Adaptive Hybrid Algorithms for Real-Time Decision-Making in Autonomous Systems

Recent breakthroughs in computational intelligence have enabled remarkable advances in decision-making systems operating within dynamic, complex environments. The work presented in this paper looks into the incorporation of three major techniques: Reinforcement Learning, Deep Neural Networks, and Fuzzy Logic in developing hybrid models in order to be able to tackle some major challenges of adaptability, handling uncertainty, and high-dimensionality data processing. These hybrid frameworks have applications in domains such as autonomous vehicle navigation, health care, robotics, and supply chain optimization, where classic methods do not work. Based on the adaptability given by RL, on the predictive power of DNNs, and on the interpretability provided by Fuzzy Logic, the proposed models demonstrate scalability and robustness under dynamic settings. It points to the existing challenges of computational complexity, real-time applicability, and cross-domain generalizability, and ascertains a unified hybrid framework in order to bridge these gaps. Experimental results also demonstrate improved accuracy with reduced response time for such models, proving their potential in advancing intelligent autonomous systems that could deal with ever-changing environments.