BEDA: Belief Estimation as Probabilistic Constraints for Performing Strategic Dialogue Acts
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 (CKBG, adversarial), Mutual Friends (MF, 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 GPT-4.1-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.
6956184b832867f253525712
Hengli Li
claimed_verified
2026-01-02T15:38:47.395Z
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Li
henry12348
user
henry12348
6956184b832867f253525713
Zhaoxin Yu
6956184b832867f253525714
Qi Shen
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Chenxi Li
6956184b832867f253525716
Mengmeng Wang
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Tinglang Wu
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Yipeng Kang
6956184b832867f253525719
Yuxuan Wang
6956184b832867f25352571a
Song-Chun Zhu
6956184b832867f25352571b
Zixia Jia
6956184b832867f25352571c
Zilong Zheng
BEDA: Belief Estimation as Probabilistic Constraints for Performing Strategic Dialogue Acts
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 (CKBG, adversarial), Mutual Friends (MF, 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 GPT-4.1-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.
A framework called BEDA uses probabilistic constraints on belief estimation to improve strategic dialogue through formalized adversarial and alignment acts, outperforming baselines across multiple task settings.
BEDA belief estimator conditional generator probabilistic constraints Adversarial Alignment