2022· International Journal of Artificial Intelligence & Digital Transformation· Vol 5, pp. 01-13· 0 citations
TL;DR
This paper analyzes hybrid AI systems that combine symbolic approaches (rule-based reasoning, interpretability) with sub-symbolic methods (machine learning, neural networks) to improve flexibility and robustness and suggests future directions, including explainable AI and scalable distributed architectures.
Abstract
Hybrid AI architectures are emerging as a powerful solution for complex decision support systems (DSS) that must handle uncertainty, heterogeneous data, and large-scale integration. Traditional AI methods are limited in addressing real-world multidimensional challenges. This paper analyzes hybrid AI systems that combine symbolic approaches (rule-based reasoning, interpretability) with sub-symbolic methods (machine learning, neural networks) to improve flexibility and robustness. A modular framework is proposed, consisting of data preprocessing, knowledge representation, inference, and learning components, enabling both offline training and real-time decision-making. The study highlights key challenges such as scalability, knowledge integration, and computational efficiency. Experimental results demonstrate that hybrid models outperform standalone AI techniques in accuracy, precision, recall, and efficiency, especially in dynamic and uncertain environments. The paper concludes by suggesting future directions, including explainable AI and scalable distributed architectures.
This study proposes a Multimodal AI Framework for Decision Intelligence Systems that integrates diverse data sources to enhance prediction accuracy, contextual understanding, and operational efficiency and demonstrates that multimodal AI significantly outperforms traditional unimodal systems.
Seshagiri N· International Journal of Art...· 0 citations
The proposed framework includes four stages: knowledge acquisition, data preprocessing, hybrid model integration, and predictive decision support, which improves prediction accuracy, reliability, transparency, and decision-making of next-generation intelligent systems.
Karen Lewis, Steven Young· International Journal of App...· 0 citations
By foregrounding decision intelligence in complex systems, Enactive AI expands the frontier of AI from model capability to system-aware action, opening new possibilities for scalable, governable, and socially valuable AI deployment.
Zuo-Jun Max Shen, Yuan Qu, Pu-Jun Zhang et al.· 0 citations
The increasing complexity of organizational, environmental, and operational decision environments requires artificial intelligence systems that can integrate heterogeneous information, recognize previously unseen situations, and support decisions under uncertainty. Conventional AI pipelines frequently depend on predefined classes, static datasets, and narrowly specified prediction objectives, limiting their ability to adapt when decision contexts evolve. This paper proposes a conceptual Semantic AI Architecture for Sustainable and Intelligent Decision-Making that combines semantic representation, open-world learning, incremental learning, attention-based representation, self-supervised feature extraction, and information-theoretic reasoning. The architecture is developed through a structured synthesis of the supplied literature and is positioned as a research-oriented framework rather than an empirical benchmark. The analysis demonstrates that sustainability-oriented decision intelligence requires more than predictive accuracy: it requires contextual interpretation, novelty awareness, continuous learning, and mechanisms for integrating multimodal evidence. Open-world recognition provides a foundation for identifying unknown situations, while incremental learning enables adaptation without complete retraining. Transformer-based representations and self-supervised learning can improve the semantic quality of continuously acquired data, while information theory provides a theoretical basis for evaluating information relevance and uncertainty. The proposed architecture therefore connects semantic interpretation with adaptive AI infrastructure and sustainable decision processes. The study identifies architectural advantages, operational trade-offs, and limitations, particularly concerning semantic consistency, computational cost, uncertainty management, and evaluation of genuinely unknown decision states.
Rahul Verma, Neha Kapoor· American Journal Of Applied...· 0 citations
Large language models and foundation models are increasingly embedded in reasoning systems that plan, invoke tools, use memory, gather evidence, and iteratively refine their outputs. The second KDD Day on AI Reasoning brings together researchers and practitioners from academia and industry to examine how these systems can be made more capable, reliable, interpretable, and efficient. The program spans scientific discovery, human-centered interaction, software engineering, time-series analysis, deep research, computer use, and inference infrastructure. Across these domains, the day highlights shared challenges: grounding decisions in evidence, designing effective feedback and verification mechanisms, evaluating open-ended behavior, managing test-time computation, and preserving meaningful human control. Through keynote and invited presentations, the event provides a forum for connecting advances in models, agents, data, systems, and applications, and for identifying research directions toward trustworthy next-generation reasoning systems.
Jun Huan, James Caverlee, Lei Li et al.· Proceedings of the 32nd ACM...· 0 citations
A new framework for Neuro-Symbolic Machine Learning (NS-ML) to enhance the adaptive decision intelligence of distributed smart systems is introduced. As smart environments become more complex, the transparency and reasoning of traditional black-box deep learning models in uncertain circumstances are unsatisfactory. Researchers address this deficit with a hybrid approach that combines neural networks (NNs), which excel at capturing perceptual patterns, and symbolic logic engines (SLEs), which excel at making decisions structured and explainable. The model uses both connectionist learning and formal knowledge representation to achieve high adaptability in a dynamic environment. The experimental study uses a well-defined set of 364 real-time sensor telemetry and operational event logs collected from an experimental smart grid infrastructure testbed. Researchers used Python to implement the framework and PyTorch for the neural parts, while the CLINGO engine handled the symbolic reasoning. The results show that our hybrid method is much more accurate in decision-making and logical consistency than using a neural network alone, especially when operational conditions vary. The framework offers a solid base for the development of self-optimising distributed systems, which must not only be capable of high-speed processing but also give assurance of decision-making logic, and is therefore suitable for modern industrial automation and smart city buildings.
Rajesh Mannam· FMDB Transactions on Sustain...· 0 citations
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