# Planning Task Shielding: Detecting and Repairing Flaws in Planning Tasks through Turning them Unsolvable

arXiv: https://arxiv.org/abs/2604.07042 · HTML: https://mlsanity.com/paper/2604.07042/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 27 references that are in the map.
- First posted on arXiv 8 Apr 2026.

## Builds on (most cited first, 2 of 2)

- [Monte Carlo Tree Search: A Review of Recent Modifications and Applications](https://mlsanity.com/paper/2103.04931/) (2021)
- [Beyond Games: A Systematic Review of Neural Monte Carlo Tree Search Applications](https://mlsanity.com/paper/2303.08060/) (2023)

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

- [Counterfactual Reasoning in Automated Planning](https://mlsanity.com/paper/2605.02603/) (2026)

## Abstract (arXiv)

Most research in planning focuses on generating a plan to achieve a desired set of goals. However, a goal specification can also be used to encode a property that should never hold, allowing a planner to identify a trace that would reach a flawed state. In such cases, the objective may shift to modifying the planning task to ensure that the flawed state is never reached, in other words, to make the planning task unsolvable. In this paper we introduce planning task shielding: the problem of detecting and repairing flaws in planning tasks. We propose three different approaches to solve these tasks, each aiming to minimize the number of modifications to the original actions that render the planning task unsolvable.