This chapter explores the initial innovations that shaped early artificial intelligence (AI), focusing on key problem-solving strategies such as blind, heuristic, local, and adversary search methods. It examines how knowledge is represented and reasoned with through logic, semantic networks, ontologies, and non-monotonic reasoning. The discussion extends to planning systems, including the STRIPS formalism and constraint satisfaction problems, which enabled machines to make decisions and solve complex tasks. Core applications like computer vision, natural language processing, and expert systems are highlighted to illustrate the practical impact of these early developments. Together, these elements laid the groundwork for modern AI systems by combining structured reasoning with adaptive problem-solving.