Search Algorithms in AI systematically explore solution spaces to find optimal or satisfactory solutions to problems. Classical algorithms include uninformed search (breadth-first, depth-first, uniform-cost) and informed search (A*, greedy best-first, beam search). Advanced techniques incorporate heuristics, pruning, bidirectional search, and iterative deepening. Modern AI integrates learning-based search (Monte Carlo Tree Search with neural networks, learned heuristics) and continuous optimisation methods.
Semantic Classification
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Key Characteristics
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Explores state spaces systematically or heuristically
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Guarantees optimality under specific conditions
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Employs admissible heuristics for informed search
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Scales to large search spaces through pruning and approximation
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Integrates learning for adaptive search strategies
Overview
Search Algorithms in AI systematically explore solution spaces to find optimal or satisfactory solutions to problems. Classical algorithms include uninformed search (breadth-first, depth-first, uniform-cost) and informed search (A*, greedy best-first, beam search). Advanced techniques incorporate heuristics, pruning, bidirectional search, and iterative deepening. Modern AI integrates learning-based search (Monte Carlo Tree Search with neural networks, learned heuristics) and continuous optimization methods. Applications span planning, constraint satisfaction, game playing, and combinatorial optimization.
Related Concepts
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References
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Hart, P. et al. (1968). A Formal Basis for the Heuristic Determination of Minimum Cost Paths. IEEE Transactions on Systems Science and Cybernetics, 4(2), 100-107.
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Korf, R. (1985). Depth-First Iterative-Deepening: An Optimal Admissible Tree Search. Artificial Intelligence, 27(1), 97-109.
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Silver, D. et al. (2017). Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm. arXiv:1712.01815.