• Aditya Mehra

In this research, the author examines the implementation of integrating symbolic and deep learning methods to develop mixed AI systems for improved complex decision-making. Conventional AI methods distinguish between first-order logic-based symbolic reasoning, a symbolic logic-based system, and neural networks, data-based systems. Each has its strengths and limitations. It is also notable that symbolic AI is easy to explain and is effective in dealing with structured knowledge. At the same time, deep learning is good at dealing with large volumes of unstructured data and recognizing patterns. Thus, the study focuses on developing a combined model of the two approaches, the merger of which will provide a more significant number of advantages and allow for even better and more effective solutions in tasks related to decision-making. The contribution of the research to AI is apparent in the following ways. For example, first, it seeks to connect symbolic reasoning with deep learning with the strengths of one compensating for the weaknesses of the other, including the lack of interpretability in deep learning and extreme formalism in the symbolic systems. The proposed approach involves creating and applying a dual AI architecture of symbolic/semantic and deep learning with a new architectural approach. The symbolic reasoning component is realized as a rule-based system. We implemented the symbolic reasoning component as a rule-based system. We created the deep learning component as a neural network. These components can then clearly interact with each other, more so within a single overall system. Several significant findings suggest that the hybrid AI Model for decision-making provides better decision-making precision when compared with decision-making models based on symbolic thinking or deep learning only. The integration helps to improve the processing of structured and unstructured data, improving the reliability of the system's results. Also, there is better interpretability; the symbolic reasoning part can explain why such a decision was made, and there is enhanced scalability to new and complicated problems. The consequences of this research highlight critical areas for development in both AI and specific fields where it is applied, such as healthcare and finance, where making correct and easily explainable decisions is essential. The main problem in AI is the consideration of interpretation about accuracy; the hybrid model suggests a possible direction for the subsequent development of AI systems. This study thus provides directions for further studies on other hybrid structures, enhancing integration approaches and expanding the use of the proposed model to other decision-making problems.

Hybrid AI, symbolic reasoning, deep learning, decision-making.

"HYBRID AI MODELS: INTEGRATING SYMBOLIC REASONING WITH DEEP LEARNING FOR COMPLEX DECISION-MAKING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 8, page no.f693-f705, August-2024, Available :http://www.jetir.org/papers/JETIR2408685.pdf

"HYBRID AI MODELS: INTEGRATING SYMBOLIC REASONING WITH DEEP LEARNING FOR COMPLEX DECISION-MAKING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 8, page no. ppf693-f705, August-2024, Available at : http://www.jetir.org/papers/JETIR2408685.pdf