Informed search algorithms

1. Informed search algorithms Chapter 4 Slides derived in part from www.cs.berkeley.edu/~russell/slides/chapter04a.pdf, converted to powerpoint by Min-Yen Kan, National University of Singapore, and from www.cs.umbc.edu/671/fall03/slides/c5-6_inf_search.ppt, Marie DesJardins, University of Maryland Baltimore County.

2. Review: Tree search • A search strategy is defined by picking the order of node expansion

3. Heuristic Search • Basic tree search is generic; the choice of node to expand is dependent on the shape of the tree and the strategy chosen for node expansion. • Often we have some domain knowledge that can help make a better decision. • For the Romania problem, for instance, eyeballing it results in looking at certain cities first because they "look closer" to where we are going. • If that domain knowledge can be captured in a heuristic, search performance can be improved by using that heuristic. • This gives us an informed search strategy.

4. So What's A Heuristic? Webster's Revised Unabridged Dictionary (1913) (web1913) Heuristic \Heu*ris"tic\, a. [Gr. ? to discover.] Serving to discover or find out. The Free On-line Dictionary of Computing (15Feb98) heuristic 1. <programming> A rule of thumb, simplification or educated guess that reduces or limits the search for solutions in domains that are difficult and poorly understood. Unlike algorithms, heuristics do not guarantee feasible solutions and are often used with no theoretical guarantee. 2. <algorithm> approximation algorithm. From WordNet (r) 1.6 heuristic adj 1: (computer science) relating to or using a heuristic rule 2: of or relating to a general formulation that serves to guide investigation [ant: algorithmic] n : a commonsense rule (or set of rules) intended to increase the probability of solving some problem [syn: heuristic rule, heuristic program]

5. Heuristics • All domain knowledge used in the search is encoded in the heuristic function h. • Examples: • Missionaries and Cannibals: Number of people on starting river bank • 8-puzzle: Number of tiles out of place • 8-puzzle: Sum of distances from goal • for each time • In general: • h(n) >= 0 for all nodes n • h(n) = 0 implies that n is a goal node • h(n) = infinity implies that n is a deadend from which a goal cannot be reached

6. Best-first search • Order nodes on the nodes list by increasing value of an evaluation function, f(n), that incorporates domain-specific information in some way. • This is a generic way of referring to the class of informed methods.

7. Best-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

8. Romania with step costs in km

9. 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

10. Greedy best-first search example

11. Greedy best-first search example

12. Greedy best-first search example

13. Greedy best-first search example

14. 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 • Remember: Time and space complexity are measured in terms of • b: maximum branching factor of the search tree • d: depth of the least-cost solution • m: maximum depth of the state space (may be infinite)

15. 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

16. A* search example

17. A* search example

18. A* search example

19. A* search example

20. A* search example

21. A* search example

22. 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)

23. Admissible heuristics E.g., for the 8-puzzle: • h1(n) = number of misplaced tiles • h2(n) = total Manhattan distance (i.e., no. of squares from desired location of each tile) • h1(S) = ? • h2(S) = ?

24. Admissible heuristics E.g., for the 8-puzzle: • h1(n) = number of misplaced tiles • h2(n) = total Manhattan distance (i.e., no. of squares from desired location of each tile) • h1(S) = ? 8 • h2(S) = ? 3+1+2+2+2+3+3+2 = 18

25. Properties of A* • If h(n) is admissible • Complete? Yes (unless there are infinitely many nodes with f <≤ f(G) ) • Time? Exponential in [relative error in h * length of solution] • Space? Keeps all nodes in memory • Optimal? Yes; cannot expand f i+1 until f i is finished.

26. Some observations on A* • Perfect heuristic: If h(n) = h*(n) for all n, then only the nodes on the optimal solution path will be expanded. So, no extra work will be performed. • Null heuristic: If h(n) = 0 for all n, then this is an admissible heuristic and A* acts like Uniform-Cost Search. • Better heuristic: If h1(n) < h2(n) <= h*(n) for all non-goal nodes, then h2 is a better heuristic than h1 • If A1* uses h1, and A2* uses h2, then every node expanded by A2* is also expanded by A1*. • In other words, A1 expands at least as many nodes as A2*. • We say that A2* is better informed than A1*, or A2* dominates A1* • The closer h is to h*, the fewer extra nodes that will be expanded

27. What’s a good heuristic? • If h1(n) < h2(n) <= h*(n) for all n, then both are admissible and h2 is better than (dominates) h1. • Relaxing the problem: remove constraints to create a (much) easier problem; use the solution cost for this problem as the heuristic function • Combining heuristics: take the max of several admissible heuristics: still have an admissible heuristic, and it’s better! • Identify good features, then use a learning algorithm to find a heuristic function: may lose admissibility

28. Relaxed problems • A problem with fewer restrictions on the actions is called a relaxed problem • The cost of an optimal solution to a relaxed problem is an admissible heuristic for the original problem • If the rules of the 8-puzzle are relaxed so that a tile can move anywhere, then h1(n) gives the shortest solution • If the rules are relaxed so that a tile can move to any adjacent square, then h2(n) gives the shortest solution

29. Some Examples of Heuristics? • 8-puzzle? • Mapquest driving directions? • Minesweeper? • Crossword puzzle? • Making a medical diagnosis? • ??

30. Iterative improvement search • Another approach to search involves starting with an initial guess at a solution and gradually improving it until a solution is reached. • Some examples: • Hill Climbing • Simulated Annealing • Constraint satisfaction

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

32. Hill-climbing search • If there exists a successor s for the current state n such that • h(s) < h(n) • h(s) <= h(t) for all the successors t of n, • then move from n to s. Otherwise, halt at n. • Looks one step ahead to determine if any successor is better than the current state; if there is, move to the best successor. • Similar to Greedy search in that it uses h, but does not allow backtracking or jumping to an alternative path since it doesn’t “remember” where it has been. • Not complete since the search will terminate at "local minima," "plateaus," and "ridges."

33. Hill-climbing search • "Like climbing Everest in thick fog with amnesia"

34. 8 8 3 1 4 7 6 5 3 1 8 4 7 6 5 1 3 8 4 7 6 5 8 2 2 6 2 1 4 7 3 5 7 1 3 5 4 7 8 6 6 8 3 1 4 5 7 4 6 1 3 5 2 2 2 2 Hill climbing example start h = 0 goal h = -4 -2 -5 -5 h = -3 h = -1 -3 -4 h = -2 h = -3 -4 f(n) = -(number of tiles out of place)

35. Drawbacks of hill climbing • Problems: • Local Maxima: peaks that aren’t the highest point in the space • Plateaus: the space has a broad flat region that gives the search algorithm no direction (random walk) • Ridges: flat like a plateau, but with dropoffs to the sides; steps to the North, East, South and West may go down, but a step to the NW may go up. • Remedy: • Random restart. • Some problem spaces are great for hill climbing and others are terrible.

36. Hill-climbing search • Problem: depending on initial state, can get stuck in local maxima

37. 6 2 7 4 8 6 3 1 2 5 7 4 8 6 3 1 8 6 1 2 5 7 8 3 3 1 2 5 7 4 4 5 3 1 2 5 7 4 8 6 Example of a local maximum -4 start goal -4 0 -3 -4

38. Simulated annealing • Simulated annealing (SA) exploits an analogy between the way in which a metal cools and freezes into a minimum-energy crystalline structure (the annealing process) and the search for a minimum [or maximum] in a more general system. • SA can avoid becoming trapped at local minima. • SA uses a random search that accepts changes that increase objective function f, as well as some that decrease it. • SA uses a control parameter T, which by analogy with the original application is known as the system “temperature.” • T starts out high and gradually decreases toward 0.

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

40. 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

41. 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

42. 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

43. Genetic algorithms

44. Summary: Informed search • Best-first search is general search where the minimum-cost nodes (according to some measure) are expanded first. • Greedy search uses minimal estimated cost h(n) to the goal state as measure. This reduces the search time, but the algorithm is neither complete nor optimal. • A* search combines uniform-cost search and greedy search: f(n) = g(n) + h(n). A* handles state repetitions and h(n) never overestimates. • A* is complete, optimal and optimally efficient, but its space complexity is still bad. • The time complexity depends on the quality of the heuristic function. • Hill-climbing algorithms keep only a single state in memory, but can get stuck on local optima. • Simulated annealing escapes local optima, and is complete and optimal given a “long enough” cooling schedule.