Uncertainty as a Planning Signal: Multi-Turn Decision Making for Goal-Oriented Conversation

2026-04-04

AI Summary

This work addresses the challenge in goal-oriented dialogue systems of simultaneously optimizing information gathering and goal confirmation over multi-turn interactions. Existing approaches often suffer from either insufficient flexibility due to rigid predefined structures or limited long-horizon decision-making capabilities when relying solely on large language models. To overcome this trade-off, the paper proposes the CUP framework, which explicitly models uncertainty as a guiding signal for sequential decision-making. CUP synergistically combines the action generation capacity of large language models with a structured planner that evaluates the long-term informational value of actions, thereby jointly optimizing exploration and commitment. Experimental results demonstrate that the method significantly improves task success rates and reduces dialogue turns across multiple benchmarks, effectively breaking the longstanding tension between flexibility and long-horizon planning.

Abstract

Goal-oriented conversational systems require making sequential decisions under uncertainty about the user's intent, where the algorithm must balance information acquisition and target commitment over multiple turns. Existing approaches address this challenge from different perspectives: structured methods enable multi-step planning but rely on predefined schemas, while LLM-based approaches support flexible interactions but lack long-horizon decision making, resulting in poor coordination between information acquisition and target commitment. To address this limitation, we formulate goal-oriented conversation as an uncertainty-aware sequential decision problem, where uncertainty serves as a guiding signal for multi-turn decision making. We propose a Conversation Uncertainty-aware Planning framework (CUP) that integrates language models with structured planning: a language model proposes feasible actions, and a planner evaluates their long-term impact on uncertainty reduction. Experiments on multiple conversational benchmarks show that CUP consistently improves success rates while requiring fewer interaction turns. Further analysis demonstrates that uncertainty-aware planning contributes to more efficient information acquisition and earlier confident commitment.

Problem

• goal-oriented conversation
• sequential decision making
• uncertainty
• information acquisition
• target commitment

Innovation

• uncertainty-aware planning
• goal-oriented conversation
• sequential decision making
• language model integration
• multi-turn dialogue