Foundations of Artificial Intelligence: Transitioning from Symbolic Reasoning to Data-Driven Intelligence
الكلمات المفتاحية:
الذكاء الاصطناعي، الاستدلال الرمزي، التعلم الآلي، التعلم العميق، الذكاء الاصطناعي الهجين، الذكاء الاصطناعي القابل للتفسيرالملخص
لقد شهد الذكاء الاصطناعي (AI) تحولًا كبيرًا، حيث انتقل من أنظمة الاستدلال الرمزي إلى الذكاء المعتمد على البيانات. اعتمدت أنظمة الذكاء الاصطناعي المبكرة على تمثيلات معرفية صريحة قائمة على القواعد، وكانت هذه الأنظمة تتسم بالوضوح والمنطقية وقابلية التفسير. ومع ذلك، فقد افتقرت إلى القدرة على التكيف، وقابلية التوسع، وإمكانية التعلم. وقد أتاح ظهور تعلم الآلة لأنظمة الذكاء الاصطناعي إمكانية التعرف على الأنماط من البيانات وتحسين الأداء من خلال الخبرة، بدلًا من الاعتماد فقط على القواعد التي يضعها الخبراء. وتسارع هذا التحول بشكل أكبر مع ظهور الشبكات العصبية الاصطناعية والتعلم العميق، اللذين يعتمدان على البيانات واسعة النطاق والقدرة الحاسوبية لتعلم تمثيلات معقدة غالبًا ما تتفوق على السمات المصممة يدويًا.
تقدم هذه الورقة تحليلًا مقارنًا للنماذج الرئيسة في الذكاء الاصطناعي، وهي: الذكاء الاصطناعي الرمزي، وتعلم الآلة، والتعلم العميق، وذلك استنادًا إلى قابلية التفسير، وقابلية التوسع، والاعتماد على البيانات، والملاءمة للتطبيقات الواقعية. بالإضافة إلى ذلك، يتم اقتراح إطار مفاهيمي للذكاء الاصطناعي الهجين يهدف إلى دمج الاستدلال والتعلم ضمن بنية موحدة تحقق التوازن بين الأداء وقابلية التفسير. وتهدف الدراسة إلى دعم الباحثين والممارسين في اختيار النماذج المناسبة للذكاء الاصطناعي في الأنظمة الذكية الحديثة، كما تسلط الضوء على الأهمية المتزايدة للذكاء الاصطناعي القابل للتفسير، والموثوق، والمتمحور حول الإنسان.
التنزيلات
المراجع
Alrawayati, H., & Tökeşer, Ü. (2021). PARKINSON’S DISEASE DIAGNOSIS BASED ON THE CONVOLUTIONAL NEURAL NETWORK AND PARTICLE SWARM OPTIMIZATION ALGORITHM. Asian Journal of Mathematics and Computer Research, 28(1), 26-37.
Apaydın, M., Yumuş, M., Değirmenci, A., & Karal, Ö. (2022). Evaluation of air temperature with machine learning regression methods using Seoul City meteorological data. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 28(5), 737-747.
Ben Dalla, L., Karal, Ömer, EL-Sseid, M., & Alsharif, A. (2026). An IoT-Enabled, THD-Based Fault Detection and Predictive Maintenance Framework for Solar PV Systems in Harsh Climates: Integrating DFT and Machine Learning for Enhanced Performance and Resilience. Wadi Alshatti University Journal of Pure and Applied Sciences, 4(1), 41-55. https://doi.org/10.63318/waujpasv4i1_05
Ben Dalla, L., Medeni, T. M., Agila, A. A., & Medeni, İ. M. (2024). Architectural Synergy: Investigating the Role of Artificial Neural Networks in Enabling Deep Learning. The International Journal of Engineering & Information Technology (IJEIT), 12(1), 96-103.
Ben Dalla, L., Medeni, T. M., Zbeida, S. Z., & Medeni, İ. M. (2024). Unveiling the Evolutionary Journey based on Tracing the Historical Relationship between Artificial Neural Networks and Deep Learning. The International Journal of Engineering & Information Technology (IJEIT), 12(1), 104-110.
Bengio, Y., LeCun, Y., & Hinton, G. (2021). Deep learning for AI. Communications of the ACM, 64(7), 58–65.
Çakır, M., Degirmenci, A., & Karal, O. (2022). Exploring the behavioural factors of cervical cancer using ANOVA and machine learning techniques. In International Conference on Science, Engineering Management and Information Technology (pp. 249-260). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-40395-8_18
Dalla, L. O. B., Karal, Ö., & Degirmenciyi, A. (2025). Leveraging LSTM for Adaptive Intrusion Detection in IoT Networks: A Case Study on the RT-IoT2022 Dataset implemented On CPU Computer Device Machine. 5th International Conference on Engineering, Natural and Social Sciences, April 15-16, 2025: Konya, Turkey, 2025. Published by All Sciences Academy. https://www.icensos.com/
Dalla, L. O. F. B. (2020). IT security Cloud Computing. . In 2020 IT security Cloud Computing Applications Conference (ITSCC) (pp. 1-10). IEEE. https://doi.org/10.16377/ITSCC 50717.2020.9259880
Dalla, L. O. F. B., & Ahmad, T. M. A. (2023). Heart Disease Prediction Via Using Machine Learning Techniques with Distributed System and Weka Visualization. Journal of Southwest Jiaotong University, 58(4), 322-333.https://doi.org/10.35741/issn.0258-2724.58.4.26
DARPA. (2017). Explainable Artificial Intelligence (XAI). Defense Advanced Research Projects Agency.
Degirmenci, A., & Karal, O. (2018). Evaluation of kernel effects on svm classification in the success of wart treatment methods. Am. J. Eng. Res, 7, 238-244.
Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv:1702.08608.
Elghaffi, et al., (2026). Temporal Dynamics in Intraoperative Monitoring: A Novel LSTM-Based Framework for Multivariate Time Series Classification in Critical Care Events. Temporal Dynamics in Intraoperative Monitoring: A Novel LSTM-Based Framework for Multivariate Time Series Classification in Critical Care Events. https://cjos.histr.edu.ly/index.php/journal
Gergerli, B., Çelebi, F. V., Rahebi, J., & Şen, B. (2023). An Approach Using in Communication Network Apply in Healthcare System Based on the Deep Learning Autoencoder Classification Optimization Metaheuristic Method. Wireless Personal Communications, 1-24.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
Gunning, D., & Aha, D. (2019). DARPA’s explainable artificial intelligence (XAI) program. AI Magazine, 40(2), 44–58.
Hawa Ahmed Alrawayati, Ümit Tokeşer. (2025).Spectral Integral Variation of Graph Theory. Asian Journal of Mathematics and Computer Research.32, Issue, 2. Pages(151-160). https://www.elibrary.ru/item.asp?id=82163806
Holzinger, A. (2022). Interactive machine learning for explainable AI. Information Fusion, 77, 1–16.
Kale, G., Bostancı, G. E., & Celebi, F. V. (2024). Evolutionary feature selection for machine learning based malware classification. Engineering Science and Technology, an International Journal, 56, 101762.
Karal, Ö., & Dalla, L. O. F. B. (2025). Lung Nodule Characterization in CT Scans Using Hybrid 3D Attention U-Net Segmentation and Transfer Learning-Based Classification Approach. Comprehensive Journal of Science, Volume (10), Issue (37), (NOV. 2025) Special issue for the Third International Conference on Science and Technology, www.sicst.ly, SICST2025, ISSN: 3014-6266, Reply: 6266-3014
Karal, Ö., & Dalla, L. O. F. B. (NOV. 2025) .Lung Nodule Characterization in CT Scans Using Hybrid 3D Attention U-Net Segmentation and Transfer Learning-Based Classification Approach. Volume (10), Issue (37), ISSN-3014-6266, SICST2025, www.sicst.ly
Karim, A. M., Karal, Ö., & Çelebi, F. V. (2018). A new automatic epilepsy serious detection method by using deep learning based on discrete wavelet transform. In Proceedings of the 3rd International Conference on Engineering Technology and Applied Sciences (ICETAS) (Vol. 4, pp. 15-18).
Kautz, H., et al. (2021). Neuro-symbolic AI: The state of the art. AI Magazine, 42(1), 3–18.
Keles, A., Ozisik, P. A., Algin, O., Celebi, F. V., & Bendechache, M. (2024). Decoding pulsatile patterns of cerebrospinal fluid dynamics through enhancing interpretability in machine learning. Scientific Reports, 14(1), 17854.
LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
Marcus, G. (2018). Deep learning: A critical appraisal. arXiv:1801.00631.
OECD. (2020). OECD principles on artificial intelligence.
Pearl, J., & Mackenzie, D. (2018). The book of why: The new science of cause and effect. Basic Books.
Recht, B., et al. (2019). Do ImageNet classifiers generalize to ImageNet? ICML. IEEE. (2019). Ethically aligned design: A vision for prioritizing human well-being with autonomous and intelligent systems.
Rudin, C. (2019). Stop explaining black box machine learning models for high stakes decisions. Nature Machine Intelligence, 1(5), 206–215.
Sinecen, M., Cinar, M., Karal, O., Engin, M., Atesci, Y. Z., Makinaci, M., & Cakmak, B. (2009, May). Diagnosis of Prostat Cancer using Artificial Neural Networks. In 2009 14th National Biomedical Engineering Meeting (pp. 1-3). IEEE https://doi.org/10.1109/BIYOMUT.2009.5130296
Tokgöz, N., Değirmenci, A., & Karal, Ö. (2024). Machine Learning-Based Classification of Turkish Music for Mood-Driven Selection. Journal of Advanced Research in Natural and Applied Sciences, 10(2), 312-328.
Vaswani, A., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems.
Yumuş, M., Apaydın, M., Değirmenci, A., & Karal, Ö. (2020). Missing data imputation using machine learning based methods to improve HCC survival prediction. In 2020 28th Signal Processing and Communications Applications Conference (SIU) (pp. 1-4). IEEE.
Zhang, Q., & Zhu, S. (2018). Visual interpretability for deep learning. IEEE Signal Processing Magazine, 35(1), 27–39.









