  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.
