E-NRPA: Emotion-Aware Nested Rollout Policy Adaptation on Large Language Models for Goal-Oriented Dialogue Planning

User emotion influences strategy selection in goal-oriented dialogue, but existing online planning methods typically do not explicitly model changes in emotion. This paper proposes Emotion-Aware Nested Rollout Policy Adaptation for Goal-Oriented Dialogue Planning (E-NRPA), which incorporates current emotion and historical emotion trajectories identified by large language models into Monte Carlo simulation and policy update processes, enabling the planner to adjust dialogue strategies based on changes in user state. We conduct experiments on ESConv, ExTES, CraigslistBargain, and P4G. Results show that E-NRPA achieves stable performance on automatic metrics, with advantages in task success rate and negotiation outcome quality. Human evaluations indicate that E-NRPA is preferred in negotiation tasks and receives better ratings for conciseness, goal progress, and overall efficiency in emotional support dialogues. The incorporation of emotion trajectories further enhances the planner’s sensitivity to changes in user state and improves its ability to adapt strategy selection dynamically. Compared with methods that rely only on the current dialogue state, E-NRPA can better capture how emotional evolution influences user goals and interaction intentions. These findings suggest that emotion-aware planning facilitates effective, goal-oriented dialogue strategy selection across different tasks.