Introduction to Intelligent Agents and AI Search Algorithms

How do autonomous systems make decisions, navigate maps, and solve complex puzzles? At the heart of modern artificial intelligence lies the concept of intelligent agents and the search algorithms they use to find optimal paths to their goals. This text-based course guides you through the foundational principles of agent-based design and the critical algorithms that power decision-making. You will transition from understanding basic environmental interactions to implementing classic and modern search strategies. What you'll learn: - Understand the core architecture of intelligent agents and how they perceive and act within an environment. - Formulate real-world problems as state-space search problems with defined initial states, transitions, and goals. - Apply uninformed search strategies including breadth-first search and depth-first search to find solutions. - Master informed search techniques such as A* search and greedy best-first search using heuristics. - Analyze the performance of different search algorithms in terms of completeness, time complexity, and space complexity. - Explore modern agentic workflows and how search-based planning integrates with contemporary AI systems. You will start by mastering key terminology, basic agent types, and foundational definitions of environment classes. From there, you will progress to reading through clear, step-by-step breakdowns of search algorithms, complete with structured pseudo-code and conceptual walkthroughs that demonstrate how these concepts apply to modern software engineering. This course is designed for aspiring AI developers, computer science students, and curious software engineers who want to build a strong theoretical and practical foundation in AI search techniques. No prior background in artificial intelligence is required. Start reading today to master the core algorithms that drive intelligent decision-making.