# Conversation as Action Under Uncertainty

arXiv: https://arxiv.org/abs/1301.3883 · HTML: https://mlsanity.com/paper/1301.3883/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2013.

## Built on by (most cited first, 11 of 11)

- [Rethinking Explainability as a Dialogue: A Practitioner's Perspective](https://mlsanity.com/paper/2202.01875/) (2022)
- [Creating General User Models from Computer Use](https://mlsanity.com/paper/2505.10831/) (2025)
- [Asking the Right Question at the Right Time: Human and Model Uncertainty Guidance to Ask Clarification Questions](https://mlsanity.com/paper/2402.06509/) (2024)
- [Navigating Rifts in Human-LLM Grounding: Study and Benchmark](https://mlsanity.com/paper/2503.13975/) (2025)
- [Retrieval-Augmented Conversational Recommendation with Prompt-based Semi-Structured Natural Language State Tracking](https://mlsanity.com/paper/2406.00033/) (2024)
- [Pragmatics in Language Grounding: Phenomena, Tasks, and Modeling Approaches](https://mlsanity.com/paper/2211.08371/) (2022)
- [Challenges in Human-Agent Communication](https://mlsanity.com/paper/2412.10380/) (2024)
- [Conversational Grounding: Annotation and Analysis of Grounding Acts and Grounding Units](https://mlsanity.com/paper/2403.16609/) (2024)
- [Decision-Theoretic Question Generation for Situated Reference Resolution: An Empirical Study and Computational Model](https://mlsanity.com/paper/2110.06288/) (2021)
- [Rescue Conversations from Dead-ends: Efficient Exploration for Task-oriented Dialogue Policy Optimization](https://mlsanity.com/paper/2305.03262/) (2023)
- [Clarify, Abstain or Answer? Strategising in Conversation with Belief-Augmented Generation](https://mlsanity.com/paper/2605.25831/) (2026)

## Abstract (arXiv)

Conversations abound with uncetainties of various kinds. Treating conversation as inference and decision making under uncertainty, we propose a task independent, multimodal architecture for supporting robust continuous spoken dialog called Quartet. We introduce four interdependent levels of analysis, and describe representations, inference procedures, and decision strategies for managing uncertainties within and between the levels. We highlight the approach by reviewing interactions between a user and two spoken dialog systems developed using the Quartet architecture: Prsenter, a prototype system for navigating Microsoft PowerPoint presentations, and the Bayesian Receptionist, a prototype system for dealing with tasks typically handled by front desk receptionists at the Microsoft corporate campus.