Counterfactual Reasoning in Automated Planning

2026-05-04

AI Summary

Traditional automated planning assumes that all task elements are fully predefined, rendering it ill-suited for real-world scenarios requiring dynamic adjustments to initial states, goals, or actions. This work presents the first systematic classification framework for counterfactual reasoning in automated planning, organizing existing approaches according to the modified elements, timing of intervention, and underlying motivations and mechanisms. By integrating perspectives from logical reasoning, planning theory, and causal modeling, the study clarifies the research landscape, highlights key findings, and identifies open challenges. The proposed framework establishes a theoretical foundation and provides clear directions for enhancing the adaptability and robustness of planning systems in complex, dynamic environments.

Abstract

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.

Problem

• counterfactual reasoning
• automated planning
• task flexibility
• unforeseen circumstances
• planning adaptation

Innovation

• counterfactual reasoning
• automated planning
• plan adaptation
• flexible planning
• uncertainty handling