探讨自动化规划中应对突发情况的反事实推理方法
Counterfactual Reasoning in Automated Planning
- 分析规划任务中状态、目标、动作等要素的可变性
- 归纳反事实推理的触发时机与修改动因分类体系
- 适合研究智能决策系统与鲁棒规划的学者参考
自动化规划传统上假设任务的所有方面（初始状态、目标和可用动作）均事先完全确定，这种方法适用于规则固定且执行确定的领域。然而，现实世界中的规划往往需要灵活性，能够根据未预见的情况偏离原定参数或优化结果。本文综述了自动化规划中反事实推理的现有工作，按被改变的元素、推理触发时机以及变更的原因和方式进行了分类。最后总结关键发现，并提出未来研究的关键问题以引导该领域的进展。
原文摘要 · Abstract (English)
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.
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