Author(s): Lukas Schneider, Elena Rossi, Anders Holmgren and María Fernández López

Abstract: Rule-based artificial intelligence (AI) models represent one of the earliest yet most resilient paradigms of intelligent system design, particularly suited for environments with limited computational, financial, and data resources. While contemporary AI research is dominated by data-intensive machine learning and deep learning approaches, these methods often remain impractical in low-resource settings due to their dependency on large datasets, high processing power, and continuous model retraining. In contrast, rule-based AI systems rely on explicit logical rules, expert knowledge, and deterministic inference mechanisms, enabling transparent, efficient, and cost-effective decision-making. This research examines the relevance, structure, and performance of rule-based AI models when deployed in low-resource decision-making systems such as embedded devices, rural healthcare tools, agricultural advisory platforms, and small-scale industrial automation. The abstract emphasizes how rule-based systems achieve reliable outcomes through symbolic reasoning, knowledge representation, and inference engines without the need for extensive training data. It further highlights the advantages of interpretability, predictability, and low energy consumption, which are critical factors in constrained environments. The research also discusses common limitations of rule-based approaches, including scalability challenges, knowledge acquisition bottlenecks, and rule maintenance complexity, while identifying strategies to mitigate these issues through modular rule design and hybrid architectures. By synthesizing foundational AI principles with contemporary low-resource application needs, this work positions rule-based AI as a viable and often preferable alternative to data-driven models in constrained contexts. The findings reinforce that, despite rapid advances in learning-based AI, rule-based systems continue to offer practical, robust, and ethically transparent solutions for decision-making where resources, data availability, and explainability requirements impose strict constraints. This analysis contributes to renewed interest in symbolic AI as a strategic component of sustainable and accessible artificial intelligence deployment.

DOI: 10.33545/27076636.2026.v7.i1a.153

Pages: 41-45 | Views: 134 | Downloads: 77

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