Counterfactual Reasoning in Automated Planning

TL;DR

This paper surveys how counterfactual reasoning enhances automated planning by allowing flexible adjustments to initial conditions, goals, or actions in response to unforeseen circumstances.

Contribution

It categorizes existing approaches to counterfactual reasoning in planning and discusses open research questions for future exploration.

Findings

Categorizes counterfactual reasoning methods based on elements changed, timing, and motivation.

Highlights the importance of flexibility in real-world planning scenarios.

Identifies open challenges and directions for future research.

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.