# Human-Agent Coordination in Games under Incomplete Information via Multi-Step Intent

arXiv: https://arxiv.org/abs/2410.18242 · HTML: https://mlsanity.com/paper/2410.18242/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 30 references that are in the map.
- First posted on arXiv 23 Oct 2024.

## Builds on (most cited first, 2 of 2)

- [Opponent Modeling in Deep Reinforcement Learning](https://mlsanity.com/paper/1609.05559/) (2016)
- [Human-Agent Cooperation in Games under Incomplete Information through Natural Language Communication](https://mlsanity.com/paper/2405.14173/) (2024)

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

- [IG-MCTS: Human-in-the-Loop Cooperative Navigation under Incomplete Information](https://mlsanity.com/paper/2502.01857/) (2025)
- [POrTAL: Plan-Orchestrated Tree Assembly for Lookahead](https://mlsanity.com/paper/2512.06002/) (2025)
- [From Control to Foresight: Simulation as a New Paradigm for Human-Agent Collaboration](https://mlsanity.com/paper/2603.11677/) (2026)
- [Design Principles for Human-Agent Interaction](https://mlsanity.com/paper/2606.20630/) (2026)

## Abstract (arXiv)

Strategic coordination between autonomous agents and human partners under incomplete information can be modeled as turn-based cooperative games. We extend a turn-based game under incomplete information, the shared-control game, to allow players to take multiple actions per turn rather than a single action. The extension enables the use of multi-step intent, which we hypothesize will improve performance in long-horizon tasks. To synthesize cooperative policies for the agent in this extended game, we propose an approach featuring a memory module for a running probabilistic belief of the environment dynamics and an online planning algorithm called IntentMCTS. This algorithm strategically selects the next action by leveraging any communicated multi-step intent via reward augmentation while considering the current belief. Agent-to-agent simulations in the Gnomes at Night testbed demonstrate that IntentMCTS requires fewer steps and control switches than baseline methods. A human-agent user study corroborates these findings, showing an 18.52% higher success rate compared to the heuristic baseline and a 5.56% improvement over the single-step prior work. Participants also report lower cognitive load, frustration, and higher satisfaction with the IntentMCTS agent partner.