Counterfactual Scenarios for Automated Planning

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 ψ defining desired properties of a

plan, counterfactual scenarios identify minimal

modifications to P such that it admits plans that comply

with ψ. In this paper, we present two qualitative

instantiations of counterfactual scenarios based on an

explicit quantification over plans that must satisfy

ψ. 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.