Belief Estimation as Probabilistic Constraints for Performing Strategic Dialogue Acts

Hengli Li, Zhaoxin Yu, Qi Shen, Chenxi Li, Mengmeng Wang, Tinglang Wu, Yipeng Kang, Yuxuan Wang, Song-Chun Zhu, Zixia Jia, Zilong Zheng

Keywords: Strategic Reasoning, Dialogues, Dialogue Games, Belief Estimation, Theory of Mind

Abstract:

Strategic dialogue requires agents to execute distinct dialogue acts,
for which belief estimation is essential. While prior work often
estimates beliefs accurately, it lacks a principled mechanism to
use those beliefs during generation. We bridge this gap by first
formalizing two core acts Adversarial and Alignment—and by
operationalizing them via probabilistic constraints on what an
agent may generate. We instantiate this idea in BEDA, a framework that consists of the world set, the belief estimator for belief
estimation, and the conditional generator that selects acts and realizes utterances consistent with the inferred beliefs. Across three
settings, Conditional Keeper–Burglar (adversarial), Mutual Friends
(cooperative), and CaSiNo (negotiation), BEDA consistently outperforms strong baselines: on CKBG it improves success rate by at
least 5.0 points across backbones and by 20.6 points with GPT4.1-nano; on Mutual Friends it achieves an average improvement
of 9.3 points; and on CaSiNo it achieves the optimal deal relative
to all baselines. These results indicate that casting belief estimation
as constraints provides a simple, general mechanism for reliable
strategic dialogue.

Track: Long Paper

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Submission Number: 27