不同领域模型设计显著影响规划算法能耗，且耗能与运行时间无必然关联。
The Energy Impact of Domain Model Design in Classical Planning
- 构建可调控的领域模型配置框架，系统测试元素顺序、动作参数等对能耗的影响。
- 在5个基准领域中测试32种变体，发现模型修改导致能耗差异最高达6倍。
- 揭示能耗与运行时间不一致，适合关注绿色计算的规划系统开发者参考。
人工智能研究传统上侧重算法性能，如机器学习的精度或自动规划的运行时间。新兴的绿色AI范式则将能耗视为关键性能指标。尽管自动规划具有高计算需求，但其能源效率却很少受到关注。这一问题尤为突出，因为规划模块结构允许领域模型与算法独立设计，这种分离也为通过领域模型设计系统分析能耗提供了可能。本文实证研究领域模型特征如何影响经典规划器的能耗。我们提出一个领域模型配置框架，支持对元素顺序、动作参数、死区状态等特征进行受控变化。利用五个基准领域和五种先进规划器，在每个基准下分析32种领域变体的能耗与运行时间。结果表明，领域级修改会引发显著的能耗差异，且能耗与运行时间并不总呈正相关。
原文摘要 · Abstract (English)
AI research has traditionally prioritised algorithmic performance, such as optimising accuracy in machine learning or runtime in automated planning. The emerging paradigm of Green AI challenges this by recognising energy consumption as a critical performance dimension. Despite the high computational demands of automated planning, its energy efficiency has received little attention. This gap is particularly salient given the modular planning structure, in which domain models are specified independently of algorithms. On the other hand, this separation also enables systematic analysis of energy usage through domain model design. We empirically investigate how domain model characteristics affect the energy consumption of classical planners. We introduce a domain model configuration framework that enables controlled variation of features, such as element ordering, action arity, and dead-end states. Using five benchmark domains and five state-of-the-art planners, we analyse energy and runtime impacts across 32 domain variants per benchmark. Results demonstrate that domain-level modifications produce measurable energy differences across planners, with energy consumption not always correlating with runtime.
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