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Search Methods for Problem Solving: Blind, Heuristic, and Competitive Search
Search Methods for Problem Solving: Blind, Heuristic, and Competitive Search
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In this course, students will grasp the fundamentals of problem-solving in Artificial Intelligence, learning to define initial state, goal, possible actions, and associated costs. Blind searches such as BFS and DFS are introduced, ideal for small spaces, along with their limitations when facing complex problems like Chess or the 8-Queens problem.
The module also explores heuristic searches, which use problem-specific knowledge to guide the exploration. Practical applications are discussed, such as GPS navigation, robot route optimization, and route planning in public health.
Finally, students will study competitive searches and metaheuristics, including Genetic Algorithms, Simulated Annealing, Tabu Search, and GRASP. These techniques handle very large problems or multiple objectives and are essential for fleet routing, industrial logistics, and complex optimization. The module emphasizes that no method is universally superior and points to future trends, such as automatic heuristics via Machine Learning and hybrid systems for Big Data and advanced robotics.
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