HILL CLIMBING IN AI

Hill Climbing in AI

Hill Climbing in AI

Blog Article

Hill Climbing is a heuristic search algorithm used in Artificial Intelligence to find optimal solutions. It starts with an arbitrary solution and iteratively moves towards a better neighboring solution. The process continues until no improvements can be made. Unlike other search methods, it doesn’t backtrack, making it fast but prone to local maxima. Variants like stochastic and simulated annealing help overcome this. Hill Climbing is commonly used in robotics, planning, and game AI. Its simplicity makes it ideal for understanding optimization in AI.

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