# Explaining Preference-driven Schedules: the EXPRES Framework

arXiv: https://arxiv.org/abs/2203.08895 · HTML: https://mlsanity.com/paper/2203.08895/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 16 references that are in the map.
- First posted on arXiv 16 Mar 2022.

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

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Explainable Artificial Intelligence: Understanding, Visualizing and Interpreting Deep Learning Models](https://mlsanity.com/paper/1708.08296/) (2017)
- [Explainable Planning](https://mlsanity.com/paper/1709.10256/) (2017)
- [Explicability? Legibility? Predictability? Transparency? Privacy? Security? The Emerging Landscape of Interpretable Agent Behavior](https://mlsanity.com/paper/1811.09722/) (2018)
- [AI for Explaining Decisions in Multi-Agent Environments](https://mlsanity.com/paper/1910.04404/) (2019)
- [Not all users are the same: Providing personalized explanations for sequential decision making problems](https://mlsanity.com/paper/2106.12207/) (2021)
- [Argumentation for Explainable Scheduling (Full Paper with Proofs)](https://mlsanity.com/paper/1811.05437/) (2018)
- [Using Explainable Scheduling for the Mars 2020 Rover Mission](https://mlsanity.com/paper/2011.08733/) (2020)

## Built on by (most cited first, 5 of 5)

- [Contrastive Explanations of Centralized Multi-agent Optimization Solutions](https://mlsanity.com/paper/2308.05984/) (2023)
- [Does Your AI Agent Get You? A Personalizable Framework for Approximating Human Models from Argumentation-based Dialogue Traces](https://mlsanity.com/paper/2502.16376/) (2025)
- [Contrastive Explainable Clustering with Differential Privacy](https://mlsanity.com/paper/2406.04610/) (2024)
- [TRACE-CS: A Hybrid Logic-LLM System for Explainable Course Scheduling](https://mlsanity.com/paper/2409.03671/) (2024)
- [Exploiting Constraint Reasoning to Build Graphical Explanations for Mixed-Integer Linear Programming](https://mlsanity.com/paper/2507.13007/) (2025)

## Abstract (arXiv)

Scheduling is the task of assigning a set of scarce resources distributed over time to a set of agents, who typically have preferences about the assignments they would like to get. Due to the constrained nature of these problems, satisfying all agents' preferences is often infeasible, which might lead to some agents not being happy with the resulting schedule. Providing explanations has been shown to increase satisfaction and trust in solutions produced by AI tools. However, it is particularly challenging to explain solutions that are influenced by and impact on multiple agents. In this paper we introduce the EXPRES framework, which can explain why a given preference was unsatisfied in a given optimal schedule. The EXPRES framework consists of: (i) an explanation generator that, based on a Mixed-Integer Linear Programming model, finds the best set of reasons that can explain an unsatisfied preference; and (ii) an explanation parser, which translates the generated explanations into human interpretable ones. Through simulations, we show that the explanation generator can efficiently scale to large instances. Finally, through a set of user studies within J.P. Morgan, we show that employees preferred the explanations generated by EXPRES over human-generated ones when considering workforce scheduling scenarios.