Search-Based Problem Solving in AI: State Space, Search Trees, Heuristics, A*, Local Search, and Game Search

Cross-posted from Zeromath. Original article: https://zeromathai.com/en/ai-search-based-problem-solving-en/ A lot of AI systems do not “know” one fixed answer in advance. They solve problems by searching through possibilities. That idea shows up in route planning, puzzle solving, robotics, optimization, and game-playing agents. The surface details change, but the pattern is often the same: represent the problem as a set of states, define how you can move between them, and then search for a good path or decision. This is one of the most useful foundations in AI because it connects topics that are often taught separately: classical search heuristic search pathfinding optimization game-playing AI planning reinforcement learning Once you see search as the common pattern, a lot of AI starts to feel less like a bag of unrelated algorithms and more like one connected design space. Why search matters Beginners often meet AI through deep learning, LLMs, or generative models. But long before tho