Princess Nora University Artificial Intelligence

1. Princess Nora UniversityArtificial Intelligence Chapter (4) Informed search algorithms

2. Outline • Best-first search • Greedy best-first search • A* search • Heuristics • Local search algorithms • Hill-climbing search • Simulated annealing search • Local beam search • Genetic algorithms

3. Informed Search Idea: use problem specific knowledge to pick which node to expand • Typically involves a heuristic function h(n) estimating the cheapest path from n.STATE to a goal state A search strategy is defined by picking the order of node expansion

4. Best-first search • We can use it to direct our search towards the goal. • Best first search simply chooses the unvisited node with the best heuristic value to visit next. • It can be implemented in the same algorithm as lowest-cost Breadth First Search. • Idea: use an evaluation functionf(n) for each node • estimate of "desirability" • Expand most desirable unexpanded node • Implementation: Order the nodes in fringe in decreasing order of desirability Special cases: • greedy best-first search • A* search

5. Romania with step costs in km

6. Greedy best-first search • Evaluation function f(n) = h(n) (heuristic) = estimate of cost from n to goal • e.g., hSLD(n) = straight-line distance from n to Bucharest • Greedy best-first search expands the node that appears to be closest to goal

7. Greedy best-first search example

8. Greedy best-first search example

9. Greedy best-first search example

10. Greedy best-first search example

11. Properties of greedy best-first search • Complete? No – can get stuck in loops, e.g., Iasi Neamt Iasi Neamt • Time?O(bm), but a good heuristic can give dramatic improvement • Space?O(bm) -- keeps all nodes in memory Optimal? No

12. A* search • Idea: avoid expanding paths that are already expensive • Evaluation function f(n) = g(n) + h(n) • g(n) = cost so far to reach n • h(n) = estimated cost from n to goal • f(n) = estimated total cost of path through n to goal

13. A* search example

14. A* search example

15. A* search example

16. A* search example

17. A* search example

18. A* search example

19. Admissible heuristics • A heuristic h(n) is admissible if for every node n, h(n) ≤ h*(n), where h*(n) is the true cost to reach the goal state from n. • An admissible heuristic never overestimates the cost to reach the goal, i.e., it is optimistic • Example: hSLD(n) (never overestimates the actual road distance) • Theorem: If h(n) is admissible, A* using TREE-SEARCH is optimal

20. Properties of A* • Complete? Yes (unless there are infinitely many nodes with f ≤ f(G) ) • Time? Exponential • Space? Keeps all nodes in memory • Optimal? Yes

21. Local search algorithms • In many optimization problems, the path to the goal is irrelevant; the goal state itself is the solution • State space = set of "complete" configurations • Find configuration satisfying constraints, e.g., n-queens • In such cases, we can use local search algorithms • keep a single "current" state, try to improve it. • Very memory efficient (only remember current state)

22. Hill-climbing search • “ a loop that continuously moves in the direction of increasing value” • terminates when a peak is reached • Value can be either • Objective function value • Heuristic function value (minimized) • Hill climbing does not look ahead of the immediate neighbors of the current state. • Can randomly choose among the set of best successors, if multiple have the best value. • Characterized as “trying to find the top of Mount Everest while in a thick fog”

23. Example: n-queens • Put n queens on an n × n board with no two queens on the same row, column, or diagonal

24. Hill-climbing search

25. Hill climbing and local maxima • When local maxima exist, hill climbing is suboptimal • Local maxima: a local maximum, as opposed to a global maximum, is a peak that is lower than the highest peak in the state space. • Once on a local maximum, the algorithm will halt • even though the solution may be far from satisfactory. • Simple (often effective) solution • Multiple random restarts

26. Hill-climbing search: 8-queens problem • A local minimum with h = 1

27. Simulated annealing search • Idea: escape local maxima by allowing some "bad" moves but gradually decrease their frequency

28. Properties of simulated annealing search • One can prove: If T decreases slowly enough, then simulated annealing search will find a global optimum with probability approaching 1 • Widely used in VLSI layout, airline scheduling, etc

29. Local beam search • Keep track of k states rather than just one • Start with k randomly generated states • At each iteration, all the successors of all k states are generated • If any one is a goal state, stop; else select the k best successors from the complete list and repeat.

30. Genetic algorithms • A successor state is generated by combining two parent states • Start with k randomly generated states (population) • A state is represented as a string over a finite alphabet (often a string of 0s and 1s) • Evaluation function (fitness function). Higher values for better states. • Produce the next generation of states by selection, crossover, and mutation

31. Genetic algorithms • Fitness function: number of non-attacking pairs of queens (min = 0, max = 8 × 7/2 = 28) • 24/(24+23+20+11) = 31% • 23/(24+23+20+11) = 29% etc

32. Genetic algorithms