# Improving Proactive Dialog Agents Using Socially-Aware Reinforcement Learning

arXiv: https://arxiv.org/abs/2211.15359 · HTML: https://mlsanity.com/paper/2211.15359/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 55 references that are in the map.
- First posted on arXiv 25 Nov 2022.

## Builds on (most cited first, 6 of 6)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Proactive Human-Machine Conversation with Explicit Conversation Goals](https://mlsanity.com/paper/1906.05572/) (2019)
- [Target-Guided Open-Domain Conversation](https://mlsanity.com/paper/1905.11553/) (2019)
- [How should my chatbot interact? A survey on human-chatbot interaction design](https://mlsanity.com/paper/1904.02743/) (2019)
- [Proactive Retrieval-based Chatbots based on Relevant Knowledge and Goals](https://mlsanity.com/paper/2107.08329/) (2021)
- [Improving Interaction Quality Estimation with BiLSTMs and the Impact on Dialogue Policy Learning](https://mlsanity.com/paper/2001.07615/) (2020)

## Built on by (most cited first, 3 of 3)

- [Development of a Trust-Aware User Simulator for Statistical Proactive Dialog Modeling in Human-AI Teams](https://mlsanity.com/paper/2304.11913/) (2023)
- [Proactive Systems in HCI and AI: Concepts, Challenges, and Opportunities](https://mlsanity.com/paper/2606.25149/) (2026)
- [Foundations of Proactive Agents: Principles, Technical Layers, and Proactivity-Gym](https://mlsanity.com/paper/2609.37267/) (2026)

## Abstract (arXiv)

The next step for intelligent dialog agents is to escape their role as silent bystanders and become proactive. Well-defined proactive behavior may improve human-machine cooperation, as the agent takes a more active role during interaction and takes off responsibility from the user. However, proactivity is a double-edged sword because poorly executed pre-emptive actions may have a devastating effect not only on the task outcome but also on the relationship with the user. For designing adequate proactive dialog strategies, we propose a novel approach including both social as well as task-relevant features in the dialog. Here, the primary goal is to optimize proactive behavior so that it is task-oriented - this implies high task success and efficiency - while also being socially effective by fostering user trust. Including both aspects in the reward function for training a proactive dialog agent using reinforcement learning showed the benefit of our approach for more successful human-machine cooperation.