# Counterfactual Reasoning in Automated Planning

arXiv: https://arxiv.org/abs/2605.02603 · HTML: https://mlsanity.com/paper/2605.02603/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 4 May 2026.

## Builds on (most cited first, 4 of 4)

- [Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary](https://mlsanity.com/paper/1705.00154/) (2017)
- [Learning First-Order Symbolic Representations for Planning from the Structure of the State Space](https://mlsanity.com/paper/1909.05546/) (2019)
- [On Learning Action Costs from Input Plans](https://mlsanity.com/paper/2408.10889/) (2024)
- [Planning Task Shielding: Detecting and Repairing Flaws in Planning Tasks through Turning them Unsolvable](https://mlsanity.com/paper/2604.07042/) (2026)

## Abstract (arXiv)

Automated planning traditionally assumes that all aspects of a planning task (initial state, goals, and available actions) are fully specified in advance, an approach well-suited to domains with fixed rules and deterministic execution. However, real-world planning often requires flexibility, allowing for deviations from the original task parameters in response to unforeseen circumstances or to improve outcomes. This paper surveys existing works on counterfactual reasoning in automated planning, categorizing them by what elements are changed, when the reasoning is triggered, and why and how these changes are made. We conclude by discussing key findings and outlining open research questions to guide future work in this area.