Planning’s Hidden Power Bill

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Author: Denis Avetisyan

New research reveals that the way AI planning problems are modeled dramatically affects energy consumption, raising concerns about the environmental impact of increasingly complex algorithms.

Domain model configuration significantly impacts the energy efficiency of classical planners, with redundancy playing a key role in overall consumption.

While artificial intelligence research has historically prioritized performance metrics like accuracy and runtime, the growing field of Green AI necessitates a broader consideration of energy consumption. This paper, ‘The Energy Impact of Domain Model Design in Classical Planning’, investigates how the design of domain models – independent specifications of planning problems – affects the energy footprint of automated planners. Our empirical analysis, using a configurable framework and five benchmark domains, demonstrates that domain-level modifications yield measurable energy differences across planners, often independent of runtime performance. Given these findings, how can we systematically incorporate energy efficiency into the design of AI planning systems and beyond?

The Architect’s Burden: Defining the Planning Landscape

Classical planning systems fundamentally depend on a meticulously defined representation of the environment, known as a domain model. This model isn’t a direct simulation of reality, but rather a formal, symbolic description outlining all possible states the environment can occupy and the actions that can transition it between those states. Each action is broken down into preconditions – what must be true for the action to be executed – and effects, which detail how the environment changes as a result. By explicitly defining these elements, a planning system can logically deduce a sequence of actions to achieve a desired goal, effectively navigating a complex landscape through symbolic reasoning rather than direct sensory input. The accuracy and completeness of this domain model are paramount; any omission or inaccuracy will limit the system’s ability to find effective, or even feasible, plans.

The efficacy of automated planning hinges on a process known as grounding, where abstract symbols representing objects and actions within a plan are connected to specific, real-world instances. Without accurate grounding, a plan detailing, for example, “move the block” remains merely a logical sequence; it cannot be executed by a robot or agent operating in a physical environment. This connection requires identifying which physical object corresponds to the symbol “block” and determining the appropriate motor commands to achieve the “move” action. Incorrect grounding – mistaking a cylinder for a block, or attempting an impossible movement – leads to plan failure. Therefore, robust grounding techniques, often involving perception and sensor data, are essential for bridging the gap between symbolic planning and successful execution in dynamic, real-world scenarios, ensuring the agent’s understanding of its environment is both accurate and actionable.

Efficient problem solving in artificial intelligence often hinges on the ability to estimate the ‘cost’ of reaching a desired goal state, a process known as heuristic evaluation. Rather than exhaustively searching every possible path, planning algorithms leverage heuristics – essentially educated guesses – to prioritize exploration along routes believed to be closest to the solution. These estimations, while not always perfect, drastically reduce computational demands, enabling solutions to complex problems within reasonable timeframes. The quality of a heuristic significantly impacts performance; an accurate heuristic guides the search effectively, while a poorly designed one can lead to wasted effort and suboptimal results. Consequently, developing and refining these cost estimations remains a central challenge in the field of automated planning, driving research into more sophisticated and reliable evaluation techniques.

The Price of Intelligence: Energy Consumption in AI

The computational demands of AI planning systems scale rapidly with problem complexity, leading to increased energy consumption. As planning tasks require exploring a larger search space of possible actions and states – particularly in domains with high dimensionality or long horizons – the number of computational steps, and therefore energy used, increases disproportionately. This is because many planning algorithms rely on exhaustive or near-exhaustive search, evaluating numerous potential plans before identifying an optimal or acceptable solution. Consequently, even modest increases in problem size can result in substantial energy expenditure, making energy efficiency a critical consideration in the development and deployment of AI planning systems.

Green AI is an emerging field dedicated to developing and implementing artificial intelligence algorithms with a reduced environmental footprint. This encompasses efforts to minimize energy consumption during both the training and deployment phases of AI models. Approaches within Green AI include algorithmic efficiency improvements – designing algorithms that achieve comparable performance with fewer computational resources – and hardware-aware optimization, tailoring algorithms to leverage energy-efficient hardware. The field also investigates methods for reducing the carbon footprint associated with the data centers and infrastructure required to support AI workloads, and promotes responsible AI development practices focused on sustainability.

Precise measurement of AI system energy consumption necessitates dedicated hardware and software tools due to the dynamic and often opaque nature of power draw during computation. PLANERGYM is a framework designed for benchmarking planning algorithms, providing detailed energy usage metrics alongside performance data, and supports various hardware platforms. Intel’s Running Average Power Limit (RAPL) is a hardware performance monitoring interface integrated into modern Intel processors, allowing for direct measurement of package, core, and uncore power consumption, offering a lower-level, hardware-centric approach to energy profiling. Utilizing these tools enables researchers and developers to accurately quantify the energy costs associated with different AI models and algorithms, facilitating the development of more energy-efficient solutions.

The Blueprint’s Imperfection: Domain Model Design

The configuration of a domain model – specifically the granularity of actions defined and the structure of predicates used to represent state – directly influences planning performance. Finer-grained actions, while potentially offering more flexibility in plan creation, increase the search space for the planner and consequently raise computational cost. Conversely, overly coarse-grained actions may limit the planner’s ability to find feasible solutions. Predicate structure impacts efficiency by affecting the complexity of state representation and the number of applicable actions in any given state. A poorly structured predicate set can lead to redundant state information and increased search time, while an optimized structure can streamline the planning process. These factors combine to determine both the speed and the energy consumption of the planning algorithm.

Syntactic and modelling choices within a domain model directly influence planning efficiency, specifically impacting energy consumption. Our research indicates that redundant action arity – defining multiple actions that achieve the same outcome – consistently elevates energy usage. Across tested configurations, redundant arity resulted in energy consumption increases ranging from a factor of 2 to 12 compared to more concise models. This inefficiency stems from the planner’s increased search space and computational overhead required to evaluate functionally equivalent actions, demonstrating the importance of minimizing redundancy in domain model design.

Task design choices exert a significant influence on energy consumption during automated planning. Empirical results indicate that varying task configurations can produce up to a fourfold difference in energy usage. Specifically, analysis of the Blocks World domain revealed a problematic configuration that increased energy consumption by a factor of 30 compared to baseline scenarios. These findings demonstrate that seemingly minor alterations to task solvability-such as the complexity of preconditions or the number of necessary steps-can dramatically impact the energy efficiency of planning algorithms.

The Echo of Choices: Evaluating and Comparing Planning Frameworks

Automated planning relies on diverse frameworks, each tackling problem-solving with unique strategies. Systems like Fast Downward prioritize speed through heuristic search, while LAPKT (Linear Programming with Knowledge Translation) leverages optimization techniques to systematically explore possible plans. These approaches differ fundamentally in how they represent problems, guide the search for solutions, and manage computational resources. Consequently, some frameworks excel in specific domains – for instance, efficiently handling problems with many possible actions – while others prove more robust in complex, constrained environments. The selection of an appropriate framework is therefore critical, influencing not only the time required to find a plan, but also the plan’s quality and the overall energy expenditure of the planning process.

The landscape of automated planning is populated by diverse implementations built upon core frameworks like Fast Downward. Variations such as Stone Soup Agile, Cerberus Agile, and DALAI Agile represent distinct approaches to heuristic search and problem representation, each optimizing for specific challenges within planning domains. Further divergence is seen in approaches like Approximate Novelty Search Tarski, which prioritize exploration and the discovery of novel solutions, even at the cost of immediate optimality. These implementations aren’t merely superficial alterations; they embody fundamental differences in search strategies, data structures, and the way planners navigate the space of possible actions, ultimately impacting both solution quality and computational efficiency.

Detailed energy consumption analysis across automated planning frameworks was enabled through the utilization of pyRAPL, revealing key insights into their efficiency. The study demonstrated a remarkably stable energy profile for planners based on the Fast Downward (FD) architecture, exhibiting a near-perfect correlation in energy use even with syntactic variations in problem descriptions. This suggests that alterations to the problem’s form do not significantly impact the planner’s core energy demands. However, analysis also pinpointed specific problematic configurations – notably a particular dead-end state within the Blocks World domain – that triggered a substantial, 30-fold increase in energy consumption, highlighting the importance of identifying and mitigating such bottlenecks to optimize planning efficiency.

The study reveals a critical truth about AI systems: their efficiency isn’t solely about algorithmic elegance, but about the very structure in which they’re grown. A poorly configured domain model, riddled with redundancy, introduces a needless energy cost, much like an overgrown garden choking itself with excess growth. As Barbara Liskov observed, “It’s one of the most difficult things about computer science: it’s much easier to build something that works than to build something that is provably correct.” This pursuit of ‘working’ without considering inherent structural efficiency, particularly in the context of heuristic search, leads to systems that consume disproportionate resources, obscuring the underlying potential for sustainable AI.

The Long Calculation

The observation that domain model configuration influences energy consumption is less a discovery than a restatement of fundamental constraints. Planners, like all systems, do not operate in a vacuum of pure logic; they are embodied in physical reality, and thus subject to its laws. To speak of ‘green AI’ is to momentarily forget that computation is thermodynamics. The focus will inevitably shift from optimizing algorithms to understanding the subtle interplay between model expressiveness and energetic cost – a dance with diminishing returns.

Redundancy, identified as a key factor, is not a bug, but a feature of all complex systems. It is the price of robustness, of the ability to navigate unforeseen circumstances. The question is not how to eliminate it, but how to manage it – to build models that are resilient without being profligate. Attempts to define an ‘optimal’ model will prove Sisyphean; architecture isn’t structure – it’s a compromise frozen in time.

Future work will likely circle back to the very foundations of knowledge representation. The pursuit of more expressive languages will continue, but with a growing awareness that each added layer of abstraction carries an energetic burden. Technologies change, dependencies remain. The long calculation is not about finding the right answer, but about learning to live with the cost of asking the question.

Original article: https://arxiv.org/pdf/2601.21967.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/

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2026-02-01 18:56