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Search_Algorithms_Presentation introduction.pptx
- 1. SEARCH ALGORITHMS IN ARTIFICIALINTELLIGENCE BEST FIRST SEARCH, A*, IDA*, LOCAL & ADVERSARIAL SEARCH TECHNIQUES
- 2. BEST FIRST SEARCH •Uses heuristic to estimate cost from current node to goal • Selects node with lowest heuristic value (h(n)) • Not guaranteed to find optimal path
- 3. A* ALGORITHM • Combinespath cost and heuristic: f(n) = g(n) + h(n) • Optimal and complete if h(n) is admissible • Widely used in pathfinding (e.g., GPS navigation)
- 4. ITERATIVE DEEPENING A*(IDA*) • Combines depth-first search with A*’s cost function • Uses iterative cost threshold to control depth • Memory-efficient and optimal
- 5. DEPTH-FIRST BRANCH ANDBOUND (DFBNB) • Performs DFS and prunes paths exceeding current best cost • Reduces memory usage compared to A* • May be slower due to backtracking
- 6. ADMISSIBLE HEURISTICS &DOMAIN RELAXATION • Admissible heuristic: never overestimates cost to goal • Domain relaxation: simplifies constraints to create heuristic • Example: Manhattan distance for 8-puzzle
- 7. LOCAL SEARCH: SATISFACTIONVS OPTIMIZATION • Satisfaction: find any solution meeting constraints • Optimization: find best solution (minimum cost, max value) • Used in CSPs and combinatorial optimization
- 8. N-QUEENS PROBLEM EXAMPLE •Place N queens on NxN board without attacks • Constraints: no two queens share row, column, or diagonal • Can be solved using local search techniques
- 9. HILL CLIMBING • Startsfrom random state, iteratively improves solution • Only moves to better neighboring states • May get stuck in local maxima or plateaus
- 10. SIMULATED ANNEALING • Variationof hill climbing with probabilistic moves • Accepts worse states to escape local optima • Cooling schedule controls acceptance probability
- 11. GENETIC ALGORITHMS • Inspiredby natural evolution • Uses population, crossover, mutation for exploration • Good for large, complex search spaces
- 12. ADVERSARIAL SEARCH: MINIMAX •Used in two-player games like chess, tic-tac-toe • Assumes opponent plays optimally • Builds game tree to choose best move
- 13. ALPHA-BETA PRUNING • Optimizesminimax by pruning irrelevant branches • Maintains same result as minimax but faster • Reduces number of nodes evaluated