Automated planning is a prominent area of Artificial Intelligence, and an important component for intelligent autonomous agents. A cornerstone of domain-independent planning is the separation between planning logic, i.e. the automated reasoning side, and the knowledge model, that encodes a formal representation of domain knowledge needed to reason upon a given problem to synthesise a solution plan. Such a separation enables the use of reformulation techniques, which transform how a model is represented in order to improve the efficiency of plan generation. Over the past decades, significant research effort has been devoted to the design of reformulation techniques. In this paper, we present a systematic review of the large body of work on reformulation techniques for classical planning, aiming to provide a holistic view of the field and to foster future research in the area. As a tangible outcome, we provide a qualitative comparison of the existing classes of techniques, that can help researchers gain an overview of their strengths and weaknesses.

翻译：自动化规划是人工智能领域的一个重要方向，也是智能自主体的关键组成部分。领域无关规划的基石在于规划逻辑（即自动推理部分）与知识模型之间的分离——后者通过编码领域知识的正式表示，为针对特定问题合成解决方案提供推理基础。这种分离使得重构技术得以应用，即通过转换模型表示方式来提高规划生成的效率。过去几十年间，大量研究工作致力于重构技术的设计。本文对经典规划中重构技术的研究成果进行了系统性综述，旨在为该领域提供全局性视角并推动未来研究发展。作为具体成果，我们对现有各类技术进行了定性比较，可帮助研究者全面了解其优势与局限。