# A Dialogue Game for Eliciting Balanced Collaboration

arXiv: https://arxiv.org/abs/2406.08202 · HTML: https://mlsanity.com/paper/2406.08202/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 12 Jun 2024.

## Builds on (most cited first, 8 of 8)

- [Open Problems in Cooperative AI](https://mlsanity.com/paper/2012.08630/) (2020)
- [Human Instruction-Following with Deep Reinforcement Learning via Transfer-Learning from Text](https://mlsanity.com/paper/2005.09382/) (2020)
- [ACTRCE: Augmenting Experience via Teacher's Advice For Multi-Goal Reinforcement Learning](https://mlsanity.com/paper/1902.04546/) (2019)
- [Draw Me a Flower: Processing and Grounding Abstraction in Natural Language](https://mlsanity.com/paper/2106.14321/) (2021)
- [What A Situated Language-Using Agent Must be Able to Do: A Top-Down Analysis](https://mlsanity.com/paper/2302.08590/) (2023)
- [The slurk Interaction Server Framework: Better Data for Better Dialog Models](https://mlsanity.com/paper/2202.01155/) (2022)
- [Generating Instructions at Different Levels of Abstraction](https://mlsanity.com/paper/2010.03982/) (2020)
- [Learning Communication Policies for Different Follower Behaviors in a Collaborative Reference Game](https://mlsanity.com/paper/2402.04824/) (2024)

## Built on by (most cited first, 4 of 4)

- [Characterizing Language Use in a Collaborative Situated Game](https://mlsanity.com/paper/2512.03381/) (2025)
- [A Survey on Complex Tasks for Goal-Directed Interactive Agents](https://mlsanity.com/paper/2409.18538/) (2024)
- [Collaborative Problem-Solving in an Optimization Game](https://mlsanity.com/paper/2505.15490/) (2025)
- [Bridging Talk and Thought: Understanding Dialogue Dynamics Across Collaborative Problem-Solving Contexts](https://mlsanity.com/paper/2606.27233/) (2026)

## Abstract (arXiv)

Collaboration is an integral part of human dialogue. Typical task-oriented dialogue games assign asymmetric roles to the participants, which limits their ability to elicit naturalistic role-taking in collaboration and its negotiation. We present a novel and simple online setup that favors balanced collaboration: a two-player 2D object placement game in which the players must negotiate the goal state themselves. We show empirically that human players exhibit a variety of role distributions, and that balanced collaboration improves task performance. We also present an LLM-based baseline agent which demonstrates that automatic playing of our game is an interesting challenge for artificial systems.