Self-Adaptive Artificial Intelligence System for Context-Aware Problem Solving and Data-Driven Decision Optimization

The present need for real-time optimization and choices makes standard AI systems hard to adjust when facing ambiguous situations. The path optimization and problemsolving software tool ACO shows widespread use however it encounters premature convergence issues as well as flexibility restrictions. Fuzzy logic handles uncertainties effectively yet it does not have strong learning abilities or optimization methods. The presented research creates a new self-adaptive AI system by uniting ACO with fuzzy logic for enhancing decision-making abilities which adapt to changing environmental conditions. Through the proposed hybrid model the pheromone updating bounds operate using fuzzy-controlled mechanisms alongside probabilistic selection methods that automatically modify evaporation parameters and selection weights through the detection of live environmental conditions. The approach improves both exploration-exploitation equilibrium and problem-solving speed together with solution efficiency. The system undergoes evaluation through benchmarking that matches traditional ACO and alternative optimization algorithms while showing maximal accuracy levels and excellent adaptability features and computational efficiency outcomes. Real-life applications of the fuzzy logic-based ACO system are demonstrated in logistics as well as healthcare and IoT-based decision systems and these applications show its feasibility at different scales. Experimental tests validate that fuzzy logicbased advancements in ACO promote outstanding results in highly complex situations combined with dynamic problem spaces. The research demonstrates how combinatorial AI methods can autonomously learn while optimizing real-time operations which creates the foundation for future AI-driven choice systems across multiple industrial sectors.