The Energy Impact of Domain Model Design in Classical Planning

2026-01-29

AI Summary

This study addresses the long-standing neglect of energy consumption in classical planning and the absence of systematic investigation into how domain model design influences planner energy efficiency. Introducing the principles of Green AI to classical planning for the first time, the work proposes a configurable domain modeling framework that systematically varies factors such as element ordering, action arity, and dead-end states. The authors generate 32 domain variants across five benchmark domains and conduct controlled experiments with five state-of-the-art planners, measuring both energy consumption and runtime. Results demonstrate that domain-level modifications can significantly alter a planner’s energy usage, and crucially, these energy trends often diverge from runtime behavior. This reveals an independent influence of domain modeling on energy efficiency, establishing a new optimization dimension for green planning.

Technology Category

• Machine Learning: Efficient ML / Green AI
• Planning, Routing, and Scheduling: Planning with Language Models
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

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.

Problem

• energy consumption
• classical planning
• domain model design
• Green AI
• automated planning

Innovation

• Green AI
• classical planning
• domain model design
• energy consumption
• planner efficiency