# Counterfactual Scenarios for Automated Planning

## TL;DR

This paper introduces a new explanation approach for automated planning using counterfactual scenarios, which identify minimal modifications to planning problems to achieve desired properties, enhancing interpretability beyond traditional plan modifications.

## Contribution

It proposes a novel counterfactual scenario framework for planning explanations based on properties specified by extless ltlf extgreater formulas, with complexity analysis and practical viability.

## Key findings

- Counterfactual scenarios can be generated with complexity comparable to plan computation.
- The framework captures higher-level properties of planning problems.
- Practical algorithms are feasible based on the complexity results.

## Abstract

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.