents: new perspectives and trends
Mengqi CHEN1, Bin GUO1(), Hao WANG1, Haoyu LI1, Qian ZHAO1, Jingqi LIU1, Yasan DING1, Yan PAN2, Zhiwen YU1
1. School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
2. Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha 410073, China
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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 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.
Keywords persuasive dialogue      cognitive strategy      cognitive psychology      persuasion strategy     
Corresponding Author(s): Bin GUO   
Just Accepted Date: 10 May 2024   Issue Date: 13 June 2024
 Cite this article:   
Mengqi CHEN,Bin GUO,Hao WANG, et al. The future of cognitive strategy-enhanced persuasive dialogue agents: new perspectives and trends[J]. Front. Comput. Sci., 2025, 19(5): 195315.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-024-40057-x
https://academic.hep.com.cn/fcs/EN/Y2025/V19/I5/195315
Fig.1  The persuasive dialogue example, where the dialogue agent persuades the user to relieve for job crisis using various persuasion strategies
Strategy Definition Example
Present of facts [8] Using factual evidence (e.g., official news reports, statistics) and a credible reasoning process to persuade others In recent months, the demand for residential properties has become extremely high. The price of residential property has risen almost twenty percent.
Challenges and inquiries [8] Expressing disbelief or opposition to the other side’s viewpoints and providing strong rebuttal evidence to enhance persuasiveness Really? I don’t agree. This Star Wars episode was incredible!
Emotional resonance [8] Eliciting specific emotions to influence others’ attitudes Advanced special effects are the main reason for the success of previous episodes, so audiences have high expectations for this one, and I don’t think they will be disappointed.
Eliciting anger [8] If that’s the case, there’s not much point in further discussion. We might as well call the whole deal off.
Eliciting guilt [8] Come on, you can at least try a little, besides your cigarette.
Self-modeling [8] Expressing one’s intention to act and opting to serve as a role model for the persuadee to emulate That still leaves a gap of 20 dollars to be covered. Let’s meet each other halfway once more, then the gap will be closed and our business completed.
Building trust [15] Building rapport and psychological trust through a harmonious conversation I’m glad we’ve agreed on price. We’ll go on to the other terms and conditions at our next meeting.
Courtesy tips [50] Expressing gratitude, approval, praise, etc. to lower the other party’s psychological defenses I know exactly what you mean. Hearing that song gives me a nostalgic feeling.
Compromise [51] Expressing concessions on time to avoid being too intense in the guidance process and causing the other party to end the conversation I think it unwise for either of us to insist on his price. How about meeting each other halfway so that business can be concluded?
Attachment of views [50] Expressing kindness and concern through active listening and to some extent seconding the other person’s point of view Better late than never.
Tab.1  Part of definitions and examples of persuasion strategies
Strategy Definition Example
Problem decomposition [52] Decomposing the ultimate persuasion goal into sub-issues and stepping through the persuasion process Let me get down some information about your apartment first. what is your property’s address?
Social identity [53] Gaining psychological support from the other person by emphasizing group and identity belonging I know. I have been a subscriber for the past two years.
Herd mentality [54] Presenting a viewpoint that is recognized or accepted by the majority of people and persuading the other side to accept it There was always a good round of applause every time she sang.
Expression of disgust [55] Expressing a particular point of view or emotion to emphasize the persuasive content Oh, my god! I look so old. I look as if I were 40. I think it’s time for some plastic surgeries.
Expression of empathy [38] I know, dear. I am too. But we’ve just been too busy to look for a house.
Expression of views [15] That means the apartment has furniture in it.
Logical appeal [8] Enhancing the credibility of persuasive content through the logical and reasoning process It certainly is. But to tell you the truth, the room is so large that I can share it with someone else, and that will decrease the total amount of the rent.
Task Inquiry [8] Asking questions related to persuasive goals That might be going overboard a bit. How about just that scarf with a bracelet?
Personal story [8] Using narrative examples to illustrate the positive outcomes of your actions to inspire others to follow suit Yes, I’m sure I’ve done a lot of house painting in my life. If I got even a tiny drop of paint on her furniture, she would get furious. So I learned to be very picky.
Refutation of objections [56] Directly refuting the other side’s point of view Not necessary. If we use a realtor to find a house, it will be more expensive.
Greeting [8] Greeting at the beginning of a dialogue Hi there! How are you doing today?
Tab.2  Part of definitions and examples of persuasion strategies
Fig.2  The generic system architecture of CogAgent
Solution Work Description Scenario
Strategy classifying based on dialogue context Wang et al. [8] Proposing a classifier to predict persuasion strategies in dialogue using context and sentence features. Social good
He et al. [57] Decoupling strategy selection and response generation in CogAgent for predicting strategy and generating responses based on dialogue history. Negotiation
Persuasion strategy planning Cheng et al. [30] Proposing lookahead heuristics to estimate future user feedback after using the specific strategy. Psychological counselling
Yu et al. [94] Using Monte Carlo Tree Search for persuasion strategy planning without model training. Social good
Graph-based strategy incorporation Joshi et al. [29] Using GNNs to model strategies, dialogue acts, and dependencies in graph structures for response generation. Negotiation
Zhou et al. [95] Modeling both dialogue context semantic and persuasion strategy history with finite state transducers. Negotiation and Social good
Knowledge-enhanced strategy modeling Jia et al. [96] Introducing a knowledge-enriched encoder and memory-enhanced strategy module for dynamic emotion and semantic pattern modeling. Psychological counselling
Chen et al. [38] Designing RAP for dynamic factual and persuasive responses based on knowledge and individual persuasion strategies. Social good
Novel integration mechanism Mishra et al. [71] Creating an RL reward function to enhance consistency in politeness strategy, persuasiveness, and emotion acknowledgment in persuasive dialogue. Social good
Tu et al. [97] Proposing a novel model MISC, which firstly infers the user’s fine-grained emotional status, and then responds skillfully using a mixture of strategies. Psychological counselling
Tab.3  Representative works of persuasion strategy-based CogAgent
Fig.3  The model architecture of DIALOGRAPH [29], which models the sequences of persuasion strategies and dialogue acts into graph structures for better modeling of strategy dependence and enriching dialogue context representations
Solution Work Description Scenario
Reinforcement learning based planning Xu et al. [109] Presenting KnowHRL, a three-layer Knowledge-aware hierarchical RL-based model for coherent topic path planning and multi-turn persuasive dialogue responses. Recommendation
Liu et al. [110] Hierarchical RL for conversation topic path planning, using high-level strategies and low-level responses. Social good
Lei et al. [111] Introducing four persuasion-related factors in the reward function to achieve persuasive goals efficiently. Recommendation
Graph-based planning Zhong et al. [32] Using commonsense knowledge graphs and GNNs to enhance semantic relations between topic keywords, improving keyword-augmented response retrieval. Recommendation
Zou et al. [112] Employing a concept graph for topic planning, utilizing an Insertion Transformer for persuasive response generation based on multi-concept paths. Social good
Wang et al. [113] Introducing a Transformer-based network for target-driven topic path planning with knowledge-target mutual attention and set-search decoding. Recommendation
Novel planning mechanism Tang et al. [61] Combining various planning algorithms for robust and smooth topic path planning, incorporating a sampling strategy, flow generator, and global planner. Recommendation
Ren et al. [114] Modelling a user’s preference in a finer granularity by identifying the most relevant relations from a structured knowledge graph. Recommendation
Tab.4  Representative works of topic path planning strategy-based CogAgent
Fig.4  The overall framework of GoChat [110], which utilizes hierarchical reinforcement learning (HRL) to concurrently plan high-level and low-level policy for efficiently achieving topic path planning
Fig.5  The overview framework of TPNet [33], which models the topic path planning process as a sequence generation task empowered by the designed target-driven conversation planner
Solution Work Description Scenario
Argument mining Khatib et al. [121] Classifying and structurally modeling arguments from online debate portals based on diverse vocabulary, grammar, and metric features. Debate
Hua et al. [122] Proposing an argument generation framework with retrieval modules and a sentence-level LSTM for generating viewpoints. Negotiation
Srivastava et al. [36] Using attention-based link prediction and Transformer encoder to model hierarchical causal relationships and discover associations in online argument structures. Negotiation
Niculae et al. [123] Introducing factor graph model for argument mining, concurrently learning fundamental unit types classification and argument relationship prediction. Negotiation
Argument structure prediction Rach et al. [65] Proposing argument search technique using supervised learning-based relation classification to retrieve arguments for debate dialogue system Debate
Sakai et al. [79] Introducing an approach to consider the human agreement and disagreement, resulting in a persuasive argument with a hierarchical argumentation structure. Debate
Prakken et al. [34] Enhancing argument modeling with a five-layer graph, serving as a knowledge base for a chatbot to identify user focal points and select rebuttal points. Debate
Li et al. [124] Using factor graphs to extract online debate features, incorporating them into an LSTM model to predict persuasive arguments. Debate
Tab.5  Representative works of argument structure prediction strategy-based cogAgent
Fig.6  The overall framework to investigate the influence of discourse structure on the persuasiveness of arguments extracted from online debates [124]
Scenario Dataset Description
Psychological counseling ESConv [49] The first dataset for psychological counseling, annotated with persuasion strategies.
AUGESC [130] The enhanced dataset from ESConv using LLMs with a broader range of topics.
PsyQA [131] A Chinese mental health support dataset featuring annotated persuasion strategies.
Debate IAC [132], IAC 2 [133] Argumentative dialog dataset with curated threads, posts, and annotations.
Winning arguments [134] A metadata-rich subset of r/ChangeMyView subreddit conversations includes data on the success of user utterances in persuading the poster.
Debate Sum [135] A dataset for the competitive formal debate with corresponding argument and extractive summaries.
Negotiation CraigslistBargain [57] A human-human dialogue dataset for price negotiation where the buyer and seller are encouraged to reach an agreement to get a better deal.
NegotiationCoach [136] An additional negotiation coach based on CraigslistBargain, which monitors the exchange between two annotators and provides real-time negotiation strategy.
Social good PersuasionForGood [8] A collection of online conversations where one participant (the persuader) tries to convince the other (the persuadee) to donate to a charity.
EPP4G and ETP4G [71] Datasets extending Persuasion For Good by annotating it with the emotion and politeness-strategy labels.
FaceAct [137] A dataset extending Persuasion For Good by adding the utterance-level annotations that change the positive and/or the negative face of the participants in a conversation.
Recommendation REDIAL [138] A dataset comprising recommendation conversations about movies.
TG-ReDial [11] A dataset consisting of dialogues between a seeker and a recommender.
DuRecDial [139] A human-to-human Chinese dialog dataset, which contains multiple sequential dialogues for every pair of a recommendation seeker and a recommender.
INSPIRED [140] A movie recommendation dataset, consisting of human-human dialogues with an annotation scheme for persuasion strategies.
Tab.6  A review of available datasets for CogAgent
Evaluation method Category Description Metrics
Automatic evaluation Overlap-based Measuring the degree of text overlap between generated responses and golden responses. BLEU [91], ROUGE [93], METEOR [141], CIDEr [142]
Embedding-based Evaluating the semantic similarity of embedding vectors between generated responses and reference ones. Greedy Matching [143], Embedding averaging [144], Vector Extreme [145]
Learning-based Employing machine learning models to predict the quality scores of generated responses, relying not only on given references. ADEM [146]
Human evaluation Scoring by human annotators to evaluate the quality of the generated responses with subjective judgment. Naturalness, Appropriateness, Informativeness, Diversity, Humanness
LLMs-based evaluation Scoring by utilizing one or multiple LLMs as expert evaluators for evaluating the performance of CogAgent. Engagingness, Persuasiveness, Strategy selection proficiency
Tab.7  Evaluation metrics for CogAgent
Models PPL↓ B-1↑ B-2↑ B-3↑ B-4↑ R-L↑ MET↑ CIDEr↑
DialoGPT-Joint [49] 15.71 17.39 5.59 2.03 1.18 16.93 7.55 11.86
BlenderBot-Joint [49] 16.79 17.62 6.91 2.81 1.66 17.94 7.54 18.04
MISC [97] 16.16 − 7.31 − 2.20 17.91 11.05 −
TransESC [165] 15.85 17.92 7.64 4.01 2.43 17.51 − −
GLHG [166] 15.67 19.66 7.57 3.74 2.13 16.37 − −
FADO [167] 15.72 − 8.00 4.00 2.32 17.53 − −
KEMI [168] 15.92 − 8.31 − 2.51 17.05 − −
MultiESC [30] 15.41 21.65 9.18 4.99 3.09 20.41 8.84 29.98
MODERN [96] 14.99 23.19 10.13 5.53 3.39 20.86 9.26 30.08
Tab.8  Performance of various CogAgent models on the ESConv dataset. The best results are highlighted in bold
Methods ReDial INSPIRED
H@1↑ H@10↑ H@50↑ Dist-3↑ Dist-4↑ HitX↑ H@1↑ H@10↑ H@50↑ Dist-3↑ Dist-4↑ HitX↑
KGSF [174] 3.55 18.04 37.89 0.40 0.50 2.40 6.05 15.88 27.75 2.40 3.20 11.40
CR-Walker [177] 3.70 17.60 37.10 3.00 3.70 3.20 − − − − − −
RevCore [178] 4.05 21.21 41.02 0.90 1.50 8.30 7.20 16.53 33.47 6.00 6.80 30.70
C2-CRS [179] 4.32 21.50 41.20 1.70 1.90 2.90 6.38 21.28 36.60 7.50 9.00 6.60
UniCRS [176] 5.03 21.82 41.62 0.70 0.90 0.70 9.01 25.00 41.23 5.50 6.50 0.10
LATTE [180] 5.61 23.93 44.01 5.10 6.00 97.00 9.79 27.23 44.26 9.50 10.10 95.40
Tab.9  Performance of various CogAgent models on the ReDial and INSPIRED benchmarks. The best results are highlighted in bold
  
  
  
  
  
  
  
  
  
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