# Preference Inference from Demonstration in Multi-objective Multi-agent Decision Making

arXiv: https://arxiv.org/abs/2304.14126 · HTML: https://mlsanity.com/paper/2304.14126/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 12 references that are in the map.
- First posted on arXiv 27 Apr 2023.

## Builds on (most cited first, 1 of 1)

- [Multi-agent Reinforcement Learning in Sequential Social Dilemmas](https://mlsanity.com/paper/1702.03037/) (2017)

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

- [Inferring Preferences from Demonstrations in Multi-Objective Residential Energy Management](https://mlsanity.com/paper/2401.07722/) (2024)
- [Inferring Preferences from Demonstrations in Multi-objective Reinforcement Learning](https://mlsanity.com/paper/2409.20258/) (2024)
- [State-of-the-art in Robot Learning for Multi-Robot Collaboration: A Comprehensive Survey](https://mlsanity.com/paper/2408.11822/) (2024)

## Abstract (arXiv)

It is challenging to quantify numerical preferences for different objectives in a multi-objective decision-making problem. However, the demonstrations of a user are often accessible. We propose an algorithm to infer linear preference weights from either optimal or near-optimal demonstrations. The algorithm is evaluated in three environments with two baseline methods. Empirical results demonstrate significant improvements compared to the baseline algorithms, in terms of both time requirements and accuracy of the inferred preferences. In future work, we plan to evaluate the algorithm's effectiveness in a multi-agent system, where one of the agents is enabled to infer the preferences of an opponent using our preference inference algorithm.