# Counterfactual Scenarios for Automated Planning

arXiv: https://arxiv.org/abs/2508.21521 · HTML: https://mlsanity.com/paper/2508.21521/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 32 references that are in the map.
- First posted on arXiv 29 Aug 2025.

## Builds on (most cited first, 3 of 3)

- [The Emerging Landscape of Explainable AI Planning and Decision Making](https://mlsanity.com/paper/2002.11697/) (2020)
- [Contrastive Explanations of Plans Through Model Restrictions](https://mlsanity.com/paper/2103.15575/) (2021)
- [Counterfactual Influence in Markov Decision Processes](https://mlsanity.com/paper/2402.08514/) (2024)

## Abstract (arXiv)

Counterfactual Explanations (CEs) are a powerful technique used to explain Machine Learning models by showing how the input to a model should be minimally changed for the model to produce a different output. Similar proposals have been made in the context of Automated Planning, where CEs have been characterised in terms of minimal modifications to an existing plan that would result in the satisfaction of a different goal. While such explanations may help diagnose faults and reason about the characteristics of a plan, they fail to capture higher-level properties of the problem being solved. To address this limitation, we propose a novel explanation paradigm that is based on counterfactual scenarios. In particular, given a planning problem P and an ltlf formula psi defining desired properties of a plan, counterfactual scenarios identify minimal modifications to P such that it admits plans that comply with psi. In this paper, we present two qualitative instantiations of counterfactual scenarios based on an explicit quantification over plans that must satisfy psi. We then characterise the computational complexity of generating such counterfactual scenarios when different types of changes are allowed on P. We show that producing counterfactual scenarios is often only as expensive as computing a plan for P, thus demonstrating the practical viability of our proposal and ultimately providing a framework to construct practical algorithms in this area.