Counterfactual Scenarios for Automated Planning

2025-08-29

AI Summary

Existing counterfactual explanations (CEs) in automated planning focus solely on minimal perturbations to *plans*, failing to expose high-level semantic properties of the underlying *planning problem*. Method: We propose “counterfactual scenarios” — a novel paradigm that identifies minimal modifications to the planning problem itself (e.g., action preconditions, goal conditions, or domain constraints), such that the modified problem admits a feasible plan satisfying a user-specified high-level property expressed in Linear Temporal Logic over finite traces (LTLf). Contribution/Results: We introduce the first formal framework for quantifying counterfactual scenarios over the space of planning problems, integrating planning logic with formal verification techniques. We systematically analyze computational complexity across diverse modification operations and prove that solving counterfactual scenarios incurs no higher worst-case complexity than solving the original planning problem. The framework achieves strong expressivity, interpretability, and practicality, significantly deepening the modeling capacity and broadening the applicability of counterfactual reasoning in automated planning.

Technology Category

• Planning, Routing, and Scheduling: Temporal Planning
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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 $ψ$ 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.

Problem

• Proposing counterfactual scenarios for automated planning explanations
• Identifying minimal modifications to planning problems for desired properties
• Characterizing computational complexity of generating counterfactual scenarios

Innovation

• Counterfactual scenarios modify planning problems minimally
• Explicit quantification over plans satisfying LTLf formulas
• Computational complexity similar to standard plan generation