SEARCH STRATEGIES IN ARTIFICIAL INTELLIGENCE

Search strategies are at the heart of artificial intelligence (AI), serving as the navigational compass that guides AI systems through vast data spaces to discover solutions and make decisions. This article explores the diverse landscape of search strategies in AI, ranging from fundamental algorithms to advanced techniques powered by machine learning. We delve into the core search algorithms, including informed search and stochastic search methods, shedding light on their strengths and applications. Real-world applications, from recommendation systems to autonomous vehicles, demonstrate the practical significance of search strategies in AI. Despite their critical role, we acknowledge the challenges and limitations, paving the way for discussions on future trends and innovations, such as quantum search and the fusion of deep learning with search algorithms. This abstract offers a glimpse into the dynamic and ever-evolving field of search strategies in AI, underscoring their pivotal role in shaping the future of technology and intelligent decision-making.