Artificial Intelligence (AI) has revolutionized numerous domains, from finance to healthcare, by enabling machines to simulate intelligent behavior. One of the fundamental pillars of AI is search algorithms, which play a crucial role in problem-solving and decision-making processes. In this short course, we delve into various AI concepts, with a particular focus on graph search algorithms and their applications.
The study of search algorithms illuminates the intricate dynamics between problem exploration and exploitation, offering insights into how agents navigate complex solution spaces. From the foundational concepts of states, actions, and transitional models to the diverse strategies of uninformed and informed search algorithms, each facet contributes to our understanding of efficient problem-solving. Techniques such as Alpha-Beta Pruning and Depth Limited Minimax further refine our approaches, emphasizing the ongoing pursuit of optimization and scalability in computational problem-solving. As we continue to delve deeper into search algorithms, we not only refine our ability to solve diverse challenges but also advance the broader landscape of artificial intelligence and intelligent systems.