# Fairness in Multi-Agent Planning

arXiv: https://arxiv.org/abs/2212.00506 · HTML: https://mlsanity.com/paper/2212.00506/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 1 Dec 2022.

## Builds on (most cited first, 9 of 9)

- [Fairness in Machine Learning](https://mlsanity.com/paper/2012.15816/) (2020)
- [The LAMA Planner: Guiding Cost-Based Anytime Planning with Landmarks](https://mlsanity.com/paper/1401.3839/) (2014)
- [Explainable Planning](https://mlsanity.com/paper/1709.10256/) (2017)
- [Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness](https://mlsanity.com/paper/2010.05434/) (2020)
- [Optimal Target Assignment and Path Finding for Teams of Agents](https://mlsanity.com/paper/1612.05693/) (2016)
- [Solving Simultaneous Target Assignment and Path Planning Efficiently with Time-Independent Execution](https://mlsanity.com/paper/2109.04264/) (2021)
- [Cooperative Multi-Agent Fairness and Equivariant Policies](https://mlsanity.com/paper/2106.05727/) (2021)
- [Flexible FOND Planning with Explicit Fairness Assumptions](https://mlsanity.com/paper/2103.08391/) (2021)
- [Profit equitably: An investigation of market maker's impact on equitable outcomes](https://mlsanity.com/paper/2111.00094/) (2021)

## Built on by (most cited first, 1 of 1)

- [Temporal Fairness in Decision Making Problems](https://mlsanity.com/paper/2408.13208/) (2024)

## Abstract (arXiv)

In cooperative Multi-Agent Planning (MAP), a set of goals has to be achieved by a set of agents. Independently of whether they perform a pre-assignment of goals to agents or they directly search for a solution without any goal assignment, most previous works did not focus on a fair distribution/achievement of goals by agents. This paper adapts well-known fairness schemes to MAP, and introduces two novel approaches to generate cost-aware fair plans. The first one solves an optimization problem to pre-assign goals to agents, and then solves a centralized MAP task using that assignment. The second one consists of a planning-based compilation that allows solving the joint problem of goal assignment and planning while taking into account the given fairness scheme. Empirical results in several standard MAP benchmarks show that these approaches outperform different baselines. They also show that there is no need to sacrifice much plan cost to generate fair plans.