  Controllability and proactivity are crucial properties of autonomous
conversational agents (CAs). Controllability requires the CAs to follow the
standard operating procedures (SOPs), such as verifying identity before
activating credit cards. Proactivity requires the CAs to guide the conversation
towards the goal during user uncooperation, such as persuasive dialogue.
Existing research cannot be unified with controllability, proactivity, and low
manual annotation. To bridge this gap, we propose a new framework for
planning-based conversational agents (PCA) powered by large language models
(LLMs), which only requires humans to define tasks and goals for the LLMs.
Before conversation, LLM plans the core and necessary SOP for dialogue offline.
During the conversation, LLM plans the best action path online referring to the
SOP, and generates responses to achieve process controllability. Subsequently,
we propose a semi-automatic dialogue data creation framework and curate a
high-quality dialogue dataset (PCA-D). Meanwhile, we develop multiple variants
and evaluation metrics for PCA, e.g., planning with Monte Carlo Tree Search
(PCA-M), which searches for the optimal dialogue action while satisfying SOP
constraints and achieving the proactive of the dialogue. Experiment results
show that LLMs finetuned on PCA-D can significantly improve the performance and
generalize to unseen domains. PCA-M outperforms other CoT and ToT baselines in
terms of conversation controllability, proactivity, task success rate, and
overall logical coherence, and is applicable in industry dialogue scenarios.
The dataset and codes are available at XXXX.
