# Multi-Agent Strategy Explanations for Human-Robot Collaboration

arXiv: https://arxiv.org/abs/2311.11955 · HTML: https://mlsanity.com/paper/2311.11955/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 15 of its 42 references that are in the map.
- First posted on arXiv 20 Nov 2023.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 15 of 15)

- [VirtualHome: Simulating Household Activities via Programs](https://mlsanity.com/paper/1806.07011/) (2018)
- [Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots](https://mlsanity.com/paper/2310.13724/) (2023)
- [Learning Latent Representations to Influence Multi-Agent Interaction](https://mlsanity.com/paper/2011.06619/) (2020)
- [Coordinated Multi-Agent Imitation Learning](https://mlsanity.com/paper/1703.03121/) (2017)
- [An Overview of Machine Teaching](https://mlsanity.com/paper/1801.05927/) (2018)
- [Establishing Appropriate Trust via Critical States](https://mlsanity.com/paper/1810.08174/) (2018)
- [Efficient Iterative Linear-Quadratic Approximations for Nonlinear Multi-Player General-Sum Differential Games](https://mlsanity.com/paper/1909.04694/) (2019)
- [Enabling Robots to Communicate their Objectives](https://mlsanity.com/paper/1702.03465/) (2017)
- [Probabilistically Safe Robot Planning with Confidence-Based Human Predictions](https://mlsanity.com/paper/1806.00109/) (2018)
- [Influencing Towards Stable Multi-Agent Interactions](https://mlsanity.com/paper/2110.08229/) (2021)
- [Co-GAIL: Learning Diverse Strategies for Human-Robot Collaboration](https://mlsanity.com/paper/2108.06038/) (2021)
- [Inference-Based Strategy Alignment for General-Sum Differential Games](https://mlsanity.com/paper/2002.04354/) (2020)
- [ALGAMES: A Fast Solver for Constrained Dynamic Games](https://mlsanity.com/paper/1910.09713/) (2019)
- [iLQGames.jl: Rapidly Designing and Solving Differential Games in Julia](https://mlsanity.com/paper/2002.10185/) (2020)
- [Generating Explanations from Deep Reinforcement Learning Using Episodic Memory](https://mlsanity.com/paper/2205.08926/) (2022)

## Built on by (most cited first, 2 of 2)

- [Adaptive Collaboration with Humans: Metacognitive Policy Optimization for Multi-Agent LLMs with Continual Learning](https://mlsanity.com/paper/2603.07972/) (2026)
- [Robots Influencing Humans to Reveal their Goals during Collaboration and Competition](https://mlsanity.com/paper/2609.05519/) (2026)

## Abstract (arXiv)

As robots are deployed in human spaces, it is important that they are able to coordinate their actions with the people around them. Part of such coordination involves ensuring that people have a good understanding of how a robot will act in the environment. This can be achieved through explanations of the robot's policy. Much prior work in explainable AI and RL focuses on generating explanations for single-agent policies, but little has been explored in generating explanations for collaborative policies. In this work, we investigate how to generate multi-agent strategy explanations for human-robot collaboration. We formulate the problem using a generic multi-agent planner, show how to generate visual explanations through strategy-conditioned landmark states and generate textual explanations by giving the landmarks to an LLM. Through a user study, we find that when presented with explanations from our proposed framework, users are able to better explore the full space of strategies and collaborate more efficiently with new robot partners.